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Multivariate optimization of capillary electrophoresis methods: A critical review
Accepted Manuscript Title: Multivariate optimization of capillary electrophoresis methods: a critical review Author: Serena Orlandini Roberto Gotti Sandra Furlanetto PII: DOI: Reference:
S0731-7085(13)00160-X http://dx.doi.org/doi:10.1016/j.jpba.2013.04.014 PBA 9036
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received date: Accepted date:
10-4-2013 12-4-2013
Please cite this article as: S. Orlandini, R. Gotti, S. Furlanetto, Multivariate optimization of capillary electrophoresis methods: a critical review, Journal of Pharmaceutical and Biomedical Analysis (2013), http://dx.doi.org/10.1016/j.jpba.2013.04.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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*Graphical Abstract
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*Highlights (for review)
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HIGHLIGHTS
Recent applications of multivariate optimization of CE methods are reviewed.
The characteristics of the applied chemometric strategies are summarized in tables.
A critical discussion on multivariate optimization of CE methods is presented.
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Multivariate optimization of capillary electrophoresis methods:
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a critical review
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Serena Orlandinia, Roberto Gottib, Sandra Furlanettoa,*
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Department of Chemistry “U. Schiff”, University of Florence, Via U. Schiff 6, 50019 Sesto Fiorentino,
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Florence, Italy b
Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, 40126 Bologna,
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* Corresponding Author. Tel.: +39 055 4573717; fax: +39 055 4573779.
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E-mail address: [email protected] (S. Furlanetto).
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Abstract
30 In this article a review on the recent applications of multivariate techniques for optimization of
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electromigration methods, is presented. Papers published in the period from August 2007 to February 2013,
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have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the
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characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number
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of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and
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pointing out the most common issues involved in multivariate set-up of CE methods, is provided.
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Keywords: Electromigration Methods; Experimental Design; Multivariate Optimization; Review
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1.
Introduction
43 “Experimental design” is a well-established procedure useful in various research fields and it is
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included in chemometric techniques. The pioneers of chemometrics were well-recognized scientists, namely
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Roger Phan-Tan-Luu [1], Svante Wold [2], Désiré Luc Massart [3,4], Michele Forina [5] and Bruce R.
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Kowalski [6]. The term “chemometrics” was coined by S. Wold in a grant application in 1971, and the
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International Chemometrics Society was formed shortly thereafter by B. Kowalski and S. Wold [7]. S.
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Wold’s group founded Umetrics [8], devoted to multivariate data analysis, including experimental design. In
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the 70’s R. Phan-Tan-Luu founded the Laboratory of Methodology of Experimental Research in Marseille,
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spreading in the world the use of experimental design. Phan Tan-Luu founded also LPRAI Company [9],
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dedicated to experimental design and still active in France.
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Experimental design is a methodology of experimental research, called also “Design of
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Experiments” (DoE), in which the variables under study are simultaneously changed inside an experiment. It
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includes different strategies and statistical tools aimed to guide the researcher in selecting regions of interest
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inside a large experimental region, with a minimum number of experiments. The use of experimental design
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has been increasing in the last years due to the large diffusion of dedicated softwares, the transfer of know-
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how, the increase of collaboration between university and industry, as well as the need of systematic analysis
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of experimental data.
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DoE includes screening, response surface, mixture and process-mixture matrices. Each of these
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should be used according to the aim of the study. Responses and controllable variables, named factors,
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should be identified, and a regression model linking factors to responses should be postulated. According to
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the postulated regression model, an experimental matrix should be selected. An experimental matrix is a
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table where the columns report the factors’ values and the rows report the codified values that each factor has
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to assume in each experiment, i.e. the rows represent the experiments. Once the experiments are carried out
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and the responses are measured, the regression model is calculated using multivariate linear or partial least
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square regressions. The analysis of variance (ANOVA) is used to establish significance and validity of the
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model. If the found regression model is significant and valid, it can be used to explain the obtained data. If
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several responses are simultaneously considered, DoE tools such as desirability function [10] or Pareto
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optimization [11] may be useful to find a proper compromise among them. In analytical chemistry, experimental design is effectively used in optimization and validation steps.
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When optimizing an analytical method, the easier approach is the screening one, that selects from a number
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of potential factors, studied at n levels, those significant, with a limited number of experiments. On the
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contrary, in response surface methodology (RSM) the number of experiments increases; anyway, in this case
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it is possible to obtain a regression model describing in a provisional way the response variation in the
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experimental domain.
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In capillary zone electrophoresis (CZE), several chemical, physical and instrumental parameters
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should be controlled in order to obtain good analysis performances in terms of minimum analysis time and
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high resolution, efficiency and sensitivity; moreover, these parameters may be often interacting in nature.
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Only process or independent variables (PVs) are involved in CZE, and their easy change permits an
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advantageous use of experimental design. When electrokinetic chromatography (EKC) is used, a
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pseudostationary phase (PSP) is present in the capillary, and the degree of separation, efficiency, and
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analysis time may depend on both electrophoretic mobility and partitioning. In particular, in microemulsion
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electrokinetic chromatography (MEEKC) mixture components (MCs) of the microemulsion constituting the
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background electrolyte (BGE) should be also taken into consideration. Thus, the PSP composition is
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fundamental for the performance of electrophoretic run and changes in the levels of process factors can
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affect the separation properties of the PSP; in the same way, changing the MC proportions of the PSP may
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differently affect the response for different combinations of the PVs. Usually, the optimization of such
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systems involved a two-stage approach, consisting in the optimization of mixture factors by mixture design
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followed by the optimization of process factors by response surface study. Anyway, it should be
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recommended to simultaneously vary mixture and process variables using a mixture-process variable
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approach (MPV), which uses experimental matrices where the process and mixture factors are
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simultaneously changed. MPV models, which correlate the MCs and the PVs with responses, are postulated.
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MPV approach is more complex than other strategies, but it allows the power of experimental design to be
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highlighted.
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5 Characteristics and applications of the designs used in the optimization of analytical methods have
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been recently reviewed [12-15], also focusing the attention on the optimization of separation methods such
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as chromatography [16-19] and capillary electrophoresis [16,17]. In particular, the multivariate optimization
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of capillary electrophoresis methods was the topic of reviews by Altria et al. [20], by Sentellas and Saurina
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[21] and more recently by Hanrahan et al. [22]. An overview of the experimental designs in the optimization
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of chiral CE and CEC has been also freshly provided by Dejaegher et al. [23].
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This paper intends to focus on the recent applications of multivariate optimization of capillary
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electrophoresis methods, taking into account the publications dating from August 2007 to February 2013,
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with a few exceptions which appeared to be of specific interest. The chapters are grouped by type of
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capillary electrophoresis operative mode and the information gathered is collected in tables. In particular,
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Table 1 gives a comprehensive overview on the applications of multivariate optimization of CZE and related
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techniques, while Table 2 refers to micellar electrokinetic chromatography (MEKC) and MEEKC. Finally, a
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critical discussion of the practical issues which may be encountered when optimizing electromigration
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methods by DoE is presented.
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Capillary zone electrophoresis (CZE)
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In capillary zone electrophoresis (CZE) the separation is based on differences in the charge-to-mass
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ratio of the analytes, thus all the relevant factors affecting the solutes’ charge, play a role in selectivity
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tuning. In particular, buffer pH, type and concentration, influence the migration velocity of the analytes, the
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separation efficiency and peak shape [24].
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2.1.
Buffer pH
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Among the factors affecting CZE separation, the most important is undoubtedly the buffer pH and,
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as theoretically demonstrated, the optimum separation selectivity is obtained at buffer pH values close to the
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pKa of the analytes [25]. Analytes containing both acidic and basic functions have to be dealt by considering
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the existence of a critical pH value corresponding to the average of the pK a values of acidic and basic
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6 function, i.e., the isoelectric point, pI. In a study on the separation of biogenic amines including amino acids,
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the chemometric evaluation of the effect of several parameters (BGE concentration, BGE pH, voltage and
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percentage of organic modifier) on the Chromatographic Exponential Function (CEF) confirmed that pH was
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the most significant individual factor with the highest effect on the quality of the electropherogram [26]. The
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pH range that has to be optimized by DoE is often preliminarily selected by univariate evaluation of the
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analytes behavior under variation of buffer pH. The evolution of electrophoretic mobility versus buffer pH of
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a number of organic aliphatic acids (acetic, aspartic, citric, formic, lactic, malic, oxalic, phthalic, pyruvic,
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succinic and tartaric acids) suggested that separation was free from interferences by system peaks normally
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occurring in direct UV detection, within two pH ranges, namely 3.0–4.2 and 5.5–7.0. Successively, by
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experimental design it was found that pH 3.4, in the presence of cetyltrimethylammonium bromide (CTAB)
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for electroosmotic flow (EOF) reversal, was the best condition. The method was validated and applied to the
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determination of some aliphatic acids in Brachiaria Brizantha extracts [27]. A similar approach was also
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applied in finding CZE conditions for the analysis of antimicrobials including fluoroquinolones,
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sulfonamides and chloramphenicol. In order to limit the number of “wet” experiments, the evolution of
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solutes mobility versus pH was estimated by simulation experiments to restrict the range of pH to be
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optimized by experimental design [28]. A nice application showing the opportunity offered by preliminary
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investigation on the effect of pH on CZE selectivity by simulation software Peakmaster 5.2, was given by
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Johns et al. [29]. The separation of 17 antipsychotics was approached by exploring the analytes behavior in a
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very wide pH range (2.0-10.5), covering all of the occurred protonation stages. It was found that strongly
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acidic conditions gave the best response as Chromatographic Response Factor (CRF). The full protonation of
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basic solutes is rather conventional in CZE analysis; in these situations, the difference in solutes molecular
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masses addresses the separation selectivity. Accordingly, strongly acidic BGE were found to be suitable in
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the analysis of basic cardiovascular drugs [30] and antihistaminics [31] performed in Tris-phosphate buffer;
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in particular, it was found that among the pH values investigated by DoE, the best response
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(Chromatographic Response Function, CRS) was achieved at the most acidic conditions (pH 2.5–2.7). The
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analysis of fluoxetine and norfluoxetine was conveniently carried out under on-line sample stacking
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conditions at pH 2.5 that additionally minimized the absorption of basic analytes to the inner capillary wall
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[32]. Schappler et al. found that even if propranolol could be analyzed in a wide pH range (pH studied within
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7 2.0–7.0), strongly acidic conditions were the best for the improved detection response (signal-to-noise) using
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Laser-Induced Fluorescence Detector (LIF). In that application, the responses sensitivity and efficiency were
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studied by a sequential multivariate methodology that involved a response surface approach carried out in
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more than one step. In particular, the response surface study was performed by means of a Box-Behnken
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design (BBD), introducing the applied voltage as a new factor in a second set of runs. At the end of the study
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all the results were merged and used to find a regression model able to describe the response in the whole
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considered experimental domain [33]. More critical separations of basic compounds, can be conveniently
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carried out by selecting a buffer pH close to the pKa values of the analytes; according to the Handerson-
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Hasselbalch equation, it can be derived that a ionized base still exists also at pH values well above the pK a;
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e.g., when pH of the electrolyte is pKa + 1, the fraction of the protonated base [BH+]/[B] is 1:10. In an
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application of experimental design to CZE, the analysis of aconitine and hypaconitine was optimized
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planning the experiments according to an experimental design strategy investigating each factor at 5 levels
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and finding the relationship among factors and responses with neural network. In particular, the pH range
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was studied within 7.5 to 9.5 and it was pointed out that it played the major role affecting the peak resolution
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with a maximized separation at pH 9.5 [34]. Similar conditions were found to be useful in analysis of
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epinastine, however in such situation the involved analytical challenge was rather simple [35].
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Electromigration techniques are conveniently applied for analytical profile (quali- and quantitative)
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of natural polyphenol compounds that increasingly attract the interest of the researchers for the role they
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have shown against biomarkers for cancer, cardiovascular diseases and other degenerative diseases [36]. Due
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to their acidic nature, phenolic compounds can be analyzed in CZE using borate buffer. A response surface
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study by means of a central composite design (CCD) was used to find optimum conditions for the separation
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of 13 phenolic compounds from extra-virgin olive oil. The considered responses were five selected critical
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resolution values of adjacent peaks and the experimental design approach showed that the effect of pH
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changing on separation was rather complicate [37]. By directly analyzing response surfaces, it was possible
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to point out optimum conditions; further sets of conditions selected by desirability function were only
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slightly different with respect to those found.
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When several analytes have to be separated and RSM is applied, the migration order of the
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compounds should not preferably be changed in order to be able to univocally measure the response
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resolution [38]. In general pH has a strong effect on migration behavior of analytes, and for this reason
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buffer pH has sometimes been suggested to be maintained at a constant value (e.g., pH 10 or the native pH of
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borate buffer) also when using a multivariate optimization [39,40].
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2.2.
Buffer type and concentration (electrolytic conductivity and ionic strength)
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The running buffer selection is a key factor to the success of any CE separation. As a general rule, it
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has to be pointed out that the buffer system effectively works in a pH range approximately corresponding to
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two units centered on the pKa value. The most suitable buffer type for a given CZE separation, is in general
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identified by preliminary experiments by univariate method [33]. More generally, phosphate and borate
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buffers can be considered as ideal for conventional CZE separations because of the low absorbance at the
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wavelength of detection (often in the UV range); in addition, they showed to be suitable for analysis using a
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number of detection systems including electrochemiluminescence (ECL) [30,31] and LIF detection [33]. On
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the other hand, buffer concentration affects the electrophoretic behavior of the analytes by influencing the
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separation selectivity, the EOF strength and stacking effect. A chemometric strategy for buffer selection was
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developed by considering some relevant descriptors of BGE systems used for CZE analysis; they were (i)
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pH, (ii) , conductivity; (iii) I, ionic strength and (iv) , relative viscosity. Two distinct series of potentially
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useful BGEs comprising 222 buffers for analysis of acidic drugs (arylpropionic acids) and 117 buffers for
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analysis of basic drugs (beta-blockers), were screened by Principal Component Analysis (PCA). In analysis
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of acidic drugs, 0.1 M borax buffer (pH = 9.22, = 8.72 mS cm-1, I = 0.200 M, = 1.067) was identified as
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the most suitable BGE, whereas in analysis of the selected set of basic drugs, citrate buffer (pH = 4.04, =
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3.35 mS cm-1, I = 0.066 M, = 1.038) was found to be the optimum [41,42]. Borate buffer is often applied in
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CZE at the native pH for its ability to give in-situ complexation of polyhydroxy compounds such as
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carbohydrates and polyphenols aglycones [36,37,39,40]. In particular, borate forms charged mobile five-
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membered-ring complexes (with 1,2-diols) and six-membered ring complexes (with 1,3-diols) and as a
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consequence, an increased selectivity of separation can be achieved [43].
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The electrical conductivity plays an important role in on-line preconcentration, mainly when sample
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stacking has to be performed. In the analysis of real samples of poliovirus, the major challenge was the
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sensitivity enhancement, thus optimization with DoE was aimed to identify all the favorable conditions for Page 10 of 62
9 increasing peak height. Plackett-Burman design (PBD) was used in the screening step and CCD was
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employed for the response surface study. It was confirmed that a successful separation of the components of
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large sample plugs, required high conductivity mismatch between BGE and sample plug. This was obtained
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by dilution of real samples and by increasing the BGE concentration to the maximum values among the
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tested levels (borax 100 mM) [44]. Phosphate - triethanolamine (TEAOH) buffer at low pH was considered
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to be useful for analysis of fluoxetine and norfluoxetine by short-end injection and on-line stacking; in
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particular TEAOH was used to prevent the analytes adsorption onto the inner capillary wall. A screening
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approach using a fractional factorial design (fFD) was chosen for the simultaneous optimization of factors
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affecting both separation and sample stacking efficiency. Then, a response surface study by means of CCD
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was used for only optimizing the response resolution between the two analytes [32].
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2.3.
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Effect of addition of organic solvents to the BGE
The addition of organic solvents to the BGE is a good choice to avoid precipitation of the solute
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during the electrophoretic run; on the other hand, the presence of organic solvents changes zeta potential and
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viscosity, thus a variation in electroosmotic velocity is observed as a consequence. Methanol, ethanol, n-
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propanol, isopropanol and acetonitrile (ACN) are the most used solvents because of their aqueous solubility
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and low UV absorption. It has been shown that using mixtures of water-alcohols (e.g., methanol and
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isopropanol), the EOF decreases compared to the pure aqueous buffer; differently, addition of ACN, because
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of its very low viscosity, decreases the EOF. Eventually, addition of organic solvents to buffer solutions can
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lead to pKa variations of the analytes with effects on their absolute and actual mobility [45,46]. Owing to the
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complex combination of the effects of the organic solvents (interaction with the analytes through solvational,
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dissociative or multimolecular complex formation phenomena) on the electromigration of the solutes,
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chemometric tools are particularly suitable to investigate their possible interactions with other parameters
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involved in CZE separations [26,32,34,39,40,47].
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2.4.
Effect of buffer additives in CZE
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2.4.1. Cyclodextrins Page 11 of 62
10 Cyclodextrins (CDs), because of their ability in inclusion-complexation of a considerable number of
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analytes (host–guest interaction), can be supplemented to the CZE buffer to favorably modify the
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physicochemical characteristics of the guest molecules by producing altered migration behavior. Several
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reviews deal with the use of CDs in capillary electrophoresis as selective agents for discrimination between
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positional isomers, molecular substructures, homologues and enantiomers [48-50]. Rousseau et al. applied
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experimental design to estimate the effects of the nature of CD (two different sulfated CDs were tested) on
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the enantioseparation of -blockers under non-aqueous CE conditions (NACE) [51]. More generally,
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however, preliminary univariate experiments are performed to select the suitable CD to be added to BGE;
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then CD concentration and other typical variables affecting CZE separation are optimized by DoE either for
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enantioresolution [52-57] and separation improvement of strictly related compounds [58,59]. Often, when
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anionic CDs are used in analysis of basic compounds, the investigated pH range is maintained within the
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values that allow for the full ionization of the chiral selector concomitantly with the solutes protonation. This
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condition indeed is able to provide an enhancement in separation selectivity because of the higher mobility
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difference between the free and the complexed enantiomers. Accordingly, in an application aimed at the
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enantioselective analysis of propranolol and a metabolite using carboxymethyl--CD (CM--CD), the
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investigated pH range in the response surface study (BBD method), was within 8.0–9.0 [56], whereas using
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sulfated CDs (strong acidic behavior), also buffers at low pH values were used in enantioseparation of
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clopidogrel [54] and tamsulosin [55].
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2.4.2. Proteins
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The tertiary structure of proteins is supposed to be responsible for the chiral recognition of neutral,
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basic and acidic analytes. Human serum albumin (HSA) was found to be particularly useful as a chiral
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additive in free solution CE because of its ability in enantioselective binding of a number of drug molecules
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by establishing hydrophobic and dipole-dipole interactions as well as hydrogen bonding. In general, owing to
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their UV absorption, when proteins are supplemented as additives to the BGE in free solution, the partial-
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filling technique has to be applied [50]. The enantioresolution in partial-filling CE is the result of the proper
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selection of pH; this paramenter affects the EOF and the electrophoretic mobility of the analytes and the
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protein, according to their respective pKa and pI values. In addition, the length of the sample plug in relation
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with that of the capillary has to be carefully identified. Because of the reciprocal interactions of these
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parameters, the chemometric optimization of enantioseparation can be considered as a good choice, as
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demonstrated by the results achieved using HSA as chiral selector in analysis of basic drugs including
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phenotiazines [60], correlated antihistamines [61] and the pesticide nuarimol [62].
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3.
Nonaqueous capillary electrophoresis (NACE)
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In nonaqueous capillary electrophoresis (NACE), pure organic solvents or their mixtures, are used in
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preparation of running buffers for improving solubility of hydrophobic analytes and/or to gain specific
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selectivity because of the effect of the solvents on acid-base properties of the solutes. NACE widens the set
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of physicochemical characteristics of the running buffers, which are known to affect the electrophoretic
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behavior of the solutes by influencing interactions with solvents, additives and ion-ion interactions that are
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too weak or cannot take place in aqueous BGEs [63-65]. Because of their solubility in organic solvents
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mixtures, the most used supporting electrolytes in NACE are ammonium acetate, ammonium formate and
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sodium acetate. In the separation of seven antihistamines by NACE using electrochemical (EC) and ECL
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detections, a CCD was applied for examining the factors such as apparent pH*, separation voltage and
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concentration of the uncommon electrolyte tetrabutylammonium perchlorate (TBAP). A strong interaction
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between buffer pH* value and voltage, together with significant quadratic effects of all the considered
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factors were found acting on the selected response CRS [66].
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Supplementing CDs as chiral selectors in NACE buffers could provide specific enantioselectivity;
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obviously, the hydrophobic interaction between solutes and CDs does not exist in organic solvent, thus it can
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be concluded that inclusion complexation occurs to a small extent, if at all. Instead, solutes might interact on
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the mouth of the CD and bind the hydroxyl groups on the rims through polar interaction; in addition, by
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using charged CD electrostatic interactions can be involved in enantiorecognition [67]. Rousseau et al.
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performed an exhaustive study on the effect of the nature of anionic CD (heptakis(2,3-di-O-methyl-6-O-
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sulfo)-β-CD (HDMS-β-CD) and heptakis(2,3-di-O-acetyl-6-O-sulfo)-β-CD (HDAS-β-CD)) and BGE
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composition on the enantioseparation of ten β-blockers. The experimental design involved both qualitative
290
and quantitative factors and the investigated responses were enantiomeric resolution, mobility difference and
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selectivity. Due to the correlation among the three responses the Authors only discussed the model relative to
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enantiomeric resolution [51]. An analogous approach was also followed by the same team of Authors in a
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subsequent study for evaluating the enantioseparation of 10 basic drugs by another anionic CD, namely
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heptakis(2-O-methyl-3-O-acetyl-6-O-sulfo)-β-CD (HMAS-β-CD) [68].
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Micellar electrokinetic chromatography (MEKC) and microemulsion
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electrokinetic chromatography (MEEKC)
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Electrokinetic chromatography (EKC) is the method of choice for the analysis of neutral compounds
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and one of the most versatile separation approaches among the electromigration methods. In micellar
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electrokinetic chromatography (MEKC) a surfactant (often sodium dodecyl sulfate, SDS) is supplemented to
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the electrophoretic BGE at concentration above the critical micelle concentration (cmc) to form anionic
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micelles whose core is highly hydrophobic, as it is formed by the SDS tails. Neutral analytes, driven by the
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EOF toward the detection end, are incorporated into the micelle according to their hydrophobicity and the
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separation is the result of combination of chromatographic partitioning of the solutes between the micellar
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phase (the pseudostationary phase, PSP) and the continuous phase. In analogy, the charged solutes undergo
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to the same process, however in such a situation the separation mechanism combines the solutes partitioning
308
into micelles and electrophoretic migration [69-71]. In microemulsion electrokinetic chromatography
309
(MEEKC) the separation medium is constituted of a nanometer-sized emulsion based on oil droplets (such as
310
n-octane or other hydrophobic solvents) suspended in aqueous buffer. Microemulsions are stabilized by the
311
presence of a surfactant and a co-surfactant, which is a short-chain alcohol such as butanol [70,72,73].
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The opportunity for selectivity tuning in EKC are thus very wide, including variation of BGE type,
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pH and concentration, but also selection of the surfactant, optimization of surfactant concentration, use of
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mixed surfactants, addition of organic modifiers and other additives. Giving the multiple and often complex
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mechanisms involved in EKC separations, experimental design appears to be a more efficient tool for the
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optimization of separation conditions than conventional univariate methods.
Page 14 of 62
13 One of the major factors affecting selectivity in EKC is undoubtedly the nature of surfactant. The
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retention of the solutes by SDS micelles, the most used surfactant in MEKC and MEEKC, is primarily
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influenced by size of the molecules and their hydrogen bond accepting basicity. In other words, the capacity
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factor increases with the size of the analytes and decreases for stronger hydrogen bond acceptor bases.
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Sodium cholate (SC) has been described as a surfactant creating more polar micelles compared to SDS and,
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in addition, SC has shown higher hydrogen bond acceptor basic character [74]. Actually, SC was favorably
323
used for the separation of highly hydrophobic tocopherols in vegetable oils by means of nonaqueous MEKC
324
[75],
325
dimethylmyristylammonio)propanesulfonate in the preparation of mixed micelles allowed the fast separation
326
of neutral budesonide and related substances [38]. Because of the lack of knowledge about the mechanism of
327
solute-micelle interactions, multivariate strategy for optimizing such a complex system was found to be
328
useful. A response surface study by means of a 34-run Doehlert design (DD) allowed the optimization of the
329
mixed MEKC system including the selection of the optimum borate concentration, pH and running voltage
330
[38].
combined
use
with
the
zwitterionic
surfactant
cr
its
3-(N,N-
M
an
us
and
ip t
317
Chiral micelle polymers were used for hyphenation of MEKC to electrospray (ESI) mass
332
spectrometry; in such a situation multivariate methodologies were helpful not only in the optimization of the
333
parameters affecting separation, but also for the fast selection of operating factors influencing MS detection
334
(sheath liquid and spray chamber parameters in ESI-MS) [76-78].
ce pt
ed
331
In general, as in CZE, the choice of BGE pH in MEKC and MEEKC is based on its ability to change
336
the ionization of analytes with a consequent alteration of their distribution towards the PSP; in addition,
337
buffer pH controls the ionization of inner capillary wall and EOF. In a recent study on the separation of
338
ramipril and related substances, it was found very useful to carry out MEEKC experiments under strongly
339
acidic phosphate buffer (pH 2.5). In fact, the suppression of the EOF allowed to perform separation in
340
reversed flow mode (RF-MEEKC), which, combined with the short–end injection modality, allowed the fast
341
separation of ramipril and related substances by preventing the cis-trans isomerization of the proline-moiety
342
containing compounds (Fig.1). The use of a mixture design followed by a response surface study allowed the
343
complex separation system to be optimized with the aid of desirability function (Fig. 2), resulting in the
344
complete separation of the analytes in about 10 min (Fig. 3) [79].
Ac
335
Page 15 of 62
14 Water-miscible organic solvents are commonly supplemented to MEKC systems to increase the
346
solubility of hydrophobic compounds as well as to control their partitioning into the micelle [69-71]. In
347
general, preliminary experiments by univariate approach are carried out to select the suitable organic solvent
348
(in general ACN, methanol, propanol etc.) for the intended purpose, then the concentration is selected by
349
DoE. The volume fraction of the solvent should not exceed 30% since higher levels may break down micelle
350
or microemulsion [76,77, 80-87].
ip t
345
Addition of cyclodextrins in MEKC (CD-MEKC) combines the potential of micelles as separation
352
carrier with the capability of cyclodextrins in recognizing specific solute molecules by hosting them into the
353
hydrophobic macrocyclic cavity. The inclusion complexation into the chiral cyclodextrin cavity occurs in
354
competition with the partition of the analyte into the surfactant micelle providing the system with an
355
extended flexibility and widening of selectivity and enantioselectivity tuning [88-90]. Variations of
356
cyclodextrin concentration in CD-MEKC optimization are however restricted by the strong interaction of
357
surfactant monomers with the cyclodextrin cavity, which competes with the self-assembling process (e.g.,
358
SDS aggregation) [88-92]. In a preliminary investigation based on conventional approach, Abromeit et al.
359
tested several neutral CDs for improving MEKC analysis of highly hydrophobic lipoxygenases metabolites
360
and found that -CD offered the best performance. Then, a multivariate optimization of separation
361
conditions involving factors such as SDS concentration, capillary temperature and voltage, buffer
362
concentration and pH, together with -CD concentration was conducted, first employing a fFD and then
363
deepening the study by a CCD [93]. Similarly, univariate optimization of SDS-based MEKC was not
364
successful in separation of pharmaceuticals as contaminants in drinking water even if different organic
365
solvents (the most suitable was found to be isopropanol) were tried as modifiers of the conventional SDS
366
system. Addition of CDs was investigated and it was found that sulphated -CD provided the best
367
separation; a face centered composite design (FCD) allowed the separation conditions to be optimized [84].
Ac
ce pt
ed
M
an
us
cr
351
368
A dual system of cyclodextrins in MEEKC was for the first time used and optimized by experimental
369
design for the separation of clemastine and its related substances. The addition of two suitable cyclodextrins
370
to MEEKC was compulsory to obtain a baseline separation of the compounds and the multivariate
371
optimization was necessary to optimize such a complex system, in particular by identifying the optimum
372
ratio between the two cyclodextrins and the interactions between the parameter concentration of each of the
Page 16 of 62
15 CDs [94]. Similarly, the separation of oxybutynin and its impurities was accomplished by optimized CD-
374
MEEKC [95]. By a proper experimental strategy, involving first a mixture design for finding the best
375
microemulsion composition (Fig. 4) and then RSM for optimizing the process parameters related to
376
instrumental conditions and buffer (Fig. 5), the complete separations of the main drugs in the presence of the
377
impurities and internal standards in short analysis time were obtained [94,95]. It should be noted that in such
378
situations the variations of experimental factors are constrained because the stability of the microemulsion
379
could be jeopardized by addition of CDs that can act as sequestering agents towards oil, surfactant and
380
cosurfactant. However, in MEEKC the effects of changing the proportions of the mixture components (MCs,
381
related to microemulsion composition) and process variables (PVs, namely voltage, buffer concentration,
382
buffer pH) can be simultaneously studied by applying a mixture process variable (MPV) approach. For the
383
first time in the literature, Piepel et al. used the MPV approach in the optimization of the MEEKC analysis of
384
coenzyme Q10, ascorbic acid and folic acid [96]. Despite of the high number of runs of the design, the MPV
385
approach pointed out for the first time the presence of important interactions between PVs and MCs (Fig. 6).
M
an
us
cr
ip t
373
CE methods involving the use of a PSP such as MEKC and MEEKC are often combined with on-line
387
sample preconcentration approaches that are used to overcome the poor concentration sensitivity of
388
conventional UV detection. Large volume sample stacking is a mode of sample injection consisting in
389
introducing large volume of sample into the separation capillary; successively, the analytes dissolved in the
390
large sample zone are focused into a narrow zone before separation, in order to obtain improved
391
concentration sensitivity [97]. Using a charged PSP, sweeping can be then performed, as micelles or
392
microemulsions by penetrating the sample zone allow the accumulation of the solutes in a small analytical
393
band. Higher is the hydrophobicity of the analyte and higher is the concentration efficiency. Although the
394
sweeping technique proposed by Quirino and Terabe was originally used for concentration of neutral
395
molecules [98], it has shown to be equally well applied to ionic analytes as long as they have high affinity
396
toward the PSP [99]. Giving the multiple variables and their complex interactions, on-line preconcentration
397
can be favorably optimized by DoE [85-87].
Ac
ce pt
ed
386
398 399
5.
Capillary electrochromatography (CEC)
400 Page 17 of 62
16 In capillary electrochromatography (CEC) the migration of charged solutes is combined with their
402
chromatographic partition toward a stationary phase in capillary format suitable to be operated by
403
conventional CE instrumentation. The CEC columns can be constituted by small particles (e.g., 3 m),
404
packed in a fused-silica capillary and fixed by frits prepared by silica sintering. The advances in column
405
technology have led to an increasing preference for application of CEC monolithic column consisting of
406
porous structure (both organic such as polymer-based and inorganic, typically silica-based) fabricated inside
407
the fused-silica capillary tube and forming a continuous bed of chromatographic material, thus avoiding frits
408
[100]. However, to produce reliably monolithic columns requires the application of strictly controlled
409
conditions. A three level screening design was thus employed for evaluating the influence of four factors
410
involved in the sol-gel synthesis on the performance of C18-silica monoliths; specifically, amounts of
411
tetramethylorthosilicate (TMOS) and polyethylenglicol (PEG), gelation temperature and modifying time
412
were screened. Then, the obtained monoliths were modified by octadecyltrichlorosilane and tested for
413
separation of benzene, toluene, ethylbenzene, 1-phenyhexane in reversed-phase mode, to estimate
414
performance parameters. The responses included both monolith properties (equivalent length and
415
electrokinetic porosity) and properties related to quality of the electropherograms, namely efficiency,
416
resolution, retention factor and symmetry factor. It was found that the concentration of TMOS and PEG in
417
the starting mixture, and the gelation temperature, had the most important effects on the separation
418
performances. Eventually, the optimized conditions were used for monolithic column preparation applied to
419
the separation of acetylsalicylic acid and related impurities [101]. Similarly, a response surface study based
420
on CCD allowed the evaluation of the effects of the polymerization mixture composition on methacrylate-
421
based monolithic columns for CEC and pressure assisted CEC (p-CEC). The mixture composition affects the
422
structure and as a consequence the chromatographic properties of the stationary phase; thus, the influence of
423
the ratio of the pore-forming solvents (mixture of water, 1,4-butanediol and 1-propanol) and the 1,4-
424
butanediol percentage in the mixture, were evaluated. Seven very different compounds (warfarin, ketoprofen,
425
praziquantel, paracetamol, metoprolo, pyrene, oxazepam) were used to prepare test samples and the
426
responses included retention time, theoretical plate number, peak asymmetry and retention factor. The
427
advantageous properties of the stationary phase identified by DoE were demonstrated [102].
Ac
ce pt
ed
M
an
us
cr
ip t
401
428
Page 18 of 62
17
429
6.
Capillary electrophoresis – mass spectrometry (CE-MS)
430 The electrospray ionization source (ESI) is well-suited for CE-MS interfacing since it produces ions
432
directly from liquid solutions at atmospheric pressure. Some of the limitations are related to buffer
433
composition and on the necessity for introduction of “makeup” liquid (sheath flow) to support the ionization.
434
The specific parameters to be optimized in hyphenation with ESI mode include electrospray voltage,
435
nebulizing gas flow rate, drying gas flow rate and temperature. In addition, the composition of the coaxial
436
makeup liquid and its flow rate, significantly affect the ionization efficiency and have to be appropriately
437
selected for good coupling performances. The most common buffers used for a conventional electrophoretic
438
separations such as borate and phosphate are not suitable for CE coupling with ESI-MS because of their poor
439
volatility and the risk of MS source contamination. More suitable buffers are based on formic and acetic
440
acid, ammonium carbonate and ammonium acetate [103,104].
an
us
cr
ip t
431
Martin et al. presented an interesting work on the optimization of a CE-MS method for peptide
442
analysis, including hepcidin. First, a mixture of model compounds was used, optimizing the conditions by
443
univariate approach to resolve a peptide test mixture by keeping into account the necessity of using a volatile
444
BGE. In the second step, the intensity of hepcidin signal was considered as the response, and a 18-run
445
asymmetric screening matrix was used for studying 8 factors at 2 levels and 1 factor at 3 levels. Seven
446
factors were quantitative (capillary voltage, applied temperature, nebulizing gas pressure, drying gas flow
447
rate, organic modifier concentration, volatile acid concentration and BGE concentration) and two were
448
qualitative (nature of the organic modifier and of the volatile acid present in the sheath liquid). The
449
significant factors (capillary voltage, applied temperature, buffer concentration and pressure of nebulizing
450
gas) were studied in a more in-depth way by a response surface study carried out by a CCD [105].
Ac
ce pt
ed
M
441
451
The wide opportunity for selectivity (and enantioselectivity) tuning by MEKC, can be exploited in
452
CE-MS hyphenation using molecular micelles because of their compatibility with the use of high organic
453
solvents percentage and volatile buffers; in addition other important features of molecular micelles include
454
zero cmc as well as their poor ionization that reduces ion suppression providing improved S/N in ESI [106].
455
He et al. presented detailed optimizations of MEKC-ESI-MS methods either in negative and positive ion
456
mode for the chiral separation of binaphthyl derivatives using a polymeric surfactant as a PSP [76,77]. Wang
Page 19 of 62
18 et al. reported the multivariate optimization of a MEKC-MS method for the chiral analysis of barbiturates
458
[78]. In all these studies RSM was used to identify the optimal conditions for both separation and MS
459
detection. For the chiral separation of binaphthyl derivatives the factors estimated were buffer pH, ACN
460
percentage, concentration of surfactant, concentration of ammonium acetate and voltage, considering as the
461
responses resolution and analysis time [77], also by applying desirability function [76]. In general it was
462
found that the most important factors affecting enantioresolution and method performance were pH and
463
nebulizer pressure (the latter affects enantioseparation because of the buffer suction effect that reduces the
464
laminar flow in capillary).
cr
ip t
457
466
7.
Critical discussion
7.1.
Selection of the variables
us
465
M
468
an
467
The accurate selection of the variables plays a key role for the success of the optimization, thus the
470
attention should be focused on all the factors that potentially affect CE analysis (both separation and
471
detection). The researcher can be tempted to reduce the number of variables considered in the multivariate
472
study in order to reduce the number of planned experiments and to obtain models/results which at a first
473
sight may seem more easily interpretable. Anyway, the selection should be carefully based on preliminary
474
experiments or knowledge and not on the basis of researcher’s convenience. In general, carrying out heavy
475
univariate studies with the aim of applying experimental design to a limited number of factors [107] should
476
be avoided.
Ac
ce pt
ed
469
477
A further critical procedure is to divide the potentially influent factors in two separated designs [27]:
478
in this way, the interactions between the two sets of factors are missing. In order to overcome this issue, it is
479
advisable to proceed with a screening step for identifying influent factors on the response and then with a
480
response surface study in order to obtain comprehensive information on all the considered factors.
481
A particular case where factors under study are divided is represented by a MEEKC system where
482
both mixture components (MCs) and process variables (PVs) have to be optimized. A plain strategy can be
483
applied to optimize first the MCs by a mixture design, and then the PVs with a response surface study
484
[79,94,95,108]. Nevertheless, recently Piepel et al. [96] demonstrated the occurrence of important Page 20 of 62
19 485
interactions among MCs and PVs, evidenced by the use of MPV approach (Fig. 6). When applying MPV the
486
number of experiments rapidly increases and the statistical tools for interpreting data are more complex.
487
Consequently, the researcher can weight advantages and issues involved in the analytical problem under
488
study in order to decide between the application of sequential mixture design and RSM or MPV approach. Finally, injection time, unless involved in identification of conditions for on-line preconcentration
490
[32,44,87], should preferably not be included among the CE parameters [35,40,83,109]. Instead of focusing
491
on this parameter, the researcher should preferably identify the level of concentration of test sample
492
according to the objective of the analysis (e.g., the LOQ in impurity assay should be at least 0.1% with
493
respect to the main drug) [38,79,94,95].
us
cr
ip t
489
494
7.2.
Selection of the experimental domain
an
495
The selection of the experimental domain is a very critical step in the multivariate optimization and
497
should be carefully made by the researcher. As a matter of fact, DoE is able to provide information only
498
inside the investigated experimental domain. Moreover, the selection of a suitable experimental range of
499
each parameter under study, is necessary for maintaining all the planned experimental runs within acceptable
500
conditions of electric current, baseline noise, analysis time and prevention of additives precipitation in the
501
buffer. Before running the experiments, one should practically check that the established conditions might be
502
performed within the limits of the experimental domain [1]. This is a valuable way of operating which could
503
be included in best DoE practices, as it can help to avoid an experimental design to be broken off for
504
practical/technical reasons.
Ac
ce pt
ed
M
496
505
The complexity of the effects of pH in CE separation makes the selection of its experimental domain
506
very critical. As mentioned, the running buffer pH strongly affects the CE separation because of the effects
507
on the EOF mobility, electrophoretic mobility of the analytes as well as those of the ionic additives of the
508
BGE (e.g., chargeable cyclodextrins), that can be supplemented for selectivity tuning [54-56]. A preliminary
509
study on pH effect on separation should be carried out before application of multivariate optimization in
510
order to avoid, if possible, peak collapsing and inversion order of the peaks when running the experimental
511
plan in response surface studies. Moreover, the direct study of the behavior of the responses in a wide pH
512
range with a RSM study might be difficult in modelling with simple regression models. For this reason, a
Page 21 of 62
20 513
convenient procedure consists in the use of asymmetrical or symmetrical screening matrices that allow pH,
514
and if necessary also other factors, to be studied at a number of levels higher than 2 [110,111].
In more complex systems, a detailed theoretical and/or experimental work is required as it was
516
shown by Mofaddel et al. [57]. In order to select the pH range for chiral separation of binaphtol derivatives
517
using neutral CDs as chiral selectors, the pKa values of the synthesized compounds under study were first
518
determined. A detailed univariate investigation made it possible to find a double inversion in the enantiomer
519
migration order, as a function of the pH and of the concentration of the chiral selector [57]. In analogy, the
520
study of the mobility evolution of mixtures of drugs as a function of pH was found to be useful in identifying
521
the suitable pH range for CZE separation optimization [28,29]. Then, according to the obtained results, either
522
multivariate strategy was applied in a restricted pH range or the value of pH was directly set [39,112].
us
cr
ip t
515
In addition, the BGE system exhibits a proper buffering capacity at pH = pKa±1. In a number of
524
reports [52,83,107,113-116], this assumption was not followed and common buffers such as phosphate,
525
borate or acetate were reported to be used in an experimental domain out of the optimum pH range. In order
526
to extend the buffer capacity of the BGE, within the whole explored experimental domain, the use of a
527
combination of different electrolytes (for instance, phosphate-borate mixture, or Britton-Robinson buffer)
528
with a wider buffering pH range would be advisable.
ed
M
an
523
When dealing with enantioseparations, the lower level of the chiral selector should not to be set equal
530
to zero, because obviously the separation of the enantiomers does not take place in the absence of a chiral
531
selector. In a study on the chiral separation of a quinolizinium compound possessing two chiral centers (the
532
anthiarrhythmic RS86017), solfobutyl ether CD was used as the chiral selector and the experimental range
533
of concentration was set within 0-24 mg/mL [53]. Although some of the runs of the design were carried out
534
in the absence of chiral selector, surprisingly a nonzero resolution was reported, suggesting that misleading
535
conclusions were drawn.
Ac
ce pt
529
536
When evaluating the possibility of adding a PSP forming micelles for analytes partitioning, the
537
experimental domain should be selected according to the cmc of the surfactant. For example SDS has a
538
reported cmc of about 8 mM in water at 25°C [117]; thus, setting the experimental range within 0-10 mM,
539
even though in the presence of an additional surfactant (i.e., Brij 35 within 0 – 5 mM) [40], is misguiding for
540
establishing if MEKC can be the suitable approach for the intended purpose.
Page 22 of 62
21 541
As for capillary temperature, if no specific concerns are involved in the analysis (sample stability,
542
buffer viscosity etc..) temperature higher than 30° C would be better not included in the experimental plan.
543
This factor should be limited to values that in practice can be easily reached and maintained by the
544
instrument cooling system. If very wide ranges have been set (15-45 °C) [118], these should be adequately
545
motivated.
7.3.
Selection of the responses
cr
547
ip t
546
In the optimization of CE analysis, the aspects regarding the selectivity and efficiency of the
549
separation (selectivity as the ratio of electrophoretic mobilities, resolution, number of peaks) and analysis
550
time, should be primarily taken into consideration, together with responses related to peak detection (peak
551
height, peak area). According to the target, these responses can be singularly considered, or simultaneously
552
optimized by comparing the related response surfaces and/or calculated model coefficients or by using
553
efficient multicriteria optimization tools such as desirability function [10] or Pareto optimization [11]. The
554
examination of the response surfaces for directly finding the optimal conditions may be immediate when
555
dealing with a limited number of factors and responses; on the other hand, it may become a difficult task
556
when more than two factors and more than two responses, which can be conflicting in nature, have to be
557
simultaneously considered. In these situations, description of the surfaces and highlight of particular points
558
could be of some interest, even though application of multiresponse optimization criteria should be
559
recommended.
ce pt
ed
M
an
us
548
A further possible approach is the use of integrated responses reflecting the quality of the
561
electropherogram, which mainly take into account selectivity and analysis time, by merging the different
562
responses into one. The drawback of these methods is that a modeling of the different responses is missing;
563
on the other side, the simpler handling of the statistical treatment of cumulated responses, make them
564
advantageous. Chromatographic exponential function (CEF) was introduced by Morris et al. [119] and used
565
in Refs. [26,85,86]. Chromatographic response function (CRF), proposed by Berridge [120], and the
566
modified versions [121] were successfully employed as response in Refs. [29,81,122]. Finally,
567
chromatography resolution statistic function (CRS function), proposed by Schlabach et al. [123], was used in
Ac
560
Page 23 of 62
22 568
Refs. [30,31,40,59,66]. The modified chromatographic exponential function MCEF, defined by Morris et al.
569
[124], was employed in Ref. [125].
570
In principle, any selected response is good if the final analysis fits to the scope according to the
571
requirements defined by researcher. Anyway, some particular issues on the selection of responses deserve to
572
be pointed out. When having to optimize selectivity, the peak area/height and related RSD values are often introduced
574
among the responses under study [44,107,109,113,126,127]. However, it is well known that to obtain good
575
repeatability in capillary electrophoresis, the use of an internal standard is strictly necessary and this
576
guidance is recommended for correcting the variability in the injection volume. The ratio between resolution
577
and analysis time (Rs/t), was reported as one of the response in the optimization of a MEKC method for the
578
determination of nicotinic acid and nicotinamide [83] and in a CZE method for the determination of honokiol
579
and magnolol [112]. The aim of the optimizations was to maximize the selected response; anyway, a
580
weakness of this choice is in the lack of information on the effect of the factors on each of the separated
581
values of Rs and t, with the consequence that it is not possible to understand from the measured value if
582
baseline resolution is achieved. Although a valuable reason for this choice could be that the quality obtained
583
by modeling the proposed combined response was found to be higher than those for the separated resolution
584
and analysis time, the selection of Rs/t should be better motivated. Analogous criticism may be reported
585
when choosing as a response the sum of enantioresolution values for the simultaneous separation of two
586
couples of enantiomers, as it was reported in the paper by Liu et al.[53]. In order to motivate the choice of
587
the sum of the enantioresolution as an effective response, it was verified that the two enantioresolution
588
values followed the same trend under variations of the selected parameters (i.e., chiral selector concentration,
589
buffer pH and concentration, organic solvent percentage, voltage and temperature). Anyway, in our opinion
590
the two resolution values should be better considered separately; as a matter of fact, some experimental runs
591
showed adequate separation for resolution of an enantiomer pair, but partial or complete lack of resolution
592
for the other one.
Ac
ce pt
ed
M
an
us
cr
ip t
573
593
In a study by Bailón Pérez et al., [128], the selected response was not clearly defined, as it was
594
mentioned to be a multiple response function represented by the combination of the efficiency of nine
595
antibiotics peaks. The aim of the experimental design was the complete resolution of the analytes in short
Page 24 of 62
23 analysis time; however, high efficiency does not necessarily correspond to good resolution nor to short
597
analysis time. Similarly, the selection of the analysis times of the individual solutes as the response [129]
598
should be discarded in favor of the critical resolution between adjacent peaks. In this way immediate
599
information about selectivity is gained and complicated comparison of the obtained response surfaces is
600
avoided. Analogously, the selection of peak areas [127] or effective mobility [130] as the unique responses,
601
should be better integrated by data related to the separation of the peaks. It is worthwhile to note that in the
602
two latter reported studies, the experimental design was applied to evaluate the effect of some factors around
603
optimal conditions identified by preliminary univariate approach.
cr
ip t
596
Recently, we have reported the challenging separation of the proline-derived drug ramipril in the
605
presence of related substances. Since the cis-trans isomerization of the proline-moiety containing compounds
606
occurred in the time scale of the CE run, in this analysis the electrophoretic peaks belonging to cis and trans
607
conformers were resolved. However, a baseline separation could not be obtained because the interpeak
608
reaction zone, corresponding to the mixture of both the isomers, migrated as a plateau band between the two
609
peaks (Fig. 1a) [79]. In order to minimize this interference and to achieve the complete separation, a DoE
610
was developed by considering as the effective response the ratio Rh, calculated between the height of the
611
interpeak reaction zone and the height of the peak corresponding to the faster migrating conformer [79].
ed
M
an
us
604
Finally, it is important to underline that if responses are correlated, the use of partial least square
613
regression allows a single regression model to be obtained making easier the data interpretation without the
614
need to find new types of overall criteria [131].
615 616
7.4.
Ac
ce pt
612
Selection of the experimental matrix and statistical treatment of the data
617
In order to obtain reliable results from an experimental design strategy, another important aspect to
618
take into account is the selection of a suitable experimental matrix for the postulated regression model, i.e.,
619
the selection of a matrix able to give an accurate estimation of model coefficients. Each researcher should be
620
able to select the better experimental matrix for the regression model postulated on the basis of quality
621
criteria [1]. Other simple approaches guided by the software should be followed with care.
622
If the experimental matrix is not suitable for the regression model, analysis of variance could give
623
poor results. As stated in the introduction, ANOVA is useful in order to verify significativity and validity of
Page 25 of 62
24 the postulated model. In particular, if the model is significant and valid it can be used in a predictive way.
625
However, in presence of poor quality criteria it will be difficult that the regression model passes ANOVA. In
626
addition, in the case of RSM, a final validation of model predictivity, evaluating that the predicted and the
627
measured results are in agreement, is necessary [108]. This step involves both the uncertainty in the
628
measured response, assumed constant inside the experimental domain, and the variance function. The
629
variance function in turn depends on the point coordinates and on the dispersion matrix, that is strictly
630
correlated to the experimental matrix and the postulated regression model [1]. These few but necessary steps
631
are still often neglected; instead they should be reported as they are important in order to investigate and
632
analyze the results. Some particular issues can be found in the literature: for instance, with two-level factorial
633
designs it is possible to calculate the coefficients only for a linear regression model; as a consequence the
634
drawn response surface cannot show a curvature [107,126]. The response surfaces should describe the real
635
behavior of the response in the investigated experimental domain. If two or more responses are combined in
636
an overall criteria, it is important at the same time to obtain coherent information on the primary responses.
637
For instance, if the migration times of two analytes show the same trend, with the same zones of maximum
638
and minimum, it is questionable that the difference between these two responses also shows the same trend,
639
and a revision of the data would be advisable [132].
ed
M
an
us
cr
ip t
624
After having verified the quality of the models, the analysis of the coefficients and/or the analysis of
641
the response surfaces can be performed. Anyway, also this type of study should be conducted critically. The
642
general expected trend for the classical considered responses and factors (for instance, an increase of voltage
643
leads to a decrease of analysis time, an increase of buffer concentration leads to an increase of migration
644
time) can represent a useful, immediate check of the carried experimentation.
Ac
ce pt
640
645 646
7.5.
Selection of the target value for the responses
647
One of the main advantages of a multivariate approach is the possibility of simultaneously optimizing
648
several responses by dedicated tools such as Pareto analysis [11] and desirability function [10] if the
649
responses are not correlated. For this reason, it is not necessary to define the target values for a response by
650
considering a priori that other values for the responses could lead to deleterious effects for a different
651
response. In this sense, some Authors set a limited range for optimum resolution values by motivating this
Page 26 of 62
25 652
choice by the fact that the greater Rs, the longer analysis time [115,133]. This statement can be in principle
653
shared, but the problem of conflicting responses can be directly taken into account and successfully
654
overcome by multicriteria decision tools. At the same time, the change of internationally accepted target values for some responses such as
656
resolution, should be motivated. For instance, resolution values lower than 1.5 cannot be accepted in the case
657
of simple separation between an analyte and the internal standard [118].
ip t
655
In some reports, different target values for resolution of different peak pairs have been set
659
[56,118,134]; it should be pointed out that when a minimum target for peak resolution is set, then the same
660
target is recommended for each pair of adjacent peaks, whereas other choices should be adequately
661
motivated. The unconcerned maximization of resolution may limit the number of combination of factors that
662
could provide good resolution values for the other peak pairs. This aspect should be especially taken into
663
account in pharmaceutical applications according to Quality by Design (QbD) approach, because finding a
664
single optimum point is no longer sufficient since it is necessary to define a global optimum zone [135].
M
an
us
cr
658
665
7.6.
Selection of the optimal conditions
ed
666
In the overwhelming majority of the considered examples, an optimum corresponding to a single set of
668
experimental conditions was found. However, in the case of pharmaceutical applications, according to the
669
QbD principles [135] it would be more appropriate to find a design space, which corresponds to the
670
multidimensional combination of parameters fulfilling the quality requirements. This was made only in one
671
example referring to the determination of Q10 in a nutraceutical preparation [96].
Ac
ce pt
667
672
One of the major advantages of response surfaces study is that if the model passes the ANOVA, then it
673
can be used to predict the considered responses in any point of the experimental domain, and this is the
674
strategy that should be usually followed. In other words, the experimental plan should not be simply
675
considered as a set of conditions to be chosen from, as sometimes it can be found, unless this is strictly
676
necessary. This procedure lacks of interest as the calculated models in RSM can optimize the values of
677
significant parameters for achieving the best response inside the experimental domain. If unfortunately the
678
model doesn’t pass ANOVA, an acceptable strategy could be to choose the best run of the first design as
Page 27 of 62
26 679
center point of another design [79], or to use desirability function to select another experimental range to be
680
studied by another design [38].
681 682
8.
Conclusion
ip t
683 This critical review pointed out that the use of experimental design is very diffuse for the
685
optimization of electromigration methods. Anyway, a correct use of experimental design is not presented in
686
all of the considered papers. Starting from this consideration, it is important to keep in mind that a deep
687
understanding of the statistical tools involved in the use of experimental design should be always present
688
behind the use of dedicated software. At the same time, planning an effective multivariate strategy requires
689
basic knowledge of the characteristics of the electrophoretic system and of the analytes under study.
690
Experimental design is a methodology for experimental research and a way of reasoning that requires
691
following the simple advices given in the introduction and in the critical discussion in order to obtain high
692
quality of information. It is the researcher’s responsibility to plan and follow a correct strategy for achieving
693
an in-depth comprehension of the problem. In addition, the growing complexity of the data to be handled by
694
the analytical researcher needs a rigorous multivariate approach in order to obtain reliable results. The
695
application of experimental design as analytical tool is strongly recommended, anyway at the same time the
696
updating of the researcher is also strongly encouraged in order to avoid pitfalls and wrong conclusions given
697
to the research community.
ce pt
ed
M
an
us
cr
684
699
Ac
698
References
700 701
[1]
702 703
G. Lewis, D. Mathieu, R. Phan-Tan-Luu, Pharmaceutical Experimental Design, Marcel Dekker, New York, 1999.
[2]
S. Wold, C. Albano, W.J. Dunn III, K. Esbensen, S. Hellberg, E. Johansson, W. Lindberg, M.
704
Sjöström, Modelling data tables by principal components and PLS: class patterns and quantitative
705
predictive relations, Analusis 12 (1984) 477-485.
Page 28 of 62
27 706
[3]
707 708
Handbook of Chemometrics and Qualimetrics: Part A, Elsevier, Amsterdam, 1997. [4]
709 710
D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke,
B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, Amsterdam, 1998.
[5]
M. Forina, R. Leardi, C. Armanino, S. Lanteri, P. Conti, P. Princi, PARVUS: An extendable package of programs for data exploration, classification and correlation, ElsevierScience Software,
712
Amsterdam, 1988, ISBN:0-444-43012-1.
ip t
711
[6]
B.R. Kowalski, Measurement analysis by pattern recognition, Anal. Chem. 47 (1975) 1152A-1162A.
714
[7]
S. Wold, Chemometrics; what do we mean with it and what do we want from it, Chemometr. Intell. Lab. 30 (1995) 109-115.
us
715
cr
713
[8]
http://www.umetrics.com/ Accessed 20 Feb 2013.
717
[9]
http://www.nemrodw.com/ Accessed 20 Feb 2013.
718
[10]
G. Derringer, R. Suich, Simultaneous optimization of several response variables, J. Qual. Technol. 12 (1980) 214-219.
721 722
D. Gétaz, G. Stroehlein, A. Butté, M. Morbidelli, Model-based design of peptide chromatographic
ed
[11]
purification processes, J. Chromatogr. A 1284 (2013) 69-79. [12]
S.L.C. Ferreira, R.E. Bruns, H.S. Ferreira, G.D. Matos, J.M. David, G.C. Brandão, E.G.P. da Silva,
ce pt
720
M
719
an
716
723
L.A. Portugal, P.S. dos Reis, A.S. Souza, W.N.L. dos Santos, Box-Behnken design: An alternative
724
for the optimization of analytical methods, Anal. Chim. Acta 597 (2007) 179-186. [13]
726 727
methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76 (2008) 965-977. [14]
728 729
732
F. Bianchi, M. Careri, Experimental design techniques for optimization of analytical methods. Part I: Separation and sample preparation techniques, Curr. Anal. Chem. 4 (2008) 55-74.
[15]
730 731
M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response surface
Ac
725
B. Dejaegher, Y. Vander Heyden, Experimental designs and their recent advances in set-up, data interpretation, and analytical applications, J. Pharm. Biomed. Anal. 56 (2011) 141-158.
[16]
A.M. Siouffi, R. Phan-Tan-Luu, Optimization methods in chromatography and capillary electrophoresis, J. Chromatogr. A 892 (2000) 75-106.
Page 29 of 62
28 733
[17]
734 735
J.-L. Veuthey, S. Rudaz, Statistical and chemometric tools applied to pharmaceutical analysis, Chimia 59 (2005) 326-330.
[18]
S.L. Costa Ferreira, R.E. Bruns, E.G. Paranhos da Silva, W.N. Lopes dos Santos, C.M Quintella, J.M. David, J. Bittencourt de Andrade, M.C. Breitkreitz, I.C. Sales Fontes Jardim, B.B. Neto,
737
Statistical designs and response surface techniques for the optimization of chromatographic systems,
738
J. Chromatogr. A 1158 (2007) 2-14. [19]
740 [20]
742 743
K.D. Altria, B.J. Clark, S.D. Filbey, M.A. Kelly, D.R. Rudd, Application of chemometric experimental designs in capillary electrophoresis: A review, Electrophoresis 16 (1995) 2143-2148.
[21]
744
S. Sentellas, J. Saurina, Chemometrics in capillary electrophoresis. Part A: Methods for optimization, J. Sep. Sci. 26 (2003) 875-885.
[22]
G. Hanrahan, R. Montes, F.A. Gomez, Chemometric experimental design based optimization
M
745
cr
(2012) 2-13.
us
741
D.B. Hibbert, Experimental design in chromatography: A tutorial review, J. Chromatogr. B 910
an
739
ip t
736
techniques in capillary electrophoresis: A critical review of modern applications, Anal. Bioanal.
747
Chem. 390 (2008) 169-179. [23]
749
optimization of chiral CE or CEC separations: an overview, Method. Mol. Biol. 940 (2013) 409-427. [24]
751 752
ce pt
750
B. Dejaegher, D. Mangelings, Y. Vander Heyden, Experimental design methodologies in the
G.M. Janini, H.J. Issaq, The buffer in capillary zone electrophoresis, in: N.A. Guzman (Ed.), Capillary Electrophoresis Technology, Marcel Dekker Inc., New York, 1993, pp. 119-160.
[25]
S. Terabe, T. Yashima, N. Tanaka, M. Araki, Separation of oxygen isotopic benzoic acids by
Ac
748
ed
746
753
capillary zone electrophoresis based on isotope effects on the dissociation of the carboxyl group,
754
Anal. Chem. 60 (1988) 1673-1677.
755
[26]
L.M. Swann, S.L. Forbes, S.W. Lewis, A capillary electrophoresis method for the determination of
756
selected biogenic amines and amino acids in mammalian decomposition fluid, Talanta 81 (2010)
757
1697-1702.
758 759
[27]
F.A. Simas Vaz, P.A. da Silva, L.Paixão Passos, M. Heller, G.A. Micke, A.C. Oliveira Costa, M.A. Leal de Oliveira, Optimisation of a capillary zone electrophoresis methodology for simultaneous
Page 30 of 62
29 760
analysis of organic aliphatic acids in extracts of Brachiaria brizantha, Phytochem. Anal. 23 (2012)
761
569-575.
762
[28]
M.C. Vargas Mamani, J. Amaya-Farfan, F.G. Reyes Reyes, J.A. Fracassi da Silva, S. Rath, Use of
763
experimental design and effective mobility calculations to develop a method for the determination of
764
antimicrobials by capillary electrophoresis, Talanta 76 (2008) 1006-1014. [29]
K.F. Johns, M.C. Breadmore, R. Bruno, P.R. Haddad, Evaluation of Peakmaster for computer-aided
ip t
765
multivariate optimization of a CE separation of 17 antipsychotic drugs using minimal experimental
767
data, Electrophoresis 30 (2009) 839-847. [30]
X. Li, D. Zhu, T. You, Simultaneous analysis of six cardiovascular drugs by capillary electrophoresis
us
768
cr
766
coupled with electrochemical and electrochemiluminescence detection, using a chemometrical
770
optimization approach, Electrophoresis 32 (2011) 2139-2147. [31]
772 773
D. Zhu, X. Li, J. Sun, T. You, Chemometrics optimization of six antihistamines separations by capillary electrophoresis with electrochemiluminescence detection, Talanta 88 (2012) 265-271.
[32]
M
771
an
769
C.-C. Lu, Y.-J. Jong, J. Ferrance, W.-K. Ko, S.-M. Wu, On-line sample stacking and short-end injection CE for the determination of fluoxetine and norfluoxetine in plasma: Method development
775
and validation using experimental designs, Electrophoresis 28 (2007) 3290-3295. [33]
J. Schappler, A. Staub, J.-L. Veuthey, S. Rudaz, Highly sensitive detection of pharmaceutical
ce pt
776
ed
774
777
compounds in biological fluids using capillary electrophoresis coupled with laser-induced native
778
fluorescence, J. Chromatogr. A 1204 (2008) 183-190. [34]
H. Liu, Y. Wen, Y. Gao, Application of experimental design and radial basis function neural network
Ac
779 780
to the separation and determination of active components in traditional Chinese medicines by
781
capillary electrophoresis, Anal. Chim. Acta 638 (2009) 88-93.
782
[35]
L. Vera-Candioti, A.C. Olivieri, H.C. Goicoechea, Simultaneous multiresponse optimization applied
783
to epinastine determination in human serum by using capillary electrophoresis, Anal. Chim. Acta 595
784
(2007) 310-318.
785 786
[36]
R. Gotti, Capillary electrophoresis of phytochemical substances in herbal drugs and medicinal plants, J. Pharm. Biomed. Anal. 55 (2011) 775-801.
Page 31 of 62
30 787
[37]
C.A. Ballus, A.D. Meinhart, R.E. Bruns, H.T. Godoy, Use of multivariate statistical techniques to
788
optimize the simultaneous separation of 13 phenolic compounds from extra-virgin olive oil by
789
capillary electrophoresis, Talanta 83 (2011) 1181-1187.
790
[38]
S. Furlanetto, S. Orlandini, I. Giannini, G. Beretta, S. Pinzauti, Pitfalls and success of experimental design in the development of a mixed MEKC method for the analysis of budesonide and its
792
impurities, 30 (2009) 633-643.
793
[39]
ip t
791
S. Orlandini, I. Giannini, S. Pinzauti, S Furlanetto, Multivariate optimisation and validation of a capillary electrophoresis method for the analysis of resveratrol in a nutraceutical, Talanta 74 (2008)
795
570-577. [40]
us
796
cr
794
R.G. Peres, G.A. Micke, M.F.M. Tavares, D.B. Rodriguez-Amaya, Multivariant optimization, validation, and application of capillary electrophoresis for simultaneous determination of
798
polyphenols and phenolic acids in Brazilian wines, J. Sep. Sci. 32 (2009) 3822-3828. [41]
S. Furlanetto, S. Lanteri, S. Orlandini, R. Gotti, I. Giannini, S. Pinzauti, Selection of background
M
799
an
797
electrolyte for CZE analysis by a chemometric approach. Part I. Separation of a mixture of acidic
801
non-steroidal anti-inflammatory drugs, J. Pharm. Biomed. Anal. 43 (2007) 1388-1401.
802
[42]
ed
800
S. Furlanetto, S. Lanteri, S. Orlandini, R. Gotti, I. Giannini, S. Pinzauti, Selection of background electrolyte for CZE analysis by a chemometric approach. Part II. Separation of a mixture of basic
804
beta-blocker drugs, J. Pharm. Biomed. Anal. 43 (2007) 1402-1408.
805
[43]
ce pt
803
P. Schmitt-Kopplin, N. Hertkorn, A.W. Garrison, D. Freitag, A. Kettup, Influence of borate buffers on the electrophoretic behavior of humic substances in capillary zone electrophoresis, Anal. Chem.
807
70 (1998) 3798-3808.
808
[44]
Ac
806
I. Oita, H. Halewyck, S. Pieters, B. Dejaegher, B. Thys, B. Rombaut, Y. Vander Heyden, Rational
809
use of stacking principles for signal enhancement in capillary electrophoretic separations of
810
poliovirus samples, J. Pharm. Biomed. Anal. 55 (2011) 135-145.
811
[45]
812 813 814
E. Kenndler, Organic solvents in capillary electrophoresis, in: N.A. Guzman (Ed.), Capillary Electrophoresis Technology, Marcel Dekker Inc., New York, 1993, pp. 161-186.
[46]
C. Schwer, E. Kenndler, Electrophoresis in fused-silica Capillaries: the influence of organic solvents on the electroosmotic velocity and the zeta potential, Anal. Chem. 63 (1991) 1801-1807.
Page 32 of 62
31 815
[47]
F. Ronda, J.M. Rodríguez-Nogales, D. Sancho, B.O.yM. Gómez, Multivariate optimisation of a
816
capillary electrophoretic method for the separation of glutenins. Application to quantitative analysis
817
of the endosperm storage proteins in wheat, Food Chem. 108 (2008) 287-296.
818
[48]
819
T. Cserháti, New applications of cyclodextrins in electrically driven chromatographic systems: a review, Biomed. Chromatogr. 22 (2008) 563–571.
[49]
S. Fanali, Chiral separations by CE employing CDs, Electrophoresis 30 (2009) S203–S210.
821
[50]
G. Gübitz, M.G. Schmid, Chiral separation by capillary electromigration techniques, J. Chromatogr.
822 [51]
cr
A 1204 (2008) 140–156.
A. Rousseau, P. Chiap, R. Oprean, J. Crommen, M. Fillet, A.-C. Servais, Effect of the nature of the
us
823
ip t
820
single-isomer anionic CD and the BGE composition on the enantiomeric separation of β-blockers in
825
NACE, Electrophoresis 30 (2009) 2862-2868.
826
[52]
an
824
G. Neumajer, T. Sohajda, A. Darcsi, G. Tóth, L. Szente, B. Noszál, S. Béni, Chiral recognition of dapoxetine enantiomers with methylated-gamma-cyclodextrin: A validated capillary electrophoresis
828
method, J. Pharm. Biomed. Anal. 62 (2012) 42-47. [53]
M. Liu, Y. Zheng, Y. Ji, C. Zhang, Development and validation of a capillary electrophoresis method
ed
829
M
827
for the enantiomeric purity determination of RS86017 using experimental design, J. Pharm. Biomed.
831
Anal. 55 (2011) 93-100.
832
[54]
ce pt
830
A.S. Fayed, S.A. Weshahy, M.A. Shehata, N.Y. Hassan, J. Pauwels, J. Hoogmartens, A. Van Schepdael, Separation and determination of clopidogrel and its impurities by capillary
834
electrophoresis, J. Pharm. Biomed. Anal. 49 (2009) 193-200.
835
[55]
Ac
833
Y.P. Zhang, Y. Jun Zhang, W. Jun Gong, S. Ming Wang, H. Yong Xue, K. Pill Lee, Design of
836
experiments for capillary electrophoretic enantioresolution of tamsulosin using sulfated-β-
837
cyclodextrin as chiral selector, J. Liq. Chromatogr. R. T. 30 (2007) 215-234.
838
[56]
K.B. Borges, M.T. Pupo, L.A.P. De Freitas, P.S. Bonato, Box-Behnken design for the optimization
839
of an enantioselective method for the simultaneous analysis of propranolol and 4-
840
hydroxypropranolol by CE, Electrophoresis 30 (2009) 2874-2881.
Page 33 of 62
32 841
[57]
N. Mofaddel, H. Krajian, D. Villemin, P.L. Desbène, Enantioseparation of binaphthol and its mono
842
derivatives by cyclodextrin-modified capillary zone electrophoresis, J. Chromatogr. A 1211 (2008)
843
142-150.
844
[58]
S. Orlandini, I. Giannini, R. Gotti, S. Pinzauti, E. La Porta, S. Furlanetto, Development of a CZE method for the determination of mizolastine and its impurities in pharmaceutical preparations using
846
response surface methodology, Electrophoresis 28 (2007) 395-405. [59]
P.-Y. Liu, Y.-H. Lin, C.H. Feng, Y.-L. Chen, Determination of hydroxy acids in cosmetics by
848
chemometric
849
Electrophoresis 33 (2012) 3079-3086. [60]
design
and
cyclodextrin-modified
capillary
electrophoresis,
us
850
experimental
cr
847
ip t
845
M.A. Martínez-Gómez, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Enantioseparation of phenotiazines by affinity electrokinetic chromatography using human serum
852
albumin as chiral selector - Application to enantiomeric quality control in pharmaceutical
853
formulations, Anal. Chim. Acta 582 (2007) 223-228. [61]
M
854
an
851
M.A. Martínez-Gómez, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Enantiomeric quality control of antihistamines in pharmaceuticals by affinity electrokinetic
856
chromatography with human serum albumin as chiral selector, Anal. Chim. Acta 592 (2007) 202-
857
209. [62]
ce pt
858
ed
855
M.A. Martínez-Gómez, L. Escuder-Gilabert, R.M. Villanueva-Camañas, S. Sagrado, M.J. MedinaHernández, Enantioseparation of nuarimol by affinity electrokinetic chromatography-partial filling
860
technique using human serum albumin as chiral selector, J. Sep. Sci. 31 (2008) 3265-3271.
861
[63]
862
Ac
859
L. Geiser, J.-L. Veuthey, Non-aqueous capillary electrophoresis 2005-2008, Electrophoresis 30 (2009) 36-49.
863
[64]
E. Kenndler, Organic solvents in CE, Electrophoresis 30 (2009) S101-S111.
864
[65]
M.L. Riekkola, M. Jussila, S.P. Porras, I.E. Valkó, Non-aqueous capillary electrophoresis, J.
865 866
Chromatogr. A 892 (2000) 155-170. [66]
X. Li, X. Xu, D.R. Albano, T. You, Optimization using central composite design for antihistamines
867
separation
868
electrochemiluminescence detections, Analyst 136 (2011) 5294-5301.
by
nonaqueous
capillary
electrophoresis
with
electrochemical
and
Page 34 of 62
33 869
[67]
870 871
F. Wang, M.G. Khaledi, Chiral separations by nonaqueous capillary electrophoresis, Anal. Chem. 68 (1996) 3460-3467.
[68]
A. Rousseau, F. Gillotin, P. Chiap, E. Bodoki, J. Crommen, M. Fillet, A.-C. Servais, Generic
872
systems for the enantioseparation of basic drugs in NACE using single-isomer anionic CDs, J.
873
Pharm. Biomed. Anal. 54 (2011) 154-159.
875
Chem. Rec. 8 (2008) 291-301.
877
Sons Ltd, Chichester, England, 2006. [71]
879 880
M. Silva, Micellar electrokinetic chromatography: A review of methodological and instrumental innovations focusing on practical aspects, Electrophoresis, 34 (2013) 141-158.
[72]
881 882
us
878
U. Pyell, Electrokinetic chromatography. Theory, instrumentation and applications, John Wiley &
cr
[70]
an
876
S. Terabe, Micellar electrokinetic chromatography for high-performance analytical separation,
ip t
[69]
R. Ryan, S. Donegan, J. Power, K. Altria, Advances in the theory and application of MEEKC, Electrophoresis 31 (2010) 755-767.
[73]
M
874
R. Ryan, K. Altria, E. McEvoy, S. Donegan, J. Power, A review of developments in the methodology and application of microemulsion electrokinetic chromatography, Electrophoresis 34 (2013) 159–
884
177. [74]
S. Yang, M.G. Khaledi, Chemical selectivity in micellar electrokinetic chromatography:
ce pt
885
ed
883
886
characterization of solute-micelle interactions for classification of surfactants, Anal. Chem. 67
887
(1995) 499-510. [75]
T. Galeano-Díaz, M.I. Acedo-Valenzuela, A. Silva-Rodríguez, Determination of tocopherols in
Ac
888 889
vegetable oil samples by non-aqueous capillary electrophoresis (NACE) with fluorimetric detection,
890
J. Food Compos. Anal. 25 (2012) 24-30.
891
[76]
J. He, S.A. Shamsi, Multivariate approach for the enantioselective analysis in micellar electrokinetic
892
chromatography-mass spectrometry I. Simultaneous optimization of binaphthyl derivatives in
893
negative ion mode, J. Chromatogr. A 1216 (2009) 845-856.
894 895
[77]
J. He, S.A. Shamsi, Multivariate approach for the enantioselective analysis in MEKC-MS: II. Optimization of 1,1'-binaphthyl-2,2 '-diamine in positive ion mode, J. Sep. Sci. 32 (2009) 1916-1926.
Page 35 of 62
34 896
[78]
B. Wang, J. He, S.A. Shamsi, A high-throughput multivariate optimization for the simultaneous
897
enantioseparation and detection of barbiturates in micellar electrokinetic chromatography-mass
898
spectrometry, J. Chromatogr. Sci. 48 (2010) 572-583.
899
[79]
S. Orlandini, R. Gotti, I. Giannini, B. Pasquini, S. Furlanetto, Development of a capillary electrophoresis method for the assay of ramipril and its impurities: An issue of cis-trans
901
isomerization, J. Chromatogr. A 1218 (2011) 2611-2617.
902
[80]
ip t
900
K. Yu, Y. Gong, Z. Lin, Y. Cheng, Quantitative analysis and chromatographic fingerprinting for the quality evaluation of Scutellaria baicalensis Georgi using capillary electrophoresis, J. Pharm.
904
Biomed. Anal. 43 (2007) 540-548. [81]
us
905
cr
903
J. Felhofer, G. Hanrahan, C.D. García, Univariate and multivariate optimization of the separation conditions for the analysis of five bisphenols by micellar electrokinetic chromatography, Talanta, 77
907
(2009) 1172–1178. [82]
X.-M. Fan, Y.-B. Ji, D.-N. Zhu, An integrated approach based on experimental designs for
M
908
an
906
fingerprint development of the complex herbal prescription Sheng-Mai-San by MEKC,
910
Chromatographia 71 (2010) 667-677.
911
[83]
ed
909
G. Mu, F. Luan, H. Liu, Y. Gao, Use of experimental design and artificial neural network in optimization of capillary electrophoresis for the determination of nicotinic acid and nicotinamide in
913
food compared with high-performance liquid chromatography, Food Anal. Methods 6 (2013) 191-
914
200. [84]
916 917
MEKC: Separation optimization using experimental design, J. Sep. Sci. 31 (2008) 3740-3748. [85]
918 919
V.J. Drover, C.S. Bottaro, Determination of pharmaceuticals in drinking water by CD-modified
Ac
915
ce pt
912
J.-F. Chiang, Y.-T. Hsiao, W.-K. Ko, S.-M. Wu, Analysis of multiple abused drugs and hypnotics in urine by sweeping CE, Electrophoresis 30 (2009) 2583-2589.
[86]
Y.-C. Cheng, C-C. Wang, Y.-L. Chen, S.-M. Wu, Large volume sample stacking with EOF and
920
sweeping in CE for determination of common preservatives in cosmetic products by chemometric
921
experimental design, Electrophoresis 33 (2012) 1443-1448.
Page 36 of 62
35 922
[87]
P. Anres, N. Delaunay, J. Vial, P. Gareil, A chemometric approach for the elucidation of the
923
parameter impact in the hyphenation of field-enhanced sample injection and sweeping in capillary
924
electrophoresis, Electrophoresis 33 (2012) 1169-1181. [88]
926 927
H. Nishi, T. Fukuyama, S. Terabe, Chiral separation by cyclodextrin-modified micellar electrokinetic chromatography, J. Chromatogr. 553 (1991) 503-516.
[89]
S. Terabe, Y. Miyashita, Y. Ishihama, O. Shibata, Cyclodextrin-modified micellar electrokinetic
ip t
925
chromatography: separation of hydrophobic and enantiomeric compounds, J. Chromatogr. 636
929
(1993) 47-55. [90]
C.L. Copper, M.J. Sepaniak, Cyclodextrin-modified electrokinetic capillary chromatography
us
930
cr
928
separations of benzopyrene isomers: correlation with computationally derived host-guest energies,
932
Anal. Chem. 66 (1994) 147-154.
933
[91]
an
931
E. Mileo, P. Franchi, R. Gotti, C. Bendazzoli, E. Mezzina, M. Lucarini, An EPR method for measuring the rate of distribution of organic substrate between cyclodextrin, micelles and water,
935
Chem. Comm. 11 (2008) 311-1313. [92]
C. Bendazzoli, E. Mileo, M. Lucarini, S. Olmo, V. Cavrini, R. Gotti, Capillary electrophoretic study
ed
936
M
934
on the interaction between sodium dodecyl sulfate and neutral cyclodextrins. Microchim. Acta 171
938
(2010) 23-31.
939
[93]
ce pt
937
H. Abromeit, A.M. Schaible, O. Werz, G.K.E. Scriba, Chemometrics-guided development of a cyclodextrin-modified micellar electrokinetic chromatography method with head-column field
941
amplified sample stacking for the analysis of 5-lipoxygenase metabolites, J. Chromatogr. A 1267
942
(2012) 217-223.
943
[94]
Ac
940
S. Orlandini, I. Giannini, M.V. Navarro, S. Pinzauti, S. Furlanetto, Dual CD system-modified
944
MEEKC method for the determination of clemastine and its impurities, Electrophoresis 31 (2010)
945
3296-3304.
946 947
[95]
I. Giannini, S. Orlandini, R. Gotti, S. Pinzauti, S. Furlanetto, Cyclodextrin-MEEKC for the analysis of oxybutynin and its impurities, Talanta 80 (2009) 781-788.
Page 37 of 62
36 948
[96]
G. Piepel, B. Pasquini, S. Cooley, A. Heredia-Langner, S. Orlandini, S. Furlanetto, Mixture-process
949
variable approach to optimize a microemulsion electrokinetic chromatography method for the quality
950
control of a nutraceutical based on coenzyme Q10, Talanta 97 (2012) 73-82. [97]
952
electrophoresis. Fundamentals and applications, J. Chromatogr. A 1184 (2008) 504-541.
954
electrokinetic chromatography, Science 282 (1998) 465-468. [99]
956 957
(1999) 1638-1644. [100]
958 959
J.P. Quirino, S. Terabe, Sweeping of analyte zone in electrokinetic chromatography, Anal. Chem. 71
cr
955
J.P. Quirino, S. Terabe, Exceeding 5000-fold concentration of dilute analytes in micellar
ip t
[98]
us
953
S.L. Jr. Simpson, J.P. Quirino, S. Terabe, On-line sample preconcentration in capillary
K.D. Bartle, P. Myers, Capillary Electrochromatography, Royal Society of Chemistry, Cambridge, 2001.
[101]
an
951
X. Wu, D. Mangelings, I. Oita, I. Tanret, C. Yan, Y. Vander Heyden, Capillary electrochromatographic testing of monolithic silica columns synthesized according to an
961
experimental design approach, J. Sep. Sci. 34 (2011) 2305-2313. [102]
I. Tanret, D. Mangelings, Y. Vander Heyden, Pressure-assisted CEC versus CEC using methacrylate-
ed
962
M
960
based monolithic columns: Influence of the polymerization-mixture composition, Electrophoresis 29
964
(2008) 4463-4474.
965
[103]
ce pt
963
W. Franklin Smyth, V. Rodriguez, Recent studies of the electrospray ionisation behaviour of selected
966
drugs
967
chromatography-mass spectrometry, J Chromatogr. A 1159 (2007) 159-174. [104]
969 970
application in capillary electrophoresis-mass
spectrometry and
liquid
Ac
968
and their
G.K. Scriba, Nonaqueous capillary electrophoresis-mass spectrometry, J. Chromatogr. A 1159 (2007) 28-41.
[105]
G.B. Martin, F. Mansion, A.-C. Servais, B. Debrus, E. Rozet, Ph. Hubert, J. Crommen, M. Fillet,
971
CE-MS method development for peptides analysis, especially hepcidin, an iron metabolism marker,
972
Electrophoresis 30 (2009) 2624-2631.
973 974
[106]
S.A. Shamsi, Micellar electrokinetic chromatography-mass spectrometry using a polymerized chiral surfactant, Anal. Chem. 73 (2001) 5103-5108.
Page 38 of 62
37 975
[107]
A. Alnajjar, H.H. AbuSeada, A.M. Idris, Capillary electrophoresis for the determination of
976
norfloxacin and tinidazole in pharmaceuticals with multi-response optimization, Talanta 72 (2007)
977
842-846.
978
[108]
S. Furlanetto, S. Orlandini, A.M. Marras, P. Mura, S. Pinzauti, Mixture design in the optimization of a microemulsion system for the electrokinetic chromatographic determination of ketorolac and its
980
impurities: Method development and validation, Electrophoresis 27 (2006) 805-818.
981
[109]
ip t
979
A.D. Meinhart, C.S. Bizzotto, C.A. Ballus, M.A. Prado, R.E. Bruns, J.T. Filho, H.T. Godoy, Optimisation of a CE method for caffeine analysis in decaffeinated coffee, Food Chem. 120 (2010)
983
1155-1161. [110]
us
984
cr
982
S. Furlanetto, S. Pinzauti, E. La Porta, A. Chiarugi, P. Mura, S. Orlandini, Development and validation of a differential pulse polarographic method for quinolinic acid determination in human
986
plasma and urine after solid-phase extraction: a chemometric approach, J. Pharm. Biomed. Anal. 17
987
(1998) 1015-1028. [111]
M
988
an
985
G. Pieraccini, S. Furlanetto, S. Orlandini, G. Bartolucci, I. Giannini, S. Pinzauti, G. Moneti, Identification and determination of mainstream and sidestream smoke components in different
990
brands and types of cigarettes by means of solid-phase microextraction-gas chromatography-mass
991
spectrometry, J. Chromatogr. A 1180 (2008) 138-150. [112]
ce pt
992
ed
989
P. Han, F. Luan, X. Yan, H. Liu, Separation and determination of honokiol and magnolol in chinese traditional medicines by capillary electrophoresis with the application of response surface
994
methodology and radial basis function neural network, J. Chromatogr. Sci. 50 (2012) 71-75.
995
[113]
Ac
993
A.O. Alnajjar, A.M. Idris, M.V. Attimarad, A.M. Aldughaish, R.E.E. Elgorashe, Capillary
996
electrophoresis assay method for metoprolol and hydrochlorothiazide in their combined dosage form
997
with multivariate optimization, J. Chromatogr. Sci. 51 (2013) 92-97.
998
[114]
999
electrophoresis separation of leucine enkephalin and its immune complex, J. Sep. Sci. 30 (2007)
1000 1001 1002
S.M.E. Babar, E.J. Song, M.N. Hasan, Y.S. Yoo, Experimental design optimization of the capillary
2311-2319. [115]
L. Song, J. Bai, W. Zhou, Determination of betamethasone and dexamethasone in human urine and serum by MEKC after an experimental design, Chromatographia 68 (2008) 287-293.
Page 39 of 62
38 1003
[116]
S.E. Vignaduzzo, L. Vera-Candioti, P.M. Castellano, H.C. Goicoechea, T.S. Kaufman, Multivariate
1004
optimization and validation of a CZE method for the analysis of pridinol mesylate and meloxicam in
1005
tablets, Chromatographia 74 (2011) 609-617. [117]
1007 1008
P. Mukerjee, K.J. Mysels, Critical micelle concentration of aqueous surfactant system, National Bureau of Standards, 1971, Washington.
[118]
M.M. Hefnawy, M.A. Sultan, H.I. Al-Johar, H.Y. Aboul-Enein, Multi-objective optimization
ip t
1006
strategy based on desirability functions used for electrophoratic separation and quantification of
1010
rosiglitazone and glimepiride in plasma and formulations, Drug Test. Anal. 4 (2012) 39-47.
cr
1009
[119]
V.M. Morris, J.G. Hughes, P.J. Marriott, J. Chromatogr. A 755 (1996) 235-243.
1012
[120]
J.C. Berridge, Unattended optimisation of reversed-phase high-performance liquid chromatographic
1013 [121]
1015 1016
an
separations using the modified simplex algorithm, J. Chromatogr. A 244 (1982) 1-14. B. Divjak, M. Moder, J. Zupan, Chemometrics approach to the optimization of ion chromatographic analysis of transition metal cations for routine work, Anal. Chim. Acta 358 (1998) 305-315. [122]
M
1014
us
1011
J. Lopez-Gazpio, R. Garcia-Arrona, M. Ostra, E. Millan, Optimization and validation of a nonaqueous micellar electrokinetic chromatography method for determination of polycyclic musks in
1018
perfumes, J. Sep. Sci. 35 (2012) 1344-1350. [123]
1020 1021
T.D. Schlabach, J.L. Excoffier, Multi-variate ranking function for optimizing separations, J.
ce pt
1019
ed
1017
Chromatogr. A 439 (1988) 173-184. [124]
V.M. Morris, C. Hargreaves, K. Overall, P.J. Mariott, J.G. Hughes, Optimization of the capillary electrophoresis separation of ranitidine and related compounds, J. Chromatogr. A 766 (1997) 245-
1023
254.
1024
[125]
Ac
1022
X. Liu, X. Chen, Application of experimental design in optimization of the separation condition for
1025
determination of four active components in cold medicines by flow injection-capillary
1026
electrophoresis, J. Chromatogr. Sci. 49 (2011) 142-147.
1027
[126]
A. Alnajjar, A.M. Idris, A.M., H.H. AbuSeada, Development of a stability-indicating capillary
1028
electrophoresis method for norfloxacin and its inactive decarboxylated degradant, Microchem. J. 87
1029
(2007) 35-40.
Page 40 of 62
39 1030
[127]
1031 1032
B.Y. Erdoğan, A.N. Onar, Determination of nitrates, nitrites and oxalates in kale and sultana pea by capillary electrophoresis, J. Anim. Vet. Adv. 10 (2011) 2051-2057.
[128]
M.I. Bailón Pérez, L.Cuadros Rodríguez, C. Cruces-Blanco, Analysis of different β-lactams
1033
antibiotics in pharmaceutical preparations using micellar electrokinetic capillary chromatography, J.
1034
Pharm. Biomed. Anal. 43 (2007) 746-752. [129]
Z. Vujic, V. Kuntic, B. Ivkovic, Statistical optimization applied to simultaneous determination of
ip t
1035
maprotiline, desipramine, and moclobemide by capillary zone electrophoresis, Monats. Chem. 139
1037
(2008) 81-87. [130]
I.M. Palabiyik, M.G. Caglayan, F. Onur, Multivariate optimization and validation of a CE method
us
1038
cr
1036
for simultaneous analysis of dorzolamide hydrochloride and timolol maleate in ophthalmic solution,
1040
Chromatographia 73 (2011) 541-548. [131]
Principles and applications, 3rd Ed., Umetrics Academy Umea, Sweden, 1998.
1042 1043
L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikstrom, S. Wols, Design of experiments:
[132]
M
1041
an
1039
M.A. Al-Ghobashy, M.A.K. Williams, D.R.K. Harding, Factors affecting the performance of capillary isoelectric focusing in dynamically coated capillaries using polyethylene oxide polymer,
1045
Anal. Lett. 41 (2008) 1914-1931. [133]
Y.-H- Lin, Y.-H. Yang, S.-M. Wu, Experimental design and capillary electrophoresis for
ce pt
1046
ed
1044
1047
simultaneous analysis of arbutin, kojic acid and hydroquinone in cosmetics, J. Pharm. Biomed. Anal.
1048
44 (2007) 279-282. [134]
A.D. Meinhart, C.A. Ballus, R.E. Bruns, J.A.L. Pallone, H.T. Godoy, Chemometrics optimization of
Ac
1049 1050
carbohydrate separations in six food matrices by micellar electrokinetic chromatography with
1051
anionic surfactant, Talanta 85 (2011) 237-244.
1052
[135]
1053 1054
S. Orlandini, S. Pinzauti, S. Furlanetto, Application of quality by design to the development of analytical separation methods, Anal. Bioanal. Chem. 405 (2013) 443-450.
[136]
L. Vera-Candioti, A.C. Olivieri, H.C. Goicoechea, Development of a novel strategy for
1055
preconcentration of antibiotic residues in milk and their quantitation by capillary electrophoresis,
1056
Talanta 82 (2010) 213-221.
Page 41 of 62
40 1057
[137]
M. Lecoeur, J.F. Goossens, C. Vaccher, J.P. Bonte, C. Foulon, A multivariate approach for the
1058
determination of isoelectric point of human carbonic anhydrase isoforms by capillary isoelectric
1059
focusing, Electrophoresis 32 (2011) 2857-2866.
1060
[138]
F.T. Dahdouh, K. Clarke, M. Salgado, G. Hanrahan, F.A. Gomez, Chemometrical examination of active parameters and interactions in flow injection-capillary electrophoresis, Electrophoresis 29
1062
(2008) 3779-3785.
ip t
1061
[139] R.E. Montes, G. Hanrahan, F.A. Gomez, Use of chemometric methodology in optimizing conditions
1064
for competitive binding partial filling affinity capillary electrophoresis, Electrophoresis 29 (2008)
1065
3325-3332. [140]
us
1066
cr
1063
R.E. Montes, F.A. Gomez, G. Hanrahan, Response surface examination of the relationship between experimental conditions and product distribution in electrophoretically mediated microanalysis,
1068
Electrophoresis 29 (2008) 375-380.
an
1067
[141] D. Hevia, C. Botas, R.M. Sainz, I. Quiros, D. Blanco, D.X. Tan, C. Gomez-Cordoves, J.C. Mayo,
1070
Development and validation of new methods for the determination of melatonin and its oxidative
1071
metabolites by high performance liquid chromatography and capillary electrophoresis, using
1072
multivariate optimization, J. Chromatogr. A 1217 (2010) 1368-1374.
ed
M
1069
1075 1076 1077 1078 1079
Ac
1074
ce pt
1073
1080
Page 42 of 62
41
1081
Figure legends
1082 1083
Fig. 1.
1084
mg mL-1 RM and (b) 0.04 mg mL-1 RM and its impurities (ramipril methyl ester, IA; ramipril isopropyl ester,
1085
IB;
1086
phenylpropyl]amino]-propanoic
1087
hydroxydiketopiperazine, IL; (2R,3R)-2,3-bis(benzoyloxy)succinic acid, IM). MEKC conditions: BGE, 20
1088
mM borate pH 9.50, 30 mM SDS; detection wavelength, 210 nm. (a) Capillary length, 64.5 cm; voltage, 30
1089
kV; temperature, 30 °C. RM migrated as a pair of peaks corresponding to the cis-trans isomers, connected by
1090
a broadened migration zone generated by the mixture of the isomeric forms. (b) Capillary length, 48.5 cm;
1091
voltage, 25 kV; temperature, 20 °C. The separation of the two isomers of RM and of the three proline-
1092
containing impurities (IA, IB, IC) is shown. Modified from Ref. [79].
Optimization of the separation of ramipril (RM) and related impurities. Electropherogram of (a) 2
ramipril acid],
diketopiperazine, IF;
ramipril
ID;
(S)-2-[[(S)-1-(ethoxycarbonyl)-3-
diketopiperazine
ip t
IC;
acid,
IK;
ramipril
M
an
us
cr
hexahydroramipril,
1093
Fig. 2. Desirability function (D) graphs in the optimization of the separation of ramipril and related
1095
impurities, obtained by plotting: (a) phosphate concentration (BGE conc.) vs. voltage (V); (b) phosphate
1096
concentration (BGE conc.) vs. temperature (T); (c) voltage (V) vs. temperature (T). Partial desirability
1097
functions were defined for critical resolution values and analysis time. Modified from Ref. [79].
1098
ce pt
ed
1094
1099
Fig. 3.
1100
(RM) and related impurities, obtained by mixture design and response surface methodology with the aid of
1101
desirability function. RM, 4 mg mL-1; RM impurities, 0.04 mg mL-1. Internal standard: flufenamic acid (FL)
1102
0.1 mg mL-1. BGE, microemulsion made by 88.95% 90 mM phosphate pH 2.5, 1.05% n-heptane, 10.00%
1103
SDS/n-butanol in 1:2 ratio; capillary length, 64.5 cm; detection wavelength 210 nm; voltage, -26 kV (reverse
1104
polarity); temperature, 17 °C. Symbols as in Fig. 1. Modified from Ref. [79].
Ac
Electropherogram referring to the optimal RF-MEEKC conditions for the separation of ramipril
1105 1106
Fig. 4.
1107
related substances indicated herein as I1, I2, I3, I4, I5 (for structures and names please see Ref. [95]). W,
Contour plots obtained by mixture design referring to the analysis of oxybutynin (OXY) and
Page 43 of 62
42 aqueous phase (10 mM sodium borate); O, oil phase (n-heptane); S/CoS, surfactant/cosurfactant (SDS/n-
1109
butanol in 1:2 ratio). (a) response I3/I4 resolution (R5); (b) response I4/OXY resolution (R6); (c) response
1110
OXY/I5 resolution (R7); (d) response analysis time (t). Voltage, 27 kV; (2-hydroxypropyl)-β-cyclodextrin 15
1111
mM. The considered resolution values R5, R6 and R7 were those found critical for the separation. The
1112
isoresponse lines correspond to the same predicted values of the considered response. Modified from Ref.
1113
[95].
ip t
1108
1114
Fig. 5. Response surfaces in the optimization of the separation of oxybutynin (OXY) and related impurities,
1116
obtained by plotting (2-hydroxypropyl)-β-cyclodextrin concentration (CD conc.) vs. voltage (V): (a) response
1117
I3/I4 resolution (R5); (b) response I4/OXY resolution (R6); (c) response OXY/I5 resolution (R7); (d) response
1118
analysis time (t). For structure and names of the impurities (Ii), please see the Ref. [95]. Modified from Ref.
1119
[95].
an
us
cr
1115
M
1120
Fig. 6. Perturbation plots obtained by the MPV approach for optimizing the response Q10 efficiency. The
1122
effect of varying each PV from its lower bound (-1.0) to its upper bound (1.0) with the other PVs at their
1123
middle values (0.0) for each of the four vertices of the constrained mixture region is shown. B, buffer
1124
(acetate buffer); S, surfactant/cosurfactant (SDS/n-butanol in 1:4.5 ratio); O, oil (n-octane). V, Applied
1125
voltage; BC, Buffer concentration; pH, Buffer pH. The different effects of the PVs for the different
1126
microemulsion formulations are due to the interaction effects of MCs and PVs on the response. Modified
1127
from Ref. [96].
1129
ce pt
Ac
1128
ed
1121
1130 1131
Page 44 of 62
ip t
Table(s)
cr
Table 1
us
Applications of multivariate strategies to the optimization of CZE and related techniques (Affinity Capillary Electrophoresis, ACE; Nonaqueous Capillary Electrophoresis, NACE; Flow-Injection CE, FI-CE; Capillary Isoelectric Focusing, CIEF; Capillary Electrochromatography, CEC;
an
pressure-assisted CEC, p-CEC; Electrophoretically Mediated MicroAnalysis, EMMA; CE-MS, Capillary Electrophoresis-Mass Spectrometry)
Type of sample Magnolia Officinalis (TCM)
Resveratrol, catechin, rutin, syringic acid, kaempferol, coumaric acid, myricetin, quercetin, caffeic acid, gallic acid
Wines
Resveratrol
Nutraceuticals
Nitrites, oxalate
nitrates
and
ep te
Ac c
Acetic, citric, formic, lactic, malic, oxalic, pyruvic, succinic, tartaric, aspartic acid
Brachiaria Brizantha extracts
Vegetables
Phenolic compounds
Extra-virgin olive oil
Lactic,
Cosmetics
malic,
tartaric,
Factors Borate concentration; MeOH %; Voltage Borate concentration; SDS concentration; Brij 35 concentration; MeOH %; Injection time; Voltage; Temperature Borate concentration; ACN %; Voltage Buffer pH; Phthalic acid concentration
d
Analytes Honokiol and magnolol
M
methods.
CTAB concentration; Voltage; Temperature Buffer pH; Temperature; Voltage Boric acid concentration; Buffer pH; Voltage Phosphate concentration;
Responses Rs/analysis time ratio
Type of design BBD
Ref [112]
CRS (Rs and analysis time)
fFD
[40]
Rs; Analysis time
CCD
[39]
S/N; Separation; Analysis time; Repeatability (data not shown)
FFD
[27]
Corrected peak area
FFD
[127]
Rs; Analysis time
CCD
[37]
CRS (Rs and analysis time)
fFD; CCD
[59]
FFD
Page 45 of 62
Antimicrobials (chloramphenicol, danofloxacin, ciprofloxacin, enrofloxacin, sulfamethazine, sulfaquinoxaline, sulfamethoxazole)
Pharmaceuticals
Rosiglitazone, glimepiride
Pharmaceuticals and spiked plasma samples Test mixture
ip t cr Rs; Analysis time; Electric current
[136]
Peak number
CCD
[28]
Phosphate concentration; Voltage; Temperature I) BGE parameters, i.e.,: Buffer pH; Conductivity; Viscosity; Ionic strength II) Borate concentration; MeOH %; Voltage; Temperature Phosphate concentration; Buffer pH
Rs; Analysis time; Electric current I) PCA
CCD
[118]
II) DD
[41]
Rs; Peak area; Migration time
FFD
[113]
Phosphate concentration; ACN %; pH; Voltage Phosphate/TEAOH concentration; TM--CD concentration; Temperature; Voltage
Rs
OD and UD
[34]
Rs
DD
[58]
d
ep te
and
Aconitine, hypaconitine
Mizolastine and related substances
PBD; CCD
seven
Ac c
Arylpropionic drugs
Metoprolol hydrochlorothiazide
us
Milk
M
Antibiotic residues
Buffer pH; CTAB concentration; MeOH %; -CD concentration Borate concentration; Buffer pH; Voltage; Temperature Temperature; Voltage
an
glycolic, citric, mandelic and salicylic acids
Pharmaceuticals
TCM
Pharmaceuticals
II) Rs and analysis time
Page 46 of 62
Real plasma sample from a forensic case
Biogenic amines
Decomposition fluids from porcine tissues
Fluoxetine, norfluoxetine
Real samples
Anthistaminic drugs (carbinoxamine, chlorpheniramine, cyproheptadine, diphenhydramine, doxylamine, ephedrine) Beta-blocker drugs
Antipsychotic drugs
plasma
Spiked samples
urine
Test samples
Test samples
ip t
Propranolol
[116]
CCD
[130]
Rs; Analysis time
PBD; CCD
[35]
Signal (S); Noise (N); S/N; Efficiency CEF (Rs and analysis time)
Sequential BBDs
[33]
FFD; CCD
[26]
Product of resolution and peak height
fFD
[32]
cr
Human serum (spiked samples)
FFD; CCD
us
Epinastine
Rs of adjacent peaks; Analysis time; Electric current Peak width Effective mobility
an
Pharmaceuticals
M
Dorzolamide and timolol maleate
Phosphate concentration; Buffer pH; Temperature; Voltage Phosphate pH; Temperature; Voltage Phosphate concentration; Buffer pH; Injection voltage; Injection time; Separation voltage TRIS-phosphate ionic strength; Buffer pH; Temperature; Voltage Boric acid concentration; Buffer pH; MeOH %; Voltage Phosphate concentration; TEAOH concentration; MeOH %; Water plug; Injection voltage; Separation voltage TRIS-phosphate concentration; Buffer pH; Voltage
d
Pharmaceuticals
ep te
and
Ac c
Pridinol mesylate meloxicam
I) BGE parameters, i.e.,: Buffer pH; Conductivity; Viscosity; Ionic strength II) Voltage; Temperature Phosphate ionic strength;
Rs CCD CRS (Rs and analysis time)
CCD
[31]
I) PCA
DD
[42]
ASM
[29]
II) Rs and analysis time CRF (Rs, number of peaks, analysis
Page 47 of 62
Norfloxacine and decarboxylated degradant
Pharmaceuticals
Norfloxacine tinidazole
Pharmaceuticals
Phosphate concentration; Buffer pH
Glutenins
Wheat varieties
IDA concentration; HPMC concentration; ACN %; Voltage; Temperature
Leucine enkephalin and its immune-complex
Test samples
Poliovirus
Real samples
Phosphate concentration; Buffer pH; Voltage Borate concentration; Sample plug size; Water plug size; Temperature; Concentration of buffer for dialysis of the samples
Ac c
NACE Antihistaminics (chlorpheniramine, cyproheptadine, diphenhydramine, doxylamine, methapyrilene, terfenadine, triprolidine) Quinolizinium
ep te
d
and
ip t
cr
Pharmaceuticals
time) CRS (Rs and analysis time)
CCD
[30]
Migration time
FFD
[129]
Rs; Peak area and RSD; migration time and RSD Rs; Peak area and RSD; migration time and RSD Rs; Peaks number; Total peak area; Time of elution of a peaks cluster (last peak – first peak); Total analysis time; Baseline drift Rs; Efficiency (plate number); Migration time Rs; Peak height; Peak width
FFD
[126]
FFD
[107]
fFDs; CCD
[47]
BBD
[114]
PBD
[44]
us
Maprotiline, desipramine, moclobemide
M
Test samples
an
Buffer pH TRIS-phosphate concentration; Buffer pH; Voltage Phosphate pH; Voltage; Temperature Phosphate concentration; Buffer pH
Cardiovascular drugs
Test samples
Sample plug size; Voltage Sodium acetate (buffer) pH; TBAP concentration; Voltage
CCD CRS (Rs and analysis time)
CCD
[66]
Test samples
SBE-β-CD concentration;
Sum of resolutions of two pairs of
OD
[53]
Page 48 of 62
Synthesized sample
Propranolol and 4-hydroxypropranolol
Test samples
Tamsulosin
Test samples
Clopidogrel and related substances
Commercial materials
Binaphtol and derivatives
Synthesized samples Pharmaceuticals
Antihistaminic drugs
Nuarimol (pesticide)
M
Pharmaceuticals
Test samples
CD-NACE Beta blockers
Test samples
CD-NACE Basic drugs
Test samples
d
ep te
raw
Ac c
Trimeprazine, promethazine
ip t
cr
enantiomers
Enantioresolution
OD
[52]
Enantioresolution; Analysis time
BBD
[56]
Enantioresolution; Analysis time
BBD; FCD; CCD (comparison)
[55]
Rs of two couples of adjacent peaks; Analysis time
Reduced FCD
[54]
Mobility ratio of enantiomers
CCD
[57]
Enantioresolution
BBD
[60]
Enantioresolution
BBD
[61]
Enantioresolution
BBD
[62]
Enantioresolution; Mobility difference; Selectivity
D-oD
[51]
Enantioresolution
D-oD
[68]
an
Dapoxetine
ACN %; Phosphate concentration; Buffer pH; Voltage; Temperature Acetate concentration; Buffer pH; M--CD concentration; MeOH %; Temperature; Voltage TEA-phosphate concentration; CM--CD concentration; Buffer pH; Voltage Sulfated--CD concentration; Voltage; Temperature TEA-phosphate concentration; Buffer pH; Sulfated--CD concentration; Voltage -CD concentration R-Etcholine NTf2concentration TRIS pH; HSA concentration; HSA plug length TRIS pH; HSA concentration; HSA plug length TRIS pH; HSA concentration; HSA plug length CD type (HDMS-β-CD, HDAS-βCD); Nature of BGE anion (formate; acetate; camphorsulfonate); BGE concentration; CD concentration Nature of BGE anion (formate; acetate; camphorsulfonate);
us
compounds derived from berberine
Page 49 of 62
CIEF Standard proteins (ribonuclease, carbonic anhydrase II, lactoglobulin A, cholecystokinin flanking peptide) FI-CE NADH and benzesulfonamide FI-CE Paracetamol, pseudoephedrine, dextromethorphan, chlorphenamine EMMA NAD and NADH, glucose-6-phosphate dehydrogenase (G6PDH) CEC Acetylsalicylic acid and impurities
Test samples
PEO concentration; PEO molecular weight; Voltage; Mobilization pressure
Test samples
Capillary length; Voltage; Injection volume Borate concentration; ACN %; Buffer pH; Voltage
CEC and p-CEC Warfarin, ketoprofen, praziquantel, paracetamol, metoprolol, pyrene, oxazepam CE-MS
ep te
d
M
Test samples
Pharmaceuticals
Mixing time; Voltage; Enzyme (G6PDH) concentration
Ac c
Test samples
ip t cr
Test samples
Relative migration time ratio obtained by using two markers for mobility calculation
BBD
[139]
Rs of adjacent pI markers, S/N
CCD
[137]
Migration time of two markers (ribonuclease, cholecystokinin); Difference in migration time of the two markers
FFDs
[132]
Absorbance
BBD
[138]
MCEF; Peak area of chlorphenamine; Rs chlorphenamine/dextromethorphan; Analysis time % of conversion of NAD to NADH
OD
[125]
BBD
[140]
Retention factor; Number of theoretical plates; Peak symmetry factor; Rs; Electrokinetic porosity; Equivalent length Retention time; Theoretical plate number; Peak asymmetry, Retention factor
Three level screening design
[101]
CCD
[102]
Signal intensity
Taguchi
us
ACE carbonic anhydrase B and a sulfonamide (estimation of Kb) CIEF Carbonic anhydrase isoforms
an
HMAS--CD concentration; BGE concentration Injection time; Voltage; Ligand (sulfonamide) concentration Content of ampholyte for CIEF; Focusing time; Mobilization pressure
Test samples
Amount of TMOS; Amount of PEG; Gelation temperature; Modifying time
Test samples
Pore-forming solvent (PFS, a mixture of water, 1,4-butanediol and 1-propanol) amount; 1,4-butanediol % in PFS.
Test samples
CH3COONH4 concentration;
L18
[105]
Page 50 of 62
ip t
Capillary voltage; screening design; Temperature (ESI); CCD Nebulizing gas pressure; Drying gas flow rate; Sheath liquid: Organic modifier nature; Organic modifier concentration; Acid nature; Acid concentration Acetonitrile (ACN); Asymmetric Screening Matrix (ASM); Background electrolyte (BGE); Binding constant (Kb); Box-Behnken Design (BBD); Carboxymethyl--cyclodextrin (CM--CD); Central Composite Design (CCD); Cetyltrimethylammonium bromide (CTAB); Chromatographic Exponential Function (CEF); Chromatographic Response Factor (CRF); Chromatographic Response Function (CRS); Cyclodextrin (CD); Cyclodextrin-modified Nonaqueous Capillary Electrophoresis (CD-NACE); Doehlert Design (DD); Doptimal Design (D-oD); Electrospray interface (ESI); Face Centered Design (FCD); Fractional Factorial Design (fFD); Full Factorial Design (FFD); Heptakis(2,3-di-O-acetyl-6O-sulfo)-βCD (HDAS-β-CD); Heptakis(2,3-di-O-methyl-6-O-sulfo)-β-CD (HDMS-β-CD); Heptakis(2-O-methyl-3-O-acetyl-6-O-sulfo)--CD (HMAS--CD); Heptakis(2,3,6tri-O-methyl)--CD (TM--CD); Human serum albumin (HSA); Hydroxypropyl methylcellulose (HPMC); Iminodiacetic acid (IDA); Isoeletric point, (pI); Methanol (MeOH); Methylated-γ-CD (M-γ-CD); Modified chromatographic exponential function (MCEF); Nicotinamide adenine dinucleotide (NAD); Nicotinamide adenine dinucleotide reduced form (NADH); Orthogonal Design (OD); Principal Component Analysis (PCA); Plackett-Burman Design (PBD); Polyethylene oxide (PEO); Polyethylenglicol (PEG); Relative Standard Deviation (RSD); Resolution (Rs); (R)()- 1-hydroxy-N,N,N-trimethylbutan-2-aminium bis(trifluoromethylsulfonyl) imide (R-Etcholine NTf2); Signal to Noise ratio (S/N); Sulfobutyl ether-β-CD (SBE-β-CD); Tetrabutylammonium perchlorate (TBAP); Tetramethylorthosilicate (TMOS); Traditional Chinese Medicine (TCM); Triethanolamine (TEAOH); Triethylamine (TEA); Tris(hydroxymethyl)aminomethane (TRIS); Uniform Design (UD).
Ac c
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d
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Hepcidin
Page 51 of 62
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Table(s)
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Table 2
Spiked samples
Budesonide and related substances
Raw materials pharmaceuticals
Baicalin, baicalein, wogonin
Scutellaria baicalensis herb
Nonsteroidal antiinflammatory drugs, lipid regulators, antiepileptics, fluoroquinolones, sulphonamides Caffeine
Drinking water
Melatonin metabolites
and
biological
Type of Design FFD
Ref [133]
Rs
FFD
[115]
Rs
D-oD; DD
[38]
Rs
PBD for robustness testing CCD
[80]
Rs; Analysis time
FCD
[84]
Sodium carbonate concentration; SDS concentration; Voltage
Interferent separation; Peak area; Noise intensity; Baseline variation; Analysis time; System current
CCD
[109]
Injection time; Injection pressure Phosphate concentration; Buffer pH; SDS concentration;
RSD (peak area)
FFD
Rs; Analysis time; Peak width
FFD
As above plus temperature Borate concentration; phosphate concentration; SDS concentration; ACN %; 2-propanol % SDS concentration; IPA %; Temperature
Ac c
ep te
and
Coffee samples
Test samples
Responses Rs
an
Betamethasone and desamethasone
Factors Phosphate concentration; Buffer pH; SDS concentration Borate concentration; Buffer pH; SDS concentration Borate concentration; cholate concentration; MAPS concentration; Buffer pH; Voltage
M
Type of sample Whitening cosmetics
d
Analytes Arbutin, kojic acid, hydroquinone
us
Applications of multivariate strategies to the optimization of MEKC and MEEKC methods.
[141]
Page 52 of 62
Polycyclic musks
Perfumes
Nicotinic acid and nicotinamide
Food
Five bisphenols
Test samples
Lignanoids
Sheng-Mai-San (TCM)
d
ep te
Ac c Abused drugs and metabolites
ip t cr
Food and beverages
Multiple response function as combination of efficiency
Draper-Lin small FCD
[128]
CCD
[134]
MCRF (Rs and analysis time)
BBD
[122]
Rs/analysis time ratio
PBD; BBD
[83]
CRF (Rs and analysis time)
BBD
[81]
Peak number
OD; sequential UDs
[82]
Relative peak area and relative migration times of selected peaks; Peak number
PBD for robustness testing
Peak numbers; Average peak areas
fFD
CEF for Rs and analysis
CCD
us
Carbohydrates
Voltage Borate concentration; Buffer pH; SDS concentration; Voltage Sorbate concentration; Electrolyte pH; SDS concentration Phosphate concentration; SDS concentration; Voltage Buffer composition (borate/phosphate); Buffer concentration; Buffer pH; SDS concentration; MeOH %; Voltage; Temperature; Injection time Borate concentration; Buffer pH; SDS concentration; ACN %; Borate concentration; Buffer pH; SDS concentration; MeOH %; Voltage; Temperature
Rs
an
Pharmaceuticals
M
-lactams antibiotics
Spiked urine
Borate concentration; Buffer pH; SDS concentration; Voltage; Temperature; Injection time; Wavelength Buffer concentration; Buffer pH; MeOH % (in both separation buffer and sweeping buffer);
[85]
Page 53 of 62
Biological samples
Test samples
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fFD
[87]
Rs; Migration times
fFD; CCD
[93]
In addition to the above, ratio of corrected peak area to that of internal standard Difference in migration time of -tocopherol from the cluster + tocopherols; Analysis time; Electric current Enantioresolution and analysis time (for MEKC optimization)
fFD for testing robustness
Sheath-liquid parameters: MeOH %; Buffer concentration; pH
Average peak area; Average S/N ratio (sheath liquid optimization)
CCD for sheath liquid optimization
Spray chamber parameters: Drying gas flow rate;
Average peak area; Average S/N ratio (spray
CCD for optimization
ep te
Vegetable oils
[86]
Corrected peak areas; Peak height
Borate concentration; Cholate concentration; Sodium hydroxide concentration
Ac c
Binaphthyl derivatives
by
fFD CCD
As above, plus MeOH%
Tocopherols NACE-MEKC
us
Test samples
Rs
CEF for Rs and analysis time
d
Cationic model compounds (e.g., quinine, propranolol, atropine etc.) Arachidonic acid metabolites
time
an
Cosmetic creams
M
Cosmetics preservatives
SDS concentration in sweeping buffer; Sample matrix concentration Phosphate concentration; Buffer pH; SDS concentration; MeOH %; Voltage Buffer conductivity; Zone length; Injection time; Sample conductivity; Water plug length Borate concentration; Buffer pH; SDS concentration; CD concentration; Temperature; Voltage
CH3COONH4 concentration; Buffer pH; Surfactants concentration; Voltage; Temperature Nebulization pressure
CCD
[75]
CCD for MEKC optimization
[76]
spray
chamber
Page 54 of 62
Sheath-liquid parameters: MeOH %; pH; Buffer concentration
Oxybutynin and related substances
Average peak area; Average S/N ratio (spray chamber optimization) Enantioresolution and analysis time (for MEKC optimization)
CCD for optimization
Sheath-liquid parameters: MeOH/H2O ratio; Buffer concentration
Average S/N ratio (sheath liquid optimization)
CCD for sheath liquid optimization
Spray chamber parameters: Drying gas flow rate Drying gas temperature Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; M-β-CD concentration; DM-β-CD concentration; Voltage
Average S/N ratio (spray chamber optimization)
CCD for optimization
Rs; Analysis time
MD; CCD
M-β-CD concentration; DM-β-CD concentration; Voltage; Temperature Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; HP-β-CD concentration;
Rs
PBD for robustness
Rs; Analysis time
MD; DD
M
Spray chamber parameters: Drying gas flow rate; Drying gas temperature CH3COONH4 concentration; Buffer pH; Surfactant concentration
d
Pharmaceuticals
Pharmaceuticals
ip t CCD for sheath liquid optimization
ep te
Clemastine and related substances
Test samples
CCD for MEKC optimization
Average peak area; Average S/N ratio (sheath liquid optimization)
Ac c
Mephobarbital, pentobarbital, secobarbital
chamber optimization) Enantioresolution and analysis time (for MEKC optimization)
cr
Drying gas temperature CH3COONH4 concentration; Buffer pH; Surfactant concentration; ACN %; Voltage
us
Test samples
an
Binaphthyl diamine
spray
chamber
CCD for MEKC optimization
spray
[77]
[78]
chamber
[94]
[95]
Page 55 of 62
ip t us
Rh measuring interference between isomerization and electrophoretic migration; Number of peaks
MEKC: ASM; BBD
Rs; Analysis time
RF-MEEKC: MD; DD
[79]
ep te
d
Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; Phosphate concentration; Voltage; Temperature
FFD for robustness
an
Pharmaceuticals
As above
M
Ramipril and related substances
HP-β-CD concentration; Voltage; Temperature Borate concentration; Buffer pH; SDS concentration; THF %; Voltage; Capillary length; Temperature
cr
Voltage
Ac c
pH; Rs; RF-MEEKC: PBD for robustness Phosphate concentration; Analysis time testing Voltage; Temperature Coenzyme Q10, Nutraceuticals Aqueous phase %; Peak efficiency MPV [96] ascorbic acid, folic Oil phase %; acid, fumaric acid, Surfactant/cosurfactant %; acesulfame Acetate (buffer) concentration; Buffer pH Voltage Heptakis(2,6-di-O-methyl)--CD (DM--CD); Hydroxypropyl--CD (HP--CD); Isopropanol (IPA); Methyl--CD (M--CD); Mixture Design (MD); Mixture-Process Variable Design (MPV); Modified Chromatographic Response Factor (MCRF); 3-(N,N-dimethylmyristylammonio)propanesulfonate (MAPS); Reversed flow (RF); Sodium dodecyl sulfate (SDS); Tetrahydrofurane (THF). For other abbreviations, please see Table 1.
Page 56 of 62
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Figure(s)
Fig. 1, Orlandini etPage al.57 of 62
Figure(s)
D
1.00
D
1.00
(a)
1.00
(b)
0.50
(c)
0.50
V
0.50
T
0.00
T
0.00
BGE conc.
V
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BGE conc.
0.00
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D
Fig.2, Orlandini etPage al.58 of 62
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Figure(s)
Fig.3, Orlandini etPage al. 59 of 62
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Figure(s)
Fig. 4, Orlandini etPage al.60 of 62
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Figure(s)
Fig.5, Orlandini etPage al.61 of 62
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Figure(s)
Fig.6, Orlandini etPage al. 62 of 62
S0731-7085(13)00160-X http://dx.doi.org/doi:10.1016/j.jpba.2013.04.014 PBA 9036
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received date: Accepted date:
10-4-2013 12-4-2013
Please cite this article as: S. Orlandini, R. Gotti, S. Furlanetto, Multivariate optimization of capillary electrophoresis methods: a critical review, Journal of Pharmaceutical and Biomedical Analysis (2013), http://dx.doi.org/10.1016/j.jpba.2013.04.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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*Graphical Abstract
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*Highlights (for review)
1
HIGHLIGHTS
Recent applications of multivariate optimization of CE methods are reviewed.
The characteristics of the applied chemometric strategies are summarized in tables.
A critical discussion on multivariate optimization of CE methods is presented.
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Page 2 of 62
1
1
Multivariate optimization of capillary electrophoresis methods:
2
a critical review
3
Serena Orlandinia, Roberto Gottib, Sandra Furlanettoa,*
4
Department of Chemistry “U. Schiff”, University of Florence, Via U. Schiff 6, 50019 Sesto Fiorentino,
6 7
Florence, Italy b
Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, 40126 Bologna,
8
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Italy
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10 11
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14 15
19 20 21 22
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23 24 25
* Corresponding Author. Tel.: +39 055 4573717; fax: +39 055 4573779.
26
E-mail address: [email protected] (S. Furlanetto).
27
Page 3 of 62
2 28 29
Abstract
30 In this article a review on the recent applications of multivariate techniques for optimization of
32
electromigration methods, is presented. Papers published in the period from August 2007 to February 2013,
33
have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the
34
characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number
35
of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and
36
pointing out the most common issues involved in multivariate set-up of CE methods, is provided.
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Keywords: Electromigration Methods; Experimental Design; Multivariate Optimization; Review
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3 41 42
1.
Introduction
43 “Experimental design” is a well-established procedure useful in various research fields and it is
45
included in chemometric techniques. The pioneers of chemometrics were well-recognized scientists, namely
46
Roger Phan-Tan-Luu [1], Svante Wold [2], Désiré Luc Massart [3,4], Michele Forina [5] and Bruce R.
47
Kowalski [6]. The term “chemometrics” was coined by S. Wold in a grant application in 1971, and the
48
International Chemometrics Society was formed shortly thereafter by B. Kowalski and S. Wold [7]. S.
49
Wold’s group founded Umetrics [8], devoted to multivariate data analysis, including experimental design. In
50
the 70’s R. Phan-Tan-Luu founded the Laboratory of Methodology of Experimental Research in Marseille,
51
spreading in the world the use of experimental design. Phan Tan-Luu founded also LPRAI Company [9],
52
dedicated to experimental design and still active in France.
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44
Experimental design is a methodology of experimental research, called also “Design of
54
Experiments” (DoE), in which the variables under study are simultaneously changed inside an experiment. It
55
includes different strategies and statistical tools aimed to guide the researcher in selecting regions of interest
56
inside a large experimental region, with a minimum number of experiments. The use of experimental design
57
has been increasing in the last years due to the large diffusion of dedicated softwares, the transfer of know-
58
how, the increase of collaboration between university and industry, as well as the need of systematic analysis
59
of experimental data.
ce pt
ed
53
DoE includes screening, response surface, mixture and process-mixture matrices. Each of these
61
should be used according to the aim of the study. Responses and controllable variables, named factors,
62
should be identified, and a regression model linking factors to responses should be postulated. According to
63
the postulated regression model, an experimental matrix should be selected. An experimental matrix is a
64
table where the columns report the factors’ values and the rows report the codified values that each factor has
65
to assume in each experiment, i.e. the rows represent the experiments. Once the experiments are carried out
66
and the responses are measured, the regression model is calculated using multivariate linear or partial least
67
square regressions. The analysis of variance (ANOVA) is used to establish significance and validity of the
68
model. If the found regression model is significant and valid, it can be used to explain the obtained data. If
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4 69
several responses are simultaneously considered, DoE tools such as desirability function [10] or Pareto
70
optimization [11] may be useful to find a proper compromise among them. In analytical chemistry, experimental design is effectively used in optimization and validation steps.
72
When optimizing an analytical method, the easier approach is the screening one, that selects from a number
73
of potential factors, studied at n levels, those significant, with a limited number of experiments. On the
74
contrary, in response surface methodology (RSM) the number of experiments increases; anyway, in this case
75
it is possible to obtain a regression model describing in a provisional way the response variation in the
76
experimental domain.
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71
In capillary zone electrophoresis (CZE), several chemical, physical and instrumental parameters
78
should be controlled in order to obtain good analysis performances in terms of minimum analysis time and
79
high resolution, efficiency and sensitivity; moreover, these parameters may be often interacting in nature.
80
Only process or independent variables (PVs) are involved in CZE, and their easy change permits an
81
advantageous use of experimental design. When electrokinetic chromatography (EKC) is used, a
82
pseudostationary phase (PSP) is present in the capillary, and the degree of separation, efficiency, and
83
analysis time may depend on both electrophoretic mobility and partitioning. In particular, in microemulsion
84
electrokinetic chromatography (MEEKC) mixture components (MCs) of the microemulsion constituting the
85
background electrolyte (BGE) should be also taken into consideration. Thus, the PSP composition is
86
fundamental for the performance of electrophoretic run and changes in the levels of process factors can
87
affect the separation properties of the PSP; in the same way, changing the MC proportions of the PSP may
88
differently affect the response for different combinations of the PVs. Usually, the optimization of such
89
systems involved a two-stage approach, consisting in the optimization of mixture factors by mixture design
90
followed by the optimization of process factors by response surface study. Anyway, it should be
91
recommended to simultaneously vary mixture and process variables using a mixture-process variable
92
approach (MPV), which uses experimental matrices where the process and mixture factors are
93
simultaneously changed. MPV models, which correlate the MCs and the PVs with responses, are postulated.
94
MPV approach is more complex than other strategies, but it allows the power of experimental design to be
95
highlighted.
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5 Characteristics and applications of the designs used in the optimization of analytical methods have
97
been recently reviewed [12-15], also focusing the attention on the optimization of separation methods such
98
as chromatography [16-19] and capillary electrophoresis [16,17]. In particular, the multivariate optimization
99
of capillary electrophoresis methods was the topic of reviews by Altria et al. [20], by Sentellas and Saurina
100
[21] and more recently by Hanrahan et al. [22]. An overview of the experimental designs in the optimization
101
of chiral CE and CEC has been also freshly provided by Dejaegher et al. [23].
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96
This paper intends to focus on the recent applications of multivariate optimization of capillary
103
electrophoresis methods, taking into account the publications dating from August 2007 to February 2013,
104
with a few exceptions which appeared to be of specific interest. The chapters are grouped by type of
105
capillary electrophoresis operative mode and the information gathered is collected in tables. In particular,
106
Table 1 gives a comprehensive overview on the applications of multivariate optimization of CZE and related
107
techniques, while Table 2 refers to micellar electrokinetic chromatography (MEKC) and MEEKC. Finally, a
108
critical discussion of the practical issues which may be encountered when optimizing electromigration
109
methods by DoE is presented.
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111
2.
ed
110
Capillary zone electrophoresis (CZE)
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112
In capillary zone electrophoresis (CZE) the separation is based on differences in the charge-to-mass
114
ratio of the analytes, thus all the relevant factors affecting the solutes’ charge, play a role in selectivity
115
tuning. In particular, buffer pH, type and concentration, influence the migration velocity of the analytes, the
116
separation efficiency and peak shape [24].
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113
117 118
2.1.
Buffer pH
119
Among the factors affecting CZE separation, the most important is undoubtedly the buffer pH and,
120
as theoretically demonstrated, the optimum separation selectivity is obtained at buffer pH values close to the
121
pKa of the analytes [25]. Analytes containing both acidic and basic functions have to be dealt by considering
122
the existence of a critical pH value corresponding to the average of the pK a values of acidic and basic
Page 7 of 62
6 function, i.e., the isoelectric point, pI. In a study on the separation of biogenic amines including amino acids,
124
the chemometric evaluation of the effect of several parameters (BGE concentration, BGE pH, voltage and
125
percentage of organic modifier) on the Chromatographic Exponential Function (CEF) confirmed that pH was
126
the most significant individual factor with the highest effect on the quality of the electropherogram [26]. The
127
pH range that has to be optimized by DoE is often preliminarily selected by univariate evaluation of the
128
analytes behavior under variation of buffer pH. The evolution of electrophoretic mobility versus buffer pH of
129
a number of organic aliphatic acids (acetic, aspartic, citric, formic, lactic, malic, oxalic, phthalic, pyruvic,
130
succinic and tartaric acids) suggested that separation was free from interferences by system peaks normally
131
occurring in direct UV detection, within two pH ranges, namely 3.0–4.2 and 5.5–7.0. Successively, by
132
experimental design it was found that pH 3.4, in the presence of cetyltrimethylammonium bromide (CTAB)
133
for electroosmotic flow (EOF) reversal, was the best condition. The method was validated and applied to the
134
determination of some aliphatic acids in Brachiaria Brizantha extracts [27]. A similar approach was also
135
applied in finding CZE conditions for the analysis of antimicrobials including fluoroquinolones,
136
sulfonamides and chloramphenicol. In order to limit the number of “wet” experiments, the evolution of
137
solutes mobility versus pH was estimated by simulation experiments to restrict the range of pH to be
138
optimized by experimental design [28]. A nice application showing the opportunity offered by preliminary
139
investigation on the effect of pH on CZE selectivity by simulation software Peakmaster 5.2, was given by
140
Johns et al. [29]. The separation of 17 antipsychotics was approached by exploring the analytes behavior in a
141
very wide pH range (2.0-10.5), covering all of the occurred protonation stages. It was found that strongly
142
acidic conditions gave the best response as Chromatographic Response Factor (CRF). The full protonation of
143
basic solutes is rather conventional in CZE analysis; in these situations, the difference in solutes molecular
144
masses addresses the separation selectivity. Accordingly, strongly acidic BGE were found to be suitable in
145
the analysis of basic cardiovascular drugs [30] and antihistaminics [31] performed in Tris-phosphate buffer;
146
in particular, it was found that among the pH values investigated by DoE, the best response
147
(Chromatographic Response Function, CRS) was achieved at the most acidic conditions (pH 2.5–2.7). The
148
analysis of fluoxetine and norfluoxetine was conveniently carried out under on-line sample stacking
149
conditions at pH 2.5 that additionally minimized the absorption of basic analytes to the inner capillary wall
150
[32]. Schappler et al. found that even if propranolol could be analyzed in a wide pH range (pH studied within
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Page 8 of 62
7 2.0–7.0), strongly acidic conditions were the best for the improved detection response (signal-to-noise) using
152
Laser-Induced Fluorescence Detector (LIF). In that application, the responses sensitivity and efficiency were
153
studied by a sequential multivariate methodology that involved a response surface approach carried out in
154
more than one step. In particular, the response surface study was performed by means of a Box-Behnken
155
design (BBD), introducing the applied voltage as a new factor in a second set of runs. At the end of the study
156
all the results were merged and used to find a regression model able to describe the response in the whole
157
considered experimental domain [33]. More critical separations of basic compounds, can be conveniently
158
carried out by selecting a buffer pH close to the pKa values of the analytes; according to the Handerson-
159
Hasselbalch equation, it can be derived that a ionized base still exists also at pH values well above the pK a;
160
e.g., when pH of the electrolyte is pKa + 1, the fraction of the protonated base [BH+]/[B] is 1:10. In an
161
application of experimental design to CZE, the analysis of aconitine and hypaconitine was optimized
162
planning the experiments according to an experimental design strategy investigating each factor at 5 levels
163
and finding the relationship among factors and responses with neural network. In particular, the pH range
164
was studied within 7.5 to 9.5 and it was pointed out that it played the major role affecting the peak resolution
165
with a maximized separation at pH 9.5 [34]. Similar conditions were found to be useful in analysis of
166
epinastine, however in such situation the involved analytical challenge was rather simple [35].
ed
M
an
us
cr
ip t
151
Electromigration techniques are conveniently applied for analytical profile (quali- and quantitative)
168
of natural polyphenol compounds that increasingly attract the interest of the researchers for the role they
169
have shown against biomarkers for cancer, cardiovascular diseases and other degenerative diseases [36]. Due
170
to their acidic nature, phenolic compounds can be analyzed in CZE using borate buffer. A response surface
171
study by means of a central composite design (CCD) was used to find optimum conditions for the separation
172
of 13 phenolic compounds from extra-virgin olive oil. The considered responses were five selected critical
173
resolution values of adjacent peaks and the experimental design approach showed that the effect of pH
174
changing on separation was rather complicate [37]. By directly analyzing response surfaces, it was possible
175
to point out optimum conditions; further sets of conditions selected by desirability function were only
176
slightly different with respect to those found.
Ac
ce pt
167
177
When several analytes have to be separated and RSM is applied, the migration order of the
178
compounds should not preferably be changed in order to be able to univocally measure the response
Page 9 of 62
8 179
resolution [38]. In general pH has a strong effect on migration behavior of analytes, and for this reason
180
buffer pH has sometimes been suggested to be maintained at a constant value (e.g., pH 10 or the native pH of
181
borate buffer) also when using a multivariate optimization [39,40].
182
2.2.
Buffer type and concentration (electrolytic conductivity and ionic strength)
ip t
183
The running buffer selection is a key factor to the success of any CE separation. As a general rule, it
185
has to be pointed out that the buffer system effectively works in a pH range approximately corresponding to
186
two units centered on the pKa value. The most suitable buffer type for a given CZE separation, is in general
187
identified by preliminary experiments by univariate method [33]. More generally, phosphate and borate
188
buffers can be considered as ideal for conventional CZE separations because of the low absorbance at the
189
wavelength of detection (often in the UV range); in addition, they showed to be suitable for analysis using a
190
number of detection systems including electrochemiluminescence (ECL) [30,31] and LIF detection [33]. On
191
the other hand, buffer concentration affects the electrophoretic behavior of the analytes by influencing the
192
separation selectivity, the EOF strength and stacking effect. A chemometric strategy for buffer selection was
193
developed by considering some relevant descriptors of BGE systems used for CZE analysis; they were (i)
194
pH, (ii) , conductivity; (iii) I, ionic strength and (iv) , relative viscosity. Two distinct series of potentially
195
useful BGEs comprising 222 buffers for analysis of acidic drugs (arylpropionic acids) and 117 buffers for
196
analysis of basic drugs (beta-blockers), were screened by Principal Component Analysis (PCA). In analysis
197
of acidic drugs, 0.1 M borax buffer (pH = 9.22, = 8.72 mS cm-1, I = 0.200 M, = 1.067) was identified as
198
the most suitable BGE, whereas in analysis of the selected set of basic drugs, citrate buffer (pH = 4.04, =
199
3.35 mS cm-1, I = 0.066 M, = 1.038) was found to be the optimum [41,42]. Borate buffer is often applied in
200
CZE at the native pH for its ability to give in-situ complexation of polyhydroxy compounds such as
201
carbohydrates and polyphenols aglycones [36,37,39,40]. In particular, borate forms charged mobile five-
202
membered-ring complexes (with 1,2-diols) and six-membered ring complexes (with 1,3-diols) and as a
203
consequence, an increased selectivity of separation can be achieved [43].
Ac
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cr
184
204
The electrical conductivity plays an important role in on-line preconcentration, mainly when sample
205
stacking has to be performed. In the analysis of real samples of poliovirus, the major challenge was the
206
sensitivity enhancement, thus optimization with DoE was aimed to identify all the favorable conditions for Page 10 of 62
9 increasing peak height. Plackett-Burman design (PBD) was used in the screening step and CCD was
208
employed for the response surface study. It was confirmed that a successful separation of the components of
209
large sample plugs, required high conductivity mismatch between BGE and sample plug. This was obtained
210
by dilution of real samples and by increasing the BGE concentration to the maximum values among the
211
tested levels (borax 100 mM) [44]. Phosphate - triethanolamine (TEAOH) buffer at low pH was considered
212
to be useful for analysis of fluoxetine and norfluoxetine by short-end injection and on-line stacking; in
213
particular TEAOH was used to prevent the analytes adsorption onto the inner capillary wall. A screening
214
approach using a fractional factorial design (fFD) was chosen for the simultaneous optimization of factors
215
affecting both separation and sample stacking efficiency. Then, a response surface study by means of CCD
216
was used for only optimizing the response resolution between the two analytes [32].
us
cr
ip t
207
218
2.3.
an
217
Effect of addition of organic solvents to the BGE
The addition of organic solvents to the BGE is a good choice to avoid precipitation of the solute
220
during the electrophoretic run; on the other hand, the presence of organic solvents changes zeta potential and
221
viscosity, thus a variation in electroosmotic velocity is observed as a consequence. Methanol, ethanol, n-
222
propanol, isopropanol and acetonitrile (ACN) are the most used solvents because of their aqueous solubility
223
and low UV absorption. It has been shown that using mixtures of water-alcohols (e.g., methanol and
224
isopropanol), the EOF decreases compared to the pure aqueous buffer; differently, addition of ACN, because
225
of its very low viscosity, decreases the EOF. Eventually, addition of organic solvents to buffer solutions can
226
lead to pKa variations of the analytes with effects on their absolute and actual mobility [45,46]. Owing to the
227
complex combination of the effects of the organic solvents (interaction with the analytes through solvational,
228
dissociative or multimolecular complex formation phenomena) on the electromigration of the solutes,
229
chemometric tools are particularly suitable to investigate their possible interactions with other parameters
230
involved in CZE separations [26,32,34,39,40,47].
Ac
ce pt
ed
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219
231 232
2.4.
Effect of buffer additives in CZE
233 234
2.4.1. Cyclodextrins Page 11 of 62
10 Cyclodextrins (CDs), because of their ability in inclusion-complexation of a considerable number of
236
analytes (host–guest interaction), can be supplemented to the CZE buffer to favorably modify the
237
physicochemical characteristics of the guest molecules by producing altered migration behavior. Several
238
reviews deal with the use of CDs in capillary electrophoresis as selective agents for discrimination between
239
positional isomers, molecular substructures, homologues and enantiomers [48-50]. Rousseau et al. applied
240
experimental design to estimate the effects of the nature of CD (two different sulfated CDs were tested) on
241
the enantioseparation of -blockers under non-aqueous CE conditions (NACE) [51]. More generally,
242
however, preliminary univariate experiments are performed to select the suitable CD to be added to BGE;
243
then CD concentration and other typical variables affecting CZE separation are optimized by DoE either for
244
enantioresolution [52-57] and separation improvement of strictly related compounds [58,59]. Often, when
245
anionic CDs are used in analysis of basic compounds, the investigated pH range is maintained within the
246
values that allow for the full ionization of the chiral selector concomitantly with the solutes protonation. This
247
condition indeed is able to provide an enhancement in separation selectivity because of the higher mobility
248
difference between the free and the complexed enantiomers. Accordingly, in an application aimed at the
249
enantioselective analysis of propranolol and a metabolite using carboxymethyl--CD (CM--CD), the
250
investigated pH range in the response surface study (BBD method), was within 8.0–9.0 [56], whereas using
251
sulfated CDs (strong acidic behavior), also buffers at low pH values were used in enantioseparation of
252
clopidogrel [54] and tamsulosin [55].
253
2.4.2. Proteins
Ac
254
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ed
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235
255
The tertiary structure of proteins is supposed to be responsible for the chiral recognition of neutral,
256
basic and acidic analytes. Human serum albumin (HSA) was found to be particularly useful as a chiral
257
additive in free solution CE because of its ability in enantioselective binding of a number of drug molecules
258
by establishing hydrophobic and dipole-dipole interactions as well as hydrogen bonding. In general, owing to
259
their UV absorption, when proteins are supplemented as additives to the BGE in free solution, the partial-
260
filling technique has to be applied [50]. The enantioresolution in partial-filling CE is the result of the proper
261
selection of pH; this paramenter affects the EOF and the electrophoretic mobility of the analytes and the
262
protein, according to their respective pKa and pI values. In addition, the length of the sample plug in relation
Page 12 of 62
11 263
with that of the capillary has to be carefully identified. Because of the reciprocal interactions of these
264
parameters, the chemometric optimization of enantioseparation can be considered as a good choice, as
265
demonstrated by the results achieved using HSA as chiral selector in analysis of basic drugs including
266
phenotiazines [60], correlated antihistamines [61] and the pesticide nuarimol [62].
268
3.
Nonaqueous capillary electrophoresis (NACE)
cr
269
ip t
267
In nonaqueous capillary electrophoresis (NACE), pure organic solvents or their mixtures, are used in
271
preparation of running buffers for improving solubility of hydrophobic analytes and/or to gain specific
272
selectivity because of the effect of the solvents on acid-base properties of the solutes. NACE widens the set
273
of physicochemical characteristics of the running buffers, which are known to affect the electrophoretic
274
behavior of the solutes by influencing interactions with solvents, additives and ion-ion interactions that are
275
too weak or cannot take place in aqueous BGEs [63-65]. Because of their solubility in organic solvents
276
mixtures, the most used supporting electrolytes in NACE are ammonium acetate, ammonium formate and
277
sodium acetate. In the separation of seven antihistamines by NACE using electrochemical (EC) and ECL
278
detections, a CCD was applied for examining the factors such as apparent pH*, separation voltage and
279
concentration of the uncommon electrolyte tetrabutylammonium perchlorate (TBAP). A strong interaction
280
between buffer pH* value and voltage, together with significant quadratic effects of all the considered
281
factors were found acting on the selected response CRS [66].
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us
270
Supplementing CDs as chiral selectors in NACE buffers could provide specific enantioselectivity;
283
obviously, the hydrophobic interaction between solutes and CDs does not exist in organic solvent, thus it can
284
be concluded that inclusion complexation occurs to a small extent, if at all. Instead, solutes might interact on
285
the mouth of the CD and bind the hydroxyl groups on the rims through polar interaction; in addition, by
286
using charged CD electrostatic interactions can be involved in enantiorecognition [67]. Rousseau et al.
287
performed an exhaustive study on the effect of the nature of anionic CD (heptakis(2,3-di-O-methyl-6-O-
288
sulfo)-β-CD (HDMS-β-CD) and heptakis(2,3-di-O-acetyl-6-O-sulfo)-β-CD (HDAS-β-CD)) and BGE
289
composition on the enantioseparation of ten β-blockers. The experimental design involved both qualitative
290
and quantitative factors and the investigated responses were enantiomeric resolution, mobility difference and
Ac
282
Page 13 of 62
12 291
selectivity. Due to the correlation among the three responses the Authors only discussed the model relative to
292
enantiomeric resolution [51]. An analogous approach was also followed by the same team of Authors in a
293
subsequent study for evaluating the enantioseparation of 10 basic drugs by another anionic CD, namely
294
heptakis(2-O-methyl-3-O-acetyl-6-O-sulfo)-β-CD (HMAS-β-CD) [68].
295
Micellar electrokinetic chromatography (MEKC) and microemulsion
297
electrokinetic chromatography (MEEKC)
ip t
4.
cr
296
us
298
Electrokinetic chromatography (EKC) is the method of choice for the analysis of neutral compounds
300
and one of the most versatile separation approaches among the electromigration methods. In micellar
301
electrokinetic chromatography (MEKC) a surfactant (often sodium dodecyl sulfate, SDS) is supplemented to
302
the electrophoretic BGE at concentration above the critical micelle concentration (cmc) to form anionic
303
micelles whose core is highly hydrophobic, as it is formed by the SDS tails. Neutral analytes, driven by the
304
EOF toward the detection end, are incorporated into the micelle according to their hydrophobicity and the
305
separation is the result of combination of chromatographic partitioning of the solutes between the micellar
306
phase (the pseudostationary phase, PSP) and the continuous phase. In analogy, the charged solutes undergo
307
to the same process, however in such a situation the separation mechanism combines the solutes partitioning
308
into micelles and electrophoretic migration [69-71]. In microemulsion electrokinetic chromatography
309
(MEEKC) the separation medium is constituted of a nanometer-sized emulsion based on oil droplets (such as
310
n-octane or other hydrophobic solvents) suspended in aqueous buffer. Microemulsions are stabilized by the
311
presence of a surfactant and a co-surfactant, which is a short-chain alcohol such as butanol [70,72,73].
Ac
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299
312
The opportunity for selectivity tuning in EKC are thus very wide, including variation of BGE type,
313
pH and concentration, but also selection of the surfactant, optimization of surfactant concentration, use of
314
mixed surfactants, addition of organic modifiers and other additives. Giving the multiple and often complex
315
mechanisms involved in EKC separations, experimental design appears to be a more efficient tool for the
316
optimization of separation conditions than conventional univariate methods.
Page 14 of 62
13 One of the major factors affecting selectivity in EKC is undoubtedly the nature of surfactant. The
318
retention of the solutes by SDS micelles, the most used surfactant in MEKC and MEEKC, is primarily
319
influenced by size of the molecules and their hydrogen bond accepting basicity. In other words, the capacity
320
factor increases with the size of the analytes and decreases for stronger hydrogen bond acceptor bases.
321
Sodium cholate (SC) has been described as a surfactant creating more polar micelles compared to SDS and,
322
in addition, SC has shown higher hydrogen bond acceptor basic character [74]. Actually, SC was favorably
323
used for the separation of highly hydrophobic tocopherols in vegetable oils by means of nonaqueous MEKC
324
[75],
325
dimethylmyristylammonio)propanesulfonate in the preparation of mixed micelles allowed the fast separation
326
of neutral budesonide and related substances [38]. Because of the lack of knowledge about the mechanism of
327
solute-micelle interactions, multivariate strategy for optimizing such a complex system was found to be
328
useful. A response surface study by means of a 34-run Doehlert design (DD) allowed the optimization of the
329
mixed MEKC system including the selection of the optimum borate concentration, pH and running voltage
330
[38].
combined
use
with
the
zwitterionic
surfactant
cr
its
3-(N,N-
M
an
us
and
ip t
317
Chiral micelle polymers were used for hyphenation of MEKC to electrospray (ESI) mass
332
spectrometry; in such a situation multivariate methodologies were helpful not only in the optimization of the
333
parameters affecting separation, but also for the fast selection of operating factors influencing MS detection
334
(sheath liquid and spray chamber parameters in ESI-MS) [76-78].
ce pt
ed
331
In general, as in CZE, the choice of BGE pH in MEKC and MEEKC is based on its ability to change
336
the ionization of analytes with a consequent alteration of their distribution towards the PSP; in addition,
337
buffer pH controls the ionization of inner capillary wall and EOF. In a recent study on the separation of
338
ramipril and related substances, it was found very useful to carry out MEEKC experiments under strongly
339
acidic phosphate buffer (pH 2.5). In fact, the suppression of the EOF allowed to perform separation in
340
reversed flow mode (RF-MEEKC), which, combined with the short–end injection modality, allowed the fast
341
separation of ramipril and related substances by preventing the cis-trans isomerization of the proline-moiety
342
containing compounds (Fig.1). The use of a mixture design followed by a response surface study allowed the
343
complex separation system to be optimized with the aid of desirability function (Fig. 2), resulting in the
344
complete separation of the analytes in about 10 min (Fig. 3) [79].
Ac
335
Page 15 of 62
14 Water-miscible organic solvents are commonly supplemented to MEKC systems to increase the
346
solubility of hydrophobic compounds as well as to control their partitioning into the micelle [69-71]. In
347
general, preliminary experiments by univariate approach are carried out to select the suitable organic solvent
348
(in general ACN, methanol, propanol etc.) for the intended purpose, then the concentration is selected by
349
DoE. The volume fraction of the solvent should not exceed 30% since higher levels may break down micelle
350
or microemulsion [76,77, 80-87].
ip t
345
Addition of cyclodextrins in MEKC (CD-MEKC) combines the potential of micelles as separation
352
carrier with the capability of cyclodextrins in recognizing specific solute molecules by hosting them into the
353
hydrophobic macrocyclic cavity. The inclusion complexation into the chiral cyclodextrin cavity occurs in
354
competition with the partition of the analyte into the surfactant micelle providing the system with an
355
extended flexibility and widening of selectivity and enantioselectivity tuning [88-90]. Variations of
356
cyclodextrin concentration in CD-MEKC optimization are however restricted by the strong interaction of
357
surfactant monomers with the cyclodextrin cavity, which competes with the self-assembling process (e.g.,
358
SDS aggregation) [88-92]. In a preliminary investigation based on conventional approach, Abromeit et al.
359
tested several neutral CDs for improving MEKC analysis of highly hydrophobic lipoxygenases metabolites
360
and found that -CD offered the best performance. Then, a multivariate optimization of separation
361
conditions involving factors such as SDS concentration, capillary temperature and voltage, buffer
362
concentration and pH, together with -CD concentration was conducted, first employing a fFD and then
363
deepening the study by a CCD [93]. Similarly, univariate optimization of SDS-based MEKC was not
364
successful in separation of pharmaceuticals as contaminants in drinking water even if different organic
365
solvents (the most suitable was found to be isopropanol) were tried as modifiers of the conventional SDS
366
system. Addition of CDs was investigated and it was found that sulphated -CD provided the best
367
separation; a face centered composite design (FCD) allowed the separation conditions to be optimized [84].
Ac
ce pt
ed
M
an
us
cr
351
368
A dual system of cyclodextrins in MEEKC was for the first time used and optimized by experimental
369
design for the separation of clemastine and its related substances. The addition of two suitable cyclodextrins
370
to MEEKC was compulsory to obtain a baseline separation of the compounds and the multivariate
371
optimization was necessary to optimize such a complex system, in particular by identifying the optimum
372
ratio between the two cyclodextrins and the interactions between the parameter concentration of each of the
Page 16 of 62
15 CDs [94]. Similarly, the separation of oxybutynin and its impurities was accomplished by optimized CD-
374
MEEKC [95]. By a proper experimental strategy, involving first a mixture design for finding the best
375
microemulsion composition (Fig. 4) and then RSM for optimizing the process parameters related to
376
instrumental conditions and buffer (Fig. 5), the complete separations of the main drugs in the presence of the
377
impurities and internal standards in short analysis time were obtained [94,95]. It should be noted that in such
378
situations the variations of experimental factors are constrained because the stability of the microemulsion
379
could be jeopardized by addition of CDs that can act as sequestering agents towards oil, surfactant and
380
cosurfactant. However, in MEEKC the effects of changing the proportions of the mixture components (MCs,
381
related to microemulsion composition) and process variables (PVs, namely voltage, buffer concentration,
382
buffer pH) can be simultaneously studied by applying a mixture process variable (MPV) approach. For the
383
first time in the literature, Piepel et al. used the MPV approach in the optimization of the MEEKC analysis of
384
coenzyme Q10, ascorbic acid and folic acid [96]. Despite of the high number of runs of the design, the MPV
385
approach pointed out for the first time the presence of important interactions between PVs and MCs (Fig. 6).
M
an
us
cr
ip t
373
CE methods involving the use of a PSP such as MEKC and MEEKC are often combined with on-line
387
sample preconcentration approaches that are used to overcome the poor concentration sensitivity of
388
conventional UV detection. Large volume sample stacking is a mode of sample injection consisting in
389
introducing large volume of sample into the separation capillary; successively, the analytes dissolved in the
390
large sample zone are focused into a narrow zone before separation, in order to obtain improved
391
concentration sensitivity [97]. Using a charged PSP, sweeping can be then performed, as micelles or
392
microemulsions by penetrating the sample zone allow the accumulation of the solutes in a small analytical
393
band. Higher is the hydrophobicity of the analyte and higher is the concentration efficiency. Although the
394
sweeping technique proposed by Quirino and Terabe was originally used for concentration of neutral
395
molecules [98], it has shown to be equally well applied to ionic analytes as long as they have high affinity
396
toward the PSP [99]. Giving the multiple variables and their complex interactions, on-line preconcentration
397
can be favorably optimized by DoE [85-87].
Ac
ce pt
ed
386
398 399
5.
Capillary electrochromatography (CEC)
400 Page 17 of 62
16 In capillary electrochromatography (CEC) the migration of charged solutes is combined with their
402
chromatographic partition toward a stationary phase in capillary format suitable to be operated by
403
conventional CE instrumentation. The CEC columns can be constituted by small particles (e.g., 3 m),
404
packed in a fused-silica capillary and fixed by frits prepared by silica sintering. The advances in column
405
technology have led to an increasing preference for application of CEC monolithic column consisting of
406
porous structure (both organic such as polymer-based and inorganic, typically silica-based) fabricated inside
407
the fused-silica capillary tube and forming a continuous bed of chromatographic material, thus avoiding frits
408
[100]. However, to produce reliably monolithic columns requires the application of strictly controlled
409
conditions. A three level screening design was thus employed for evaluating the influence of four factors
410
involved in the sol-gel synthesis on the performance of C18-silica monoliths; specifically, amounts of
411
tetramethylorthosilicate (TMOS) and polyethylenglicol (PEG), gelation temperature and modifying time
412
were screened. Then, the obtained monoliths were modified by octadecyltrichlorosilane and tested for
413
separation of benzene, toluene, ethylbenzene, 1-phenyhexane in reversed-phase mode, to estimate
414
performance parameters. The responses included both monolith properties (equivalent length and
415
electrokinetic porosity) and properties related to quality of the electropherograms, namely efficiency,
416
resolution, retention factor and symmetry factor. It was found that the concentration of TMOS and PEG in
417
the starting mixture, and the gelation temperature, had the most important effects on the separation
418
performances. Eventually, the optimized conditions were used for monolithic column preparation applied to
419
the separation of acetylsalicylic acid and related impurities [101]. Similarly, a response surface study based
420
on CCD allowed the evaluation of the effects of the polymerization mixture composition on methacrylate-
421
based monolithic columns for CEC and pressure assisted CEC (p-CEC). The mixture composition affects the
422
structure and as a consequence the chromatographic properties of the stationary phase; thus, the influence of
423
the ratio of the pore-forming solvents (mixture of water, 1,4-butanediol and 1-propanol) and the 1,4-
424
butanediol percentage in the mixture, were evaluated. Seven very different compounds (warfarin, ketoprofen,
425
praziquantel, paracetamol, metoprolo, pyrene, oxazepam) were used to prepare test samples and the
426
responses included retention time, theoretical plate number, peak asymmetry and retention factor. The
427
advantageous properties of the stationary phase identified by DoE were demonstrated [102].
Ac
ce pt
ed
M
an
us
cr
ip t
401
428
Page 18 of 62
17
429
6.
Capillary electrophoresis – mass spectrometry (CE-MS)
430 The electrospray ionization source (ESI) is well-suited for CE-MS interfacing since it produces ions
432
directly from liquid solutions at atmospheric pressure. Some of the limitations are related to buffer
433
composition and on the necessity for introduction of “makeup” liquid (sheath flow) to support the ionization.
434
The specific parameters to be optimized in hyphenation with ESI mode include electrospray voltage,
435
nebulizing gas flow rate, drying gas flow rate and temperature. In addition, the composition of the coaxial
436
makeup liquid and its flow rate, significantly affect the ionization efficiency and have to be appropriately
437
selected for good coupling performances. The most common buffers used for a conventional electrophoretic
438
separations such as borate and phosphate are not suitable for CE coupling with ESI-MS because of their poor
439
volatility and the risk of MS source contamination. More suitable buffers are based on formic and acetic
440
acid, ammonium carbonate and ammonium acetate [103,104].
an
us
cr
ip t
431
Martin et al. presented an interesting work on the optimization of a CE-MS method for peptide
442
analysis, including hepcidin. First, a mixture of model compounds was used, optimizing the conditions by
443
univariate approach to resolve a peptide test mixture by keeping into account the necessity of using a volatile
444
BGE. In the second step, the intensity of hepcidin signal was considered as the response, and a 18-run
445
asymmetric screening matrix was used for studying 8 factors at 2 levels and 1 factor at 3 levels. Seven
446
factors were quantitative (capillary voltage, applied temperature, nebulizing gas pressure, drying gas flow
447
rate, organic modifier concentration, volatile acid concentration and BGE concentration) and two were
448
qualitative (nature of the organic modifier and of the volatile acid present in the sheath liquid). The
449
significant factors (capillary voltage, applied temperature, buffer concentration and pressure of nebulizing
450
gas) were studied in a more in-depth way by a response surface study carried out by a CCD [105].
Ac
ce pt
ed
M
441
451
The wide opportunity for selectivity (and enantioselectivity) tuning by MEKC, can be exploited in
452
CE-MS hyphenation using molecular micelles because of their compatibility with the use of high organic
453
solvents percentage and volatile buffers; in addition other important features of molecular micelles include
454
zero cmc as well as their poor ionization that reduces ion suppression providing improved S/N in ESI [106].
455
He et al. presented detailed optimizations of MEKC-ESI-MS methods either in negative and positive ion
456
mode for the chiral separation of binaphthyl derivatives using a polymeric surfactant as a PSP [76,77]. Wang
Page 19 of 62
18 et al. reported the multivariate optimization of a MEKC-MS method for the chiral analysis of barbiturates
458
[78]. In all these studies RSM was used to identify the optimal conditions for both separation and MS
459
detection. For the chiral separation of binaphthyl derivatives the factors estimated were buffer pH, ACN
460
percentage, concentration of surfactant, concentration of ammonium acetate and voltage, considering as the
461
responses resolution and analysis time [77], also by applying desirability function [76]. In general it was
462
found that the most important factors affecting enantioresolution and method performance were pH and
463
nebulizer pressure (the latter affects enantioseparation because of the buffer suction effect that reduces the
464
laminar flow in capillary).
cr
ip t
457
466
7.
Critical discussion
7.1.
Selection of the variables
us
465
M
468
an
467
The accurate selection of the variables plays a key role for the success of the optimization, thus the
470
attention should be focused on all the factors that potentially affect CE analysis (both separation and
471
detection). The researcher can be tempted to reduce the number of variables considered in the multivariate
472
study in order to reduce the number of planned experiments and to obtain models/results which at a first
473
sight may seem more easily interpretable. Anyway, the selection should be carefully based on preliminary
474
experiments or knowledge and not on the basis of researcher’s convenience. In general, carrying out heavy
475
univariate studies with the aim of applying experimental design to a limited number of factors [107] should
476
be avoided.
Ac
ce pt
ed
469
477
A further critical procedure is to divide the potentially influent factors in two separated designs [27]:
478
in this way, the interactions between the two sets of factors are missing. In order to overcome this issue, it is
479
advisable to proceed with a screening step for identifying influent factors on the response and then with a
480
response surface study in order to obtain comprehensive information on all the considered factors.
481
A particular case where factors under study are divided is represented by a MEEKC system where
482
both mixture components (MCs) and process variables (PVs) have to be optimized. A plain strategy can be
483
applied to optimize first the MCs by a mixture design, and then the PVs with a response surface study
484
[79,94,95,108]. Nevertheless, recently Piepel et al. [96] demonstrated the occurrence of important Page 20 of 62
19 485
interactions among MCs and PVs, evidenced by the use of MPV approach (Fig. 6). When applying MPV the
486
number of experiments rapidly increases and the statistical tools for interpreting data are more complex.
487
Consequently, the researcher can weight advantages and issues involved in the analytical problem under
488
study in order to decide between the application of sequential mixture design and RSM or MPV approach. Finally, injection time, unless involved in identification of conditions for on-line preconcentration
490
[32,44,87], should preferably not be included among the CE parameters [35,40,83,109]. Instead of focusing
491
on this parameter, the researcher should preferably identify the level of concentration of test sample
492
according to the objective of the analysis (e.g., the LOQ in impurity assay should be at least 0.1% with
493
respect to the main drug) [38,79,94,95].
us
cr
ip t
489
494
7.2.
Selection of the experimental domain
an
495
The selection of the experimental domain is a very critical step in the multivariate optimization and
497
should be carefully made by the researcher. As a matter of fact, DoE is able to provide information only
498
inside the investigated experimental domain. Moreover, the selection of a suitable experimental range of
499
each parameter under study, is necessary for maintaining all the planned experimental runs within acceptable
500
conditions of electric current, baseline noise, analysis time and prevention of additives precipitation in the
501
buffer. Before running the experiments, one should practically check that the established conditions might be
502
performed within the limits of the experimental domain [1]. This is a valuable way of operating which could
503
be included in best DoE practices, as it can help to avoid an experimental design to be broken off for
504
practical/technical reasons.
Ac
ce pt
ed
M
496
505
The complexity of the effects of pH in CE separation makes the selection of its experimental domain
506
very critical. As mentioned, the running buffer pH strongly affects the CE separation because of the effects
507
on the EOF mobility, electrophoretic mobility of the analytes as well as those of the ionic additives of the
508
BGE (e.g., chargeable cyclodextrins), that can be supplemented for selectivity tuning [54-56]. A preliminary
509
study on pH effect on separation should be carried out before application of multivariate optimization in
510
order to avoid, if possible, peak collapsing and inversion order of the peaks when running the experimental
511
plan in response surface studies. Moreover, the direct study of the behavior of the responses in a wide pH
512
range with a RSM study might be difficult in modelling with simple regression models. For this reason, a
Page 21 of 62
20 513
convenient procedure consists in the use of asymmetrical or symmetrical screening matrices that allow pH,
514
and if necessary also other factors, to be studied at a number of levels higher than 2 [110,111].
In more complex systems, a detailed theoretical and/or experimental work is required as it was
516
shown by Mofaddel et al. [57]. In order to select the pH range for chiral separation of binaphtol derivatives
517
using neutral CDs as chiral selectors, the pKa values of the synthesized compounds under study were first
518
determined. A detailed univariate investigation made it possible to find a double inversion in the enantiomer
519
migration order, as a function of the pH and of the concentration of the chiral selector [57]. In analogy, the
520
study of the mobility evolution of mixtures of drugs as a function of pH was found to be useful in identifying
521
the suitable pH range for CZE separation optimization [28,29]. Then, according to the obtained results, either
522
multivariate strategy was applied in a restricted pH range or the value of pH was directly set [39,112].
us
cr
ip t
515
In addition, the BGE system exhibits a proper buffering capacity at pH = pKa±1. In a number of
524
reports [52,83,107,113-116], this assumption was not followed and common buffers such as phosphate,
525
borate or acetate were reported to be used in an experimental domain out of the optimum pH range. In order
526
to extend the buffer capacity of the BGE, within the whole explored experimental domain, the use of a
527
combination of different electrolytes (for instance, phosphate-borate mixture, or Britton-Robinson buffer)
528
with a wider buffering pH range would be advisable.
ed
M
an
523
When dealing with enantioseparations, the lower level of the chiral selector should not to be set equal
530
to zero, because obviously the separation of the enantiomers does not take place in the absence of a chiral
531
selector. In a study on the chiral separation of a quinolizinium compound possessing two chiral centers (the
532
anthiarrhythmic RS86017), solfobutyl ether CD was used as the chiral selector and the experimental range
533
of concentration was set within 0-24 mg/mL [53]. Although some of the runs of the design were carried out
534
in the absence of chiral selector, surprisingly a nonzero resolution was reported, suggesting that misleading
535
conclusions were drawn.
Ac
ce pt
529
536
When evaluating the possibility of adding a PSP forming micelles for analytes partitioning, the
537
experimental domain should be selected according to the cmc of the surfactant. For example SDS has a
538
reported cmc of about 8 mM in water at 25°C [117]; thus, setting the experimental range within 0-10 mM,
539
even though in the presence of an additional surfactant (i.e., Brij 35 within 0 – 5 mM) [40], is misguiding for
540
establishing if MEKC can be the suitable approach for the intended purpose.
Page 22 of 62
21 541
As for capillary temperature, if no specific concerns are involved in the analysis (sample stability,
542
buffer viscosity etc..) temperature higher than 30° C would be better not included in the experimental plan.
543
This factor should be limited to values that in practice can be easily reached and maintained by the
544
instrument cooling system. If very wide ranges have been set (15-45 °C) [118], these should be adequately
545
motivated.
7.3.
Selection of the responses
cr
547
ip t
546
In the optimization of CE analysis, the aspects regarding the selectivity and efficiency of the
549
separation (selectivity as the ratio of electrophoretic mobilities, resolution, number of peaks) and analysis
550
time, should be primarily taken into consideration, together with responses related to peak detection (peak
551
height, peak area). According to the target, these responses can be singularly considered, or simultaneously
552
optimized by comparing the related response surfaces and/or calculated model coefficients or by using
553
efficient multicriteria optimization tools such as desirability function [10] or Pareto optimization [11]. The
554
examination of the response surfaces for directly finding the optimal conditions may be immediate when
555
dealing with a limited number of factors and responses; on the other hand, it may become a difficult task
556
when more than two factors and more than two responses, which can be conflicting in nature, have to be
557
simultaneously considered. In these situations, description of the surfaces and highlight of particular points
558
could be of some interest, even though application of multiresponse optimization criteria should be
559
recommended.
ce pt
ed
M
an
us
548
A further possible approach is the use of integrated responses reflecting the quality of the
561
electropherogram, which mainly take into account selectivity and analysis time, by merging the different
562
responses into one. The drawback of these methods is that a modeling of the different responses is missing;
563
on the other side, the simpler handling of the statistical treatment of cumulated responses, make them
564
advantageous. Chromatographic exponential function (CEF) was introduced by Morris et al. [119] and used
565
in Refs. [26,85,86]. Chromatographic response function (CRF), proposed by Berridge [120], and the
566
modified versions [121] were successfully employed as response in Refs. [29,81,122]. Finally,
567
chromatography resolution statistic function (CRS function), proposed by Schlabach et al. [123], was used in
Ac
560
Page 23 of 62
22 568
Refs. [30,31,40,59,66]. The modified chromatographic exponential function MCEF, defined by Morris et al.
569
[124], was employed in Ref. [125].
570
In principle, any selected response is good if the final analysis fits to the scope according to the
571
requirements defined by researcher. Anyway, some particular issues on the selection of responses deserve to
572
be pointed out. When having to optimize selectivity, the peak area/height and related RSD values are often introduced
574
among the responses under study [44,107,109,113,126,127]. However, it is well known that to obtain good
575
repeatability in capillary electrophoresis, the use of an internal standard is strictly necessary and this
576
guidance is recommended for correcting the variability in the injection volume. The ratio between resolution
577
and analysis time (Rs/t), was reported as one of the response in the optimization of a MEKC method for the
578
determination of nicotinic acid and nicotinamide [83] and in a CZE method for the determination of honokiol
579
and magnolol [112]. The aim of the optimizations was to maximize the selected response; anyway, a
580
weakness of this choice is in the lack of information on the effect of the factors on each of the separated
581
values of Rs and t, with the consequence that it is not possible to understand from the measured value if
582
baseline resolution is achieved. Although a valuable reason for this choice could be that the quality obtained
583
by modeling the proposed combined response was found to be higher than those for the separated resolution
584
and analysis time, the selection of Rs/t should be better motivated. Analogous criticism may be reported
585
when choosing as a response the sum of enantioresolution values for the simultaneous separation of two
586
couples of enantiomers, as it was reported in the paper by Liu et al.[53]. In order to motivate the choice of
587
the sum of the enantioresolution as an effective response, it was verified that the two enantioresolution
588
values followed the same trend under variations of the selected parameters (i.e., chiral selector concentration,
589
buffer pH and concentration, organic solvent percentage, voltage and temperature). Anyway, in our opinion
590
the two resolution values should be better considered separately; as a matter of fact, some experimental runs
591
showed adequate separation for resolution of an enantiomer pair, but partial or complete lack of resolution
592
for the other one.
Ac
ce pt
ed
M
an
us
cr
ip t
573
593
In a study by Bailón Pérez et al., [128], the selected response was not clearly defined, as it was
594
mentioned to be a multiple response function represented by the combination of the efficiency of nine
595
antibiotics peaks. The aim of the experimental design was the complete resolution of the analytes in short
Page 24 of 62
23 analysis time; however, high efficiency does not necessarily correspond to good resolution nor to short
597
analysis time. Similarly, the selection of the analysis times of the individual solutes as the response [129]
598
should be discarded in favor of the critical resolution between adjacent peaks. In this way immediate
599
information about selectivity is gained and complicated comparison of the obtained response surfaces is
600
avoided. Analogously, the selection of peak areas [127] or effective mobility [130] as the unique responses,
601
should be better integrated by data related to the separation of the peaks. It is worthwhile to note that in the
602
two latter reported studies, the experimental design was applied to evaluate the effect of some factors around
603
optimal conditions identified by preliminary univariate approach.
cr
ip t
596
Recently, we have reported the challenging separation of the proline-derived drug ramipril in the
605
presence of related substances. Since the cis-trans isomerization of the proline-moiety containing compounds
606
occurred in the time scale of the CE run, in this analysis the electrophoretic peaks belonging to cis and trans
607
conformers were resolved. However, a baseline separation could not be obtained because the interpeak
608
reaction zone, corresponding to the mixture of both the isomers, migrated as a plateau band between the two
609
peaks (Fig. 1a) [79]. In order to minimize this interference and to achieve the complete separation, a DoE
610
was developed by considering as the effective response the ratio Rh, calculated between the height of the
611
interpeak reaction zone and the height of the peak corresponding to the faster migrating conformer [79].
ed
M
an
us
604
Finally, it is important to underline that if responses are correlated, the use of partial least square
613
regression allows a single regression model to be obtained making easier the data interpretation without the
614
need to find new types of overall criteria [131].
615 616
7.4.
Ac
ce pt
612
Selection of the experimental matrix and statistical treatment of the data
617
In order to obtain reliable results from an experimental design strategy, another important aspect to
618
take into account is the selection of a suitable experimental matrix for the postulated regression model, i.e.,
619
the selection of a matrix able to give an accurate estimation of model coefficients. Each researcher should be
620
able to select the better experimental matrix for the regression model postulated on the basis of quality
621
criteria [1]. Other simple approaches guided by the software should be followed with care.
622
If the experimental matrix is not suitable for the regression model, analysis of variance could give
623
poor results. As stated in the introduction, ANOVA is useful in order to verify significativity and validity of
Page 25 of 62
24 the postulated model. In particular, if the model is significant and valid it can be used in a predictive way.
625
However, in presence of poor quality criteria it will be difficult that the regression model passes ANOVA. In
626
addition, in the case of RSM, a final validation of model predictivity, evaluating that the predicted and the
627
measured results are in agreement, is necessary [108]. This step involves both the uncertainty in the
628
measured response, assumed constant inside the experimental domain, and the variance function. The
629
variance function in turn depends on the point coordinates and on the dispersion matrix, that is strictly
630
correlated to the experimental matrix and the postulated regression model [1]. These few but necessary steps
631
are still often neglected; instead they should be reported as they are important in order to investigate and
632
analyze the results. Some particular issues can be found in the literature: for instance, with two-level factorial
633
designs it is possible to calculate the coefficients only for a linear regression model; as a consequence the
634
drawn response surface cannot show a curvature [107,126]. The response surfaces should describe the real
635
behavior of the response in the investigated experimental domain. If two or more responses are combined in
636
an overall criteria, it is important at the same time to obtain coherent information on the primary responses.
637
For instance, if the migration times of two analytes show the same trend, with the same zones of maximum
638
and minimum, it is questionable that the difference between these two responses also shows the same trend,
639
and a revision of the data would be advisable [132].
ed
M
an
us
cr
ip t
624
After having verified the quality of the models, the analysis of the coefficients and/or the analysis of
641
the response surfaces can be performed. Anyway, also this type of study should be conducted critically. The
642
general expected trend for the classical considered responses and factors (for instance, an increase of voltage
643
leads to a decrease of analysis time, an increase of buffer concentration leads to an increase of migration
644
time) can represent a useful, immediate check of the carried experimentation.
Ac
ce pt
640
645 646
7.5.
Selection of the target value for the responses
647
One of the main advantages of a multivariate approach is the possibility of simultaneously optimizing
648
several responses by dedicated tools such as Pareto analysis [11] and desirability function [10] if the
649
responses are not correlated. For this reason, it is not necessary to define the target values for a response by
650
considering a priori that other values for the responses could lead to deleterious effects for a different
651
response. In this sense, some Authors set a limited range for optimum resolution values by motivating this
Page 26 of 62
25 652
choice by the fact that the greater Rs, the longer analysis time [115,133]. This statement can be in principle
653
shared, but the problem of conflicting responses can be directly taken into account and successfully
654
overcome by multicriteria decision tools. At the same time, the change of internationally accepted target values for some responses such as
656
resolution, should be motivated. For instance, resolution values lower than 1.5 cannot be accepted in the case
657
of simple separation between an analyte and the internal standard [118].
ip t
655
In some reports, different target values for resolution of different peak pairs have been set
659
[56,118,134]; it should be pointed out that when a minimum target for peak resolution is set, then the same
660
target is recommended for each pair of adjacent peaks, whereas other choices should be adequately
661
motivated. The unconcerned maximization of resolution may limit the number of combination of factors that
662
could provide good resolution values for the other peak pairs. This aspect should be especially taken into
663
account in pharmaceutical applications according to Quality by Design (QbD) approach, because finding a
664
single optimum point is no longer sufficient since it is necessary to define a global optimum zone [135].
M
an
us
cr
658
665
7.6.
Selection of the optimal conditions
ed
666
In the overwhelming majority of the considered examples, an optimum corresponding to a single set of
668
experimental conditions was found. However, in the case of pharmaceutical applications, according to the
669
QbD principles [135] it would be more appropriate to find a design space, which corresponds to the
670
multidimensional combination of parameters fulfilling the quality requirements. This was made only in one
671
example referring to the determination of Q10 in a nutraceutical preparation [96].
Ac
ce pt
667
672
One of the major advantages of response surfaces study is that if the model passes the ANOVA, then it
673
can be used to predict the considered responses in any point of the experimental domain, and this is the
674
strategy that should be usually followed. In other words, the experimental plan should not be simply
675
considered as a set of conditions to be chosen from, as sometimes it can be found, unless this is strictly
676
necessary. This procedure lacks of interest as the calculated models in RSM can optimize the values of
677
significant parameters for achieving the best response inside the experimental domain. If unfortunately the
678
model doesn’t pass ANOVA, an acceptable strategy could be to choose the best run of the first design as
Page 27 of 62
26 679
center point of another design [79], or to use desirability function to select another experimental range to be
680
studied by another design [38].
681 682
8.
Conclusion
ip t
683 This critical review pointed out that the use of experimental design is very diffuse for the
685
optimization of electromigration methods. Anyway, a correct use of experimental design is not presented in
686
all of the considered papers. Starting from this consideration, it is important to keep in mind that a deep
687
understanding of the statistical tools involved in the use of experimental design should be always present
688
behind the use of dedicated software. At the same time, planning an effective multivariate strategy requires
689
basic knowledge of the characteristics of the electrophoretic system and of the analytes under study.
690
Experimental design is a methodology for experimental research and a way of reasoning that requires
691
following the simple advices given in the introduction and in the critical discussion in order to obtain high
692
quality of information. It is the researcher’s responsibility to plan and follow a correct strategy for achieving
693
an in-depth comprehension of the problem. In addition, the growing complexity of the data to be handled by
694
the analytical researcher needs a rigorous multivariate approach in order to obtain reliable results. The
695
application of experimental design as analytical tool is strongly recommended, anyway at the same time the
696
updating of the researcher is also strongly encouraged in order to avoid pitfalls and wrong conclusions given
697
to the research community.
ce pt
ed
M
an
us
cr
684
699
Ac
698
References
700 701
[1]
702 703
G. Lewis, D. Mathieu, R. Phan-Tan-Luu, Pharmaceutical Experimental Design, Marcel Dekker, New York, 1999.
[2]
S. Wold, C. Albano, W.J. Dunn III, K. Esbensen, S. Hellberg, E. Johansson, W. Lindberg, M.
704
Sjöström, Modelling data tables by principal components and PLS: class patterns and quantitative
705
predictive relations, Analusis 12 (1984) 477-485.
Page 28 of 62
27 706
[3]
707 708
Handbook of Chemometrics and Qualimetrics: Part A, Elsevier, Amsterdam, 1997. [4]
709 710
D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke,
B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, Amsterdam, 1998.
[5]
M. Forina, R. Leardi, C. Armanino, S. Lanteri, P. Conti, P. Princi, PARVUS: An extendable package of programs for data exploration, classification and correlation, ElsevierScience Software,
712
Amsterdam, 1988, ISBN:0-444-43012-1.
ip t
711
[6]
B.R. Kowalski, Measurement analysis by pattern recognition, Anal. Chem. 47 (1975) 1152A-1162A.
714
[7]
S. Wold, Chemometrics; what do we mean with it and what do we want from it, Chemometr. Intell. Lab. 30 (1995) 109-115.
us
715
cr
713
[8]
http://www.umetrics.com/ Accessed 20 Feb 2013.
717
[9]
http://www.nemrodw.com/ Accessed 20 Feb 2013.
718
[10]
G. Derringer, R. Suich, Simultaneous optimization of several response variables, J. Qual. Technol. 12 (1980) 214-219.
721 722
D. Gétaz, G. Stroehlein, A. Butté, M. Morbidelli, Model-based design of peptide chromatographic
ed
[11]
purification processes, J. Chromatogr. A 1284 (2013) 69-79. [12]
S.L.C. Ferreira, R.E. Bruns, H.S. Ferreira, G.D. Matos, J.M. David, G.C. Brandão, E.G.P. da Silva,
ce pt
720
M
719
an
716
723
L.A. Portugal, P.S. dos Reis, A.S. Souza, W.N.L. dos Santos, Box-Behnken design: An alternative
724
for the optimization of analytical methods, Anal. Chim. Acta 597 (2007) 179-186. [13]
726 727
methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76 (2008) 965-977. [14]
728 729
732
F. Bianchi, M. Careri, Experimental design techniques for optimization of analytical methods. Part I: Separation and sample preparation techniques, Curr. Anal. Chem. 4 (2008) 55-74.
[15]
730 731
M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response surface
Ac
725
B. Dejaegher, Y. Vander Heyden, Experimental designs and their recent advances in set-up, data interpretation, and analytical applications, J. Pharm. Biomed. Anal. 56 (2011) 141-158.
[16]
A.M. Siouffi, R. Phan-Tan-Luu, Optimization methods in chromatography and capillary electrophoresis, J. Chromatogr. A 892 (2000) 75-106.
Page 29 of 62
28 733
[17]
734 735
J.-L. Veuthey, S. Rudaz, Statistical and chemometric tools applied to pharmaceutical analysis, Chimia 59 (2005) 326-330.
[18]
S.L. Costa Ferreira, R.E. Bruns, E.G. Paranhos da Silva, W.N. Lopes dos Santos, C.M Quintella, J.M. David, J. Bittencourt de Andrade, M.C. Breitkreitz, I.C. Sales Fontes Jardim, B.B. Neto,
737
Statistical designs and response surface techniques for the optimization of chromatographic systems,
738
J. Chromatogr. A 1158 (2007) 2-14. [19]
740 [20]
742 743
K.D. Altria, B.J. Clark, S.D. Filbey, M.A. Kelly, D.R. Rudd, Application of chemometric experimental designs in capillary electrophoresis: A review, Electrophoresis 16 (1995) 2143-2148.
[21]
744
S. Sentellas, J. Saurina, Chemometrics in capillary electrophoresis. Part A: Methods for optimization, J. Sep. Sci. 26 (2003) 875-885.
[22]
G. Hanrahan, R. Montes, F.A. Gomez, Chemometric experimental design based optimization
M
745
cr
(2012) 2-13.
us
741
D.B. Hibbert, Experimental design in chromatography: A tutorial review, J. Chromatogr. B 910
an
739
ip t
736
techniques in capillary electrophoresis: A critical review of modern applications, Anal. Bioanal.
747
Chem. 390 (2008) 169-179. [23]
749
optimization of chiral CE or CEC separations: an overview, Method. Mol. Biol. 940 (2013) 409-427. [24]
751 752
ce pt
750
B. Dejaegher, D. Mangelings, Y. Vander Heyden, Experimental design methodologies in the
G.M. Janini, H.J. Issaq, The buffer in capillary zone electrophoresis, in: N.A. Guzman (Ed.), Capillary Electrophoresis Technology, Marcel Dekker Inc., New York, 1993, pp. 119-160.
[25]
S. Terabe, T. Yashima, N. Tanaka, M. Araki, Separation of oxygen isotopic benzoic acids by
Ac
748
ed
746
753
capillary zone electrophoresis based on isotope effects on the dissociation of the carboxyl group,
754
Anal. Chem. 60 (1988) 1673-1677.
755
[26]
L.M. Swann, S.L. Forbes, S.W. Lewis, A capillary electrophoresis method for the determination of
756
selected biogenic amines and amino acids in mammalian decomposition fluid, Talanta 81 (2010)
757
1697-1702.
758 759
[27]
F.A. Simas Vaz, P.A. da Silva, L.Paixão Passos, M. Heller, G.A. Micke, A.C. Oliveira Costa, M.A. Leal de Oliveira, Optimisation of a capillary zone electrophoresis methodology for simultaneous
Page 30 of 62
29 760
analysis of organic aliphatic acids in extracts of Brachiaria brizantha, Phytochem. Anal. 23 (2012)
761
569-575.
762
[28]
M.C. Vargas Mamani, J. Amaya-Farfan, F.G. Reyes Reyes, J.A. Fracassi da Silva, S. Rath, Use of
763
experimental design and effective mobility calculations to develop a method for the determination of
764
antimicrobials by capillary electrophoresis, Talanta 76 (2008) 1006-1014. [29]
K.F. Johns, M.C. Breadmore, R. Bruno, P.R. Haddad, Evaluation of Peakmaster for computer-aided
ip t
765
multivariate optimization of a CE separation of 17 antipsychotic drugs using minimal experimental
767
data, Electrophoresis 30 (2009) 839-847. [30]
X. Li, D. Zhu, T. You, Simultaneous analysis of six cardiovascular drugs by capillary electrophoresis
us
768
cr
766
coupled with electrochemical and electrochemiluminescence detection, using a chemometrical
770
optimization approach, Electrophoresis 32 (2011) 2139-2147. [31]
772 773
D. Zhu, X. Li, J. Sun, T. You, Chemometrics optimization of six antihistamines separations by capillary electrophoresis with electrochemiluminescence detection, Talanta 88 (2012) 265-271.
[32]
M
771
an
769
C.-C. Lu, Y.-J. Jong, J. Ferrance, W.-K. Ko, S.-M. Wu, On-line sample stacking and short-end injection CE for the determination of fluoxetine and norfluoxetine in plasma: Method development
775
and validation using experimental designs, Electrophoresis 28 (2007) 3290-3295. [33]
J. Schappler, A. Staub, J.-L. Veuthey, S. Rudaz, Highly sensitive detection of pharmaceutical
ce pt
776
ed
774
777
compounds in biological fluids using capillary electrophoresis coupled with laser-induced native
778
fluorescence, J. Chromatogr. A 1204 (2008) 183-190. [34]
H. Liu, Y. Wen, Y. Gao, Application of experimental design and radial basis function neural network
Ac
779 780
to the separation and determination of active components in traditional Chinese medicines by
781
capillary electrophoresis, Anal. Chim. Acta 638 (2009) 88-93.
782
[35]
L. Vera-Candioti, A.C. Olivieri, H.C. Goicoechea, Simultaneous multiresponse optimization applied
783
to epinastine determination in human serum by using capillary electrophoresis, Anal. Chim. Acta 595
784
(2007) 310-318.
785 786
[36]
R. Gotti, Capillary electrophoresis of phytochemical substances in herbal drugs and medicinal plants, J. Pharm. Biomed. Anal. 55 (2011) 775-801.
Page 31 of 62
30 787
[37]
C.A. Ballus, A.D. Meinhart, R.E. Bruns, H.T. Godoy, Use of multivariate statistical techniques to
788
optimize the simultaneous separation of 13 phenolic compounds from extra-virgin olive oil by
789
capillary electrophoresis, Talanta 83 (2011) 1181-1187.
790
[38]
S. Furlanetto, S. Orlandini, I. Giannini, G. Beretta, S. Pinzauti, Pitfalls and success of experimental design in the development of a mixed MEKC method for the analysis of budesonide and its
792
impurities, 30 (2009) 633-643.
793
[39]
ip t
791
S. Orlandini, I. Giannini, S. Pinzauti, S Furlanetto, Multivariate optimisation and validation of a capillary electrophoresis method for the analysis of resveratrol in a nutraceutical, Talanta 74 (2008)
795
570-577. [40]
us
796
cr
794
R.G. Peres, G.A. Micke, M.F.M. Tavares, D.B. Rodriguez-Amaya, Multivariant optimization, validation, and application of capillary electrophoresis for simultaneous determination of
798
polyphenols and phenolic acids in Brazilian wines, J. Sep. Sci. 32 (2009) 3822-3828. [41]
S. Furlanetto, S. Lanteri, S. Orlandini, R. Gotti, I. Giannini, S. Pinzauti, Selection of background
M
799
an
797
electrolyte for CZE analysis by a chemometric approach. Part I. Separation of a mixture of acidic
801
non-steroidal anti-inflammatory drugs, J. Pharm. Biomed. Anal. 43 (2007) 1388-1401.
802
[42]
ed
800
S. Furlanetto, S. Lanteri, S. Orlandini, R. Gotti, I. Giannini, S. Pinzauti, Selection of background electrolyte for CZE analysis by a chemometric approach. Part II. Separation of a mixture of basic
804
beta-blocker drugs, J. Pharm. Biomed. Anal. 43 (2007) 1402-1408.
805
[43]
ce pt
803
P. Schmitt-Kopplin, N. Hertkorn, A.W. Garrison, D. Freitag, A. Kettup, Influence of borate buffers on the electrophoretic behavior of humic substances in capillary zone electrophoresis, Anal. Chem.
807
70 (1998) 3798-3808.
808
[44]
Ac
806
I. Oita, H. Halewyck, S. Pieters, B. Dejaegher, B. Thys, B. Rombaut, Y. Vander Heyden, Rational
809
use of stacking principles for signal enhancement in capillary electrophoretic separations of
810
poliovirus samples, J. Pharm. Biomed. Anal. 55 (2011) 135-145.
811
[45]
812 813 814
E. Kenndler, Organic solvents in capillary electrophoresis, in: N.A. Guzman (Ed.), Capillary Electrophoresis Technology, Marcel Dekker Inc., New York, 1993, pp. 161-186.
[46]
C. Schwer, E. Kenndler, Electrophoresis in fused-silica Capillaries: the influence of organic solvents on the electroosmotic velocity and the zeta potential, Anal. Chem. 63 (1991) 1801-1807.
Page 32 of 62
31 815
[47]
F. Ronda, J.M. Rodríguez-Nogales, D. Sancho, B.O.yM. Gómez, Multivariate optimisation of a
816
capillary electrophoretic method for the separation of glutenins. Application to quantitative analysis
817
of the endosperm storage proteins in wheat, Food Chem. 108 (2008) 287-296.
818
[48]
819
T. Cserháti, New applications of cyclodextrins in electrically driven chromatographic systems: a review, Biomed. Chromatogr. 22 (2008) 563–571.
[49]
S. Fanali, Chiral separations by CE employing CDs, Electrophoresis 30 (2009) S203–S210.
821
[50]
G. Gübitz, M.G. Schmid, Chiral separation by capillary electromigration techniques, J. Chromatogr.
822 [51]
cr
A 1204 (2008) 140–156.
A. Rousseau, P. Chiap, R. Oprean, J. Crommen, M. Fillet, A.-C. Servais, Effect of the nature of the
us
823
ip t
820
single-isomer anionic CD and the BGE composition on the enantiomeric separation of β-blockers in
825
NACE, Electrophoresis 30 (2009) 2862-2868.
826
[52]
an
824
G. Neumajer, T. Sohajda, A. Darcsi, G. Tóth, L. Szente, B. Noszál, S. Béni, Chiral recognition of dapoxetine enantiomers with methylated-gamma-cyclodextrin: A validated capillary electrophoresis
828
method, J. Pharm. Biomed. Anal. 62 (2012) 42-47. [53]
M. Liu, Y. Zheng, Y. Ji, C. Zhang, Development and validation of a capillary electrophoresis method
ed
829
M
827
for the enantiomeric purity determination of RS86017 using experimental design, J. Pharm. Biomed.
831
Anal. 55 (2011) 93-100.
832
[54]
ce pt
830
A.S. Fayed, S.A. Weshahy, M.A. Shehata, N.Y. Hassan, J. Pauwels, J. Hoogmartens, A. Van Schepdael, Separation and determination of clopidogrel and its impurities by capillary
834
electrophoresis, J. Pharm. Biomed. Anal. 49 (2009) 193-200.
835
[55]
Ac
833
Y.P. Zhang, Y. Jun Zhang, W. Jun Gong, S. Ming Wang, H. Yong Xue, K. Pill Lee, Design of
836
experiments for capillary electrophoretic enantioresolution of tamsulosin using sulfated-β-
837
cyclodextrin as chiral selector, J. Liq. Chromatogr. R. T. 30 (2007) 215-234.
838
[56]
K.B. Borges, M.T. Pupo, L.A.P. De Freitas, P.S. Bonato, Box-Behnken design for the optimization
839
of an enantioselective method for the simultaneous analysis of propranolol and 4-
840
hydroxypropranolol by CE, Electrophoresis 30 (2009) 2874-2881.
Page 33 of 62
32 841
[57]
N. Mofaddel, H. Krajian, D. Villemin, P.L. Desbène, Enantioseparation of binaphthol and its mono
842
derivatives by cyclodextrin-modified capillary zone electrophoresis, J. Chromatogr. A 1211 (2008)
843
142-150.
844
[58]
S. Orlandini, I. Giannini, R. Gotti, S. Pinzauti, E. La Porta, S. Furlanetto, Development of a CZE method for the determination of mizolastine and its impurities in pharmaceutical preparations using
846
response surface methodology, Electrophoresis 28 (2007) 395-405. [59]
P.-Y. Liu, Y.-H. Lin, C.H. Feng, Y.-L. Chen, Determination of hydroxy acids in cosmetics by
848
chemometric
849
Electrophoresis 33 (2012) 3079-3086. [60]
design
and
cyclodextrin-modified
capillary
electrophoresis,
us
850
experimental
cr
847
ip t
845
M.A. Martínez-Gómez, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Enantioseparation of phenotiazines by affinity electrokinetic chromatography using human serum
852
albumin as chiral selector - Application to enantiomeric quality control in pharmaceutical
853
formulations, Anal. Chim. Acta 582 (2007) 223-228. [61]
M
854
an
851
M.A. Martínez-Gómez, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Enantiomeric quality control of antihistamines in pharmaceuticals by affinity electrokinetic
856
chromatography with human serum albumin as chiral selector, Anal. Chim. Acta 592 (2007) 202-
857
209. [62]
ce pt
858
ed
855
M.A. Martínez-Gómez, L. Escuder-Gilabert, R.M. Villanueva-Camañas, S. Sagrado, M.J. MedinaHernández, Enantioseparation of nuarimol by affinity electrokinetic chromatography-partial filling
860
technique using human serum albumin as chiral selector, J. Sep. Sci. 31 (2008) 3265-3271.
861
[63]
862
Ac
859
L. Geiser, J.-L. Veuthey, Non-aqueous capillary electrophoresis 2005-2008, Electrophoresis 30 (2009) 36-49.
863
[64]
E. Kenndler, Organic solvents in CE, Electrophoresis 30 (2009) S101-S111.
864
[65]
M.L. Riekkola, M. Jussila, S.P. Porras, I.E. Valkó, Non-aqueous capillary electrophoresis, J.
865 866
Chromatogr. A 892 (2000) 155-170. [66]
X. Li, X. Xu, D.R. Albano, T. You, Optimization using central composite design for antihistamines
867
separation
868
electrochemiluminescence detections, Analyst 136 (2011) 5294-5301.
by
nonaqueous
capillary
electrophoresis
with
electrochemical
and
Page 34 of 62
33 869
[67]
870 871
F. Wang, M.G. Khaledi, Chiral separations by nonaqueous capillary electrophoresis, Anal. Chem. 68 (1996) 3460-3467.
[68]
A. Rousseau, F. Gillotin, P. Chiap, E. Bodoki, J. Crommen, M. Fillet, A.-C. Servais, Generic
872
systems for the enantioseparation of basic drugs in NACE using single-isomer anionic CDs, J.
873
Pharm. Biomed. Anal. 54 (2011) 154-159.
875
Chem. Rec. 8 (2008) 291-301.
877
Sons Ltd, Chichester, England, 2006. [71]
879 880
M. Silva, Micellar electrokinetic chromatography: A review of methodological and instrumental innovations focusing on practical aspects, Electrophoresis, 34 (2013) 141-158.
[72]
881 882
us
878
U. Pyell, Electrokinetic chromatography. Theory, instrumentation and applications, John Wiley &
cr
[70]
an
876
S. Terabe, Micellar electrokinetic chromatography for high-performance analytical separation,
ip t
[69]
R. Ryan, S. Donegan, J. Power, K. Altria, Advances in the theory and application of MEEKC, Electrophoresis 31 (2010) 755-767.
[73]
M
874
R. Ryan, K. Altria, E. McEvoy, S. Donegan, J. Power, A review of developments in the methodology and application of microemulsion electrokinetic chromatography, Electrophoresis 34 (2013) 159–
884
177. [74]
S. Yang, M.G. Khaledi, Chemical selectivity in micellar electrokinetic chromatography:
ce pt
885
ed
883
886
characterization of solute-micelle interactions for classification of surfactants, Anal. Chem. 67
887
(1995) 499-510. [75]
T. Galeano-Díaz, M.I. Acedo-Valenzuela, A. Silva-Rodríguez, Determination of tocopherols in
Ac
888 889
vegetable oil samples by non-aqueous capillary electrophoresis (NACE) with fluorimetric detection,
890
J. Food Compos. Anal. 25 (2012) 24-30.
891
[76]
J. He, S.A. Shamsi, Multivariate approach for the enantioselective analysis in micellar electrokinetic
892
chromatography-mass spectrometry I. Simultaneous optimization of binaphthyl derivatives in
893
negative ion mode, J. Chromatogr. A 1216 (2009) 845-856.
894 895
[77]
J. He, S.A. Shamsi, Multivariate approach for the enantioselective analysis in MEKC-MS: II. Optimization of 1,1'-binaphthyl-2,2 '-diamine in positive ion mode, J. Sep. Sci. 32 (2009) 1916-1926.
Page 35 of 62
34 896
[78]
B. Wang, J. He, S.A. Shamsi, A high-throughput multivariate optimization for the simultaneous
897
enantioseparation and detection of barbiturates in micellar electrokinetic chromatography-mass
898
spectrometry, J. Chromatogr. Sci. 48 (2010) 572-583.
899
[79]
S. Orlandini, R. Gotti, I. Giannini, B. Pasquini, S. Furlanetto, Development of a capillary electrophoresis method for the assay of ramipril and its impurities: An issue of cis-trans
901
isomerization, J. Chromatogr. A 1218 (2011) 2611-2617.
902
[80]
ip t
900
K. Yu, Y. Gong, Z. Lin, Y. Cheng, Quantitative analysis and chromatographic fingerprinting for the quality evaluation of Scutellaria baicalensis Georgi using capillary electrophoresis, J. Pharm.
904
Biomed. Anal. 43 (2007) 540-548. [81]
us
905
cr
903
J. Felhofer, G. Hanrahan, C.D. García, Univariate and multivariate optimization of the separation conditions for the analysis of five bisphenols by micellar electrokinetic chromatography, Talanta, 77
907
(2009) 1172–1178. [82]
X.-M. Fan, Y.-B. Ji, D.-N. Zhu, An integrated approach based on experimental designs for
M
908
an
906
fingerprint development of the complex herbal prescription Sheng-Mai-San by MEKC,
910
Chromatographia 71 (2010) 667-677.
911
[83]
ed
909
G. Mu, F. Luan, H. Liu, Y. Gao, Use of experimental design and artificial neural network in optimization of capillary electrophoresis for the determination of nicotinic acid and nicotinamide in
913
food compared with high-performance liquid chromatography, Food Anal. Methods 6 (2013) 191-
914
200. [84]
916 917
MEKC: Separation optimization using experimental design, J. Sep. Sci. 31 (2008) 3740-3748. [85]
918 919
V.J. Drover, C.S. Bottaro, Determination of pharmaceuticals in drinking water by CD-modified
Ac
915
ce pt
912
J.-F. Chiang, Y.-T. Hsiao, W.-K. Ko, S.-M. Wu, Analysis of multiple abused drugs and hypnotics in urine by sweeping CE, Electrophoresis 30 (2009) 2583-2589.
[86]
Y.-C. Cheng, C-C. Wang, Y.-L. Chen, S.-M. Wu, Large volume sample stacking with EOF and
920
sweeping in CE for determination of common preservatives in cosmetic products by chemometric
921
experimental design, Electrophoresis 33 (2012) 1443-1448.
Page 36 of 62
35 922
[87]
P. Anres, N. Delaunay, J. Vial, P. Gareil, A chemometric approach for the elucidation of the
923
parameter impact in the hyphenation of field-enhanced sample injection and sweeping in capillary
924
electrophoresis, Electrophoresis 33 (2012) 1169-1181. [88]
926 927
H. Nishi, T. Fukuyama, S. Terabe, Chiral separation by cyclodextrin-modified micellar electrokinetic chromatography, J. Chromatogr. 553 (1991) 503-516.
[89]
S. Terabe, Y. Miyashita, Y. Ishihama, O. Shibata, Cyclodextrin-modified micellar electrokinetic
ip t
925
chromatography: separation of hydrophobic and enantiomeric compounds, J. Chromatogr. 636
929
(1993) 47-55. [90]
C.L. Copper, M.J. Sepaniak, Cyclodextrin-modified electrokinetic capillary chromatography
us
930
cr
928
separations of benzopyrene isomers: correlation with computationally derived host-guest energies,
932
Anal. Chem. 66 (1994) 147-154.
933
[91]
an
931
E. Mileo, P. Franchi, R. Gotti, C. Bendazzoli, E. Mezzina, M. Lucarini, An EPR method for measuring the rate of distribution of organic substrate between cyclodextrin, micelles and water,
935
Chem. Comm. 11 (2008) 311-1313. [92]
C. Bendazzoli, E. Mileo, M. Lucarini, S. Olmo, V. Cavrini, R. Gotti, Capillary electrophoretic study
ed
936
M
934
on the interaction between sodium dodecyl sulfate and neutral cyclodextrins. Microchim. Acta 171
938
(2010) 23-31.
939
[93]
ce pt
937
H. Abromeit, A.M. Schaible, O. Werz, G.K.E. Scriba, Chemometrics-guided development of a cyclodextrin-modified micellar electrokinetic chromatography method with head-column field
941
amplified sample stacking for the analysis of 5-lipoxygenase metabolites, J. Chromatogr. A 1267
942
(2012) 217-223.
943
[94]
Ac
940
S. Orlandini, I. Giannini, M.V. Navarro, S. Pinzauti, S. Furlanetto, Dual CD system-modified
944
MEEKC method for the determination of clemastine and its impurities, Electrophoresis 31 (2010)
945
3296-3304.
946 947
[95]
I. Giannini, S. Orlandini, R. Gotti, S. Pinzauti, S. Furlanetto, Cyclodextrin-MEEKC for the analysis of oxybutynin and its impurities, Talanta 80 (2009) 781-788.
Page 37 of 62
36 948
[96]
G. Piepel, B. Pasquini, S. Cooley, A. Heredia-Langner, S. Orlandini, S. Furlanetto, Mixture-process
949
variable approach to optimize a microemulsion electrokinetic chromatography method for the quality
950
control of a nutraceutical based on coenzyme Q10, Talanta 97 (2012) 73-82. [97]
952
electrophoresis. Fundamentals and applications, J. Chromatogr. A 1184 (2008) 504-541.
954
electrokinetic chromatography, Science 282 (1998) 465-468. [99]
956 957
(1999) 1638-1644. [100]
958 959
J.P. Quirino, S. Terabe, Sweeping of analyte zone in electrokinetic chromatography, Anal. Chem. 71
cr
955
J.P. Quirino, S. Terabe, Exceeding 5000-fold concentration of dilute analytes in micellar
ip t
[98]
us
953
S.L. Jr. Simpson, J.P. Quirino, S. Terabe, On-line sample preconcentration in capillary
K.D. Bartle, P. Myers, Capillary Electrochromatography, Royal Society of Chemistry, Cambridge, 2001.
[101]
an
951
X. Wu, D. Mangelings, I. Oita, I. Tanret, C. Yan, Y. Vander Heyden, Capillary electrochromatographic testing of monolithic silica columns synthesized according to an
961
experimental design approach, J. Sep. Sci. 34 (2011) 2305-2313. [102]
I. Tanret, D. Mangelings, Y. Vander Heyden, Pressure-assisted CEC versus CEC using methacrylate-
ed
962
M
960
based monolithic columns: Influence of the polymerization-mixture composition, Electrophoresis 29
964
(2008) 4463-4474.
965
[103]
ce pt
963
W. Franklin Smyth, V. Rodriguez, Recent studies of the electrospray ionisation behaviour of selected
966
drugs
967
chromatography-mass spectrometry, J Chromatogr. A 1159 (2007) 159-174. [104]
969 970
application in capillary electrophoresis-mass
spectrometry and
liquid
Ac
968
and their
G.K. Scriba, Nonaqueous capillary electrophoresis-mass spectrometry, J. Chromatogr. A 1159 (2007) 28-41.
[105]
G.B. Martin, F. Mansion, A.-C. Servais, B. Debrus, E. Rozet, Ph. Hubert, J. Crommen, M. Fillet,
971
CE-MS method development for peptides analysis, especially hepcidin, an iron metabolism marker,
972
Electrophoresis 30 (2009) 2624-2631.
973 974
[106]
S.A. Shamsi, Micellar electrokinetic chromatography-mass spectrometry using a polymerized chiral surfactant, Anal. Chem. 73 (2001) 5103-5108.
Page 38 of 62
37 975
[107]
A. Alnajjar, H.H. AbuSeada, A.M. Idris, Capillary electrophoresis for the determination of
976
norfloxacin and tinidazole in pharmaceuticals with multi-response optimization, Talanta 72 (2007)
977
842-846.
978
[108]
S. Furlanetto, S. Orlandini, A.M. Marras, P. Mura, S. Pinzauti, Mixture design in the optimization of a microemulsion system for the electrokinetic chromatographic determination of ketorolac and its
980
impurities: Method development and validation, Electrophoresis 27 (2006) 805-818.
981
[109]
ip t
979
A.D. Meinhart, C.S. Bizzotto, C.A. Ballus, M.A. Prado, R.E. Bruns, J.T. Filho, H.T. Godoy, Optimisation of a CE method for caffeine analysis in decaffeinated coffee, Food Chem. 120 (2010)
983
1155-1161. [110]
us
984
cr
982
S. Furlanetto, S. Pinzauti, E. La Porta, A. Chiarugi, P. Mura, S. Orlandini, Development and validation of a differential pulse polarographic method for quinolinic acid determination in human
986
plasma and urine after solid-phase extraction: a chemometric approach, J. Pharm. Biomed. Anal. 17
987
(1998) 1015-1028. [111]
M
988
an
985
G. Pieraccini, S. Furlanetto, S. Orlandini, G. Bartolucci, I. Giannini, S. Pinzauti, G. Moneti, Identification and determination of mainstream and sidestream smoke components in different
990
brands and types of cigarettes by means of solid-phase microextraction-gas chromatography-mass
991
spectrometry, J. Chromatogr. A 1180 (2008) 138-150. [112]
ce pt
992
ed
989
P. Han, F. Luan, X. Yan, H. Liu, Separation and determination of honokiol and magnolol in chinese traditional medicines by capillary electrophoresis with the application of response surface
994
methodology and radial basis function neural network, J. Chromatogr. Sci. 50 (2012) 71-75.
995
[113]
Ac
993
A.O. Alnajjar, A.M. Idris, M.V. Attimarad, A.M. Aldughaish, R.E.E. Elgorashe, Capillary
996
electrophoresis assay method for metoprolol and hydrochlorothiazide in their combined dosage form
997
with multivariate optimization, J. Chromatogr. Sci. 51 (2013) 92-97.
998
[114]
999
electrophoresis separation of leucine enkephalin and its immune complex, J. Sep. Sci. 30 (2007)
1000 1001 1002
S.M.E. Babar, E.J. Song, M.N. Hasan, Y.S. Yoo, Experimental design optimization of the capillary
2311-2319. [115]
L. Song, J. Bai, W. Zhou, Determination of betamethasone and dexamethasone in human urine and serum by MEKC after an experimental design, Chromatographia 68 (2008) 287-293.
Page 39 of 62
38 1003
[116]
S.E. Vignaduzzo, L. Vera-Candioti, P.M. Castellano, H.C. Goicoechea, T.S. Kaufman, Multivariate
1004
optimization and validation of a CZE method for the analysis of pridinol mesylate and meloxicam in
1005
tablets, Chromatographia 74 (2011) 609-617. [117]
1007 1008
P. Mukerjee, K.J. Mysels, Critical micelle concentration of aqueous surfactant system, National Bureau of Standards, 1971, Washington.
[118]
M.M. Hefnawy, M.A. Sultan, H.I. Al-Johar, H.Y. Aboul-Enein, Multi-objective optimization
ip t
1006
strategy based on desirability functions used for electrophoratic separation and quantification of
1010
rosiglitazone and glimepiride in plasma and formulations, Drug Test. Anal. 4 (2012) 39-47.
cr
1009
[119]
V.M. Morris, J.G. Hughes, P.J. Marriott, J. Chromatogr. A 755 (1996) 235-243.
1012
[120]
J.C. Berridge, Unattended optimisation of reversed-phase high-performance liquid chromatographic
1013 [121]
1015 1016
an
separations using the modified simplex algorithm, J. Chromatogr. A 244 (1982) 1-14. B. Divjak, M. Moder, J. Zupan, Chemometrics approach to the optimization of ion chromatographic analysis of transition metal cations for routine work, Anal. Chim. Acta 358 (1998) 305-315. [122]
M
1014
us
1011
J. Lopez-Gazpio, R. Garcia-Arrona, M. Ostra, E. Millan, Optimization and validation of a nonaqueous micellar electrokinetic chromatography method for determination of polycyclic musks in
1018
perfumes, J. Sep. Sci. 35 (2012) 1344-1350. [123]
1020 1021
T.D. Schlabach, J.L. Excoffier, Multi-variate ranking function for optimizing separations, J.
ce pt
1019
ed
1017
Chromatogr. A 439 (1988) 173-184. [124]
V.M. Morris, C. Hargreaves, K. Overall, P.J. Mariott, J.G. Hughes, Optimization of the capillary electrophoresis separation of ranitidine and related compounds, J. Chromatogr. A 766 (1997) 245-
1023
254.
1024
[125]
Ac
1022
X. Liu, X. Chen, Application of experimental design in optimization of the separation condition for
1025
determination of four active components in cold medicines by flow injection-capillary
1026
electrophoresis, J. Chromatogr. Sci. 49 (2011) 142-147.
1027
[126]
A. Alnajjar, A.M. Idris, A.M., H.H. AbuSeada, Development of a stability-indicating capillary
1028
electrophoresis method for norfloxacin and its inactive decarboxylated degradant, Microchem. J. 87
1029
(2007) 35-40.
Page 40 of 62
39 1030
[127]
1031 1032
B.Y. Erdoğan, A.N. Onar, Determination of nitrates, nitrites and oxalates in kale and sultana pea by capillary electrophoresis, J. Anim. Vet. Adv. 10 (2011) 2051-2057.
[128]
M.I. Bailón Pérez, L.Cuadros Rodríguez, C. Cruces-Blanco, Analysis of different β-lactams
1033
antibiotics in pharmaceutical preparations using micellar electrokinetic capillary chromatography, J.
1034
Pharm. Biomed. Anal. 43 (2007) 746-752. [129]
Z. Vujic, V. Kuntic, B. Ivkovic, Statistical optimization applied to simultaneous determination of
ip t
1035
maprotiline, desipramine, and moclobemide by capillary zone electrophoresis, Monats. Chem. 139
1037
(2008) 81-87. [130]
I.M. Palabiyik, M.G. Caglayan, F. Onur, Multivariate optimization and validation of a CE method
us
1038
cr
1036
for simultaneous analysis of dorzolamide hydrochloride and timolol maleate in ophthalmic solution,
1040
Chromatographia 73 (2011) 541-548. [131]
Principles and applications, 3rd Ed., Umetrics Academy Umea, Sweden, 1998.
1042 1043
L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikstrom, S. Wols, Design of experiments:
[132]
M
1041
an
1039
M.A. Al-Ghobashy, M.A.K. Williams, D.R.K. Harding, Factors affecting the performance of capillary isoelectric focusing in dynamically coated capillaries using polyethylene oxide polymer,
1045
Anal. Lett. 41 (2008) 1914-1931. [133]
Y.-H- Lin, Y.-H. Yang, S.-M. Wu, Experimental design and capillary electrophoresis for
ce pt
1046
ed
1044
1047
simultaneous analysis of arbutin, kojic acid and hydroquinone in cosmetics, J. Pharm. Biomed. Anal.
1048
44 (2007) 279-282. [134]
A.D. Meinhart, C.A. Ballus, R.E. Bruns, J.A.L. Pallone, H.T. Godoy, Chemometrics optimization of
Ac
1049 1050
carbohydrate separations in six food matrices by micellar electrokinetic chromatography with
1051
anionic surfactant, Talanta 85 (2011) 237-244.
1052
[135]
1053 1054
S. Orlandini, S. Pinzauti, S. Furlanetto, Application of quality by design to the development of analytical separation methods, Anal. Bioanal. Chem. 405 (2013) 443-450.
[136]
L. Vera-Candioti, A.C. Olivieri, H.C. Goicoechea, Development of a novel strategy for
1055
preconcentration of antibiotic residues in milk and their quantitation by capillary electrophoresis,
1056
Talanta 82 (2010) 213-221.
Page 41 of 62
40 1057
[137]
M. Lecoeur, J.F. Goossens, C. Vaccher, J.P. Bonte, C. Foulon, A multivariate approach for the
1058
determination of isoelectric point of human carbonic anhydrase isoforms by capillary isoelectric
1059
focusing, Electrophoresis 32 (2011) 2857-2866.
1060
[138]
F.T. Dahdouh, K. Clarke, M. Salgado, G. Hanrahan, F.A. Gomez, Chemometrical examination of active parameters and interactions in flow injection-capillary electrophoresis, Electrophoresis 29
1062
(2008) 3779-3785.
ip t
1061
[139] R.E. Montes, G. Hanrahan, F.A. Gomez, Use of chemometric methodology in optimizing conditions
1064
for competitive binding partial filling affinity capillary electrophoresis, Electrophoresis 29 (2008)
1065
3325-3332. [140]
us
1066
cr
1063
R.E. Montes, F.A. Gomez, G. Hanrahan, Response surface examination of the relationship between experimental conditions and product distribution in electrophoretically mediated microanalysis,
1068
Electrophoresis 29 (2008) 375-380.
an
1067
[141] D. Hevia, C. Botas, R.M. Sainz, I. Quiros, D. Blanco, D.X. Tan, C. Gomez-Cordoves, J.C. Mayo,
1070
Development and validation of new methods for the determination of melatonin and its oxidative
1071
metabolites by high performance liquid chromatography and capillary electrophoresis, using
1072
multivariate optimization, J. Chromatogr. A 1217 (2010) 1368-1374.
ed
M
1069
1075 1076 1077 1078 1079
Ac
1074
ce pt
1073
1080
Page 42 of 62
41
1081
Figure legends
1082 1083
Fig. 1.
1084
mg mL-1 RM and (b) 0.04 mg mL-1 RM and its impurities (ramipril methyl ester, IA; ramipril isopropyl ester,
1085
IB;
1086
phenylpropyl]amino]-propanoic
1087
hydroxydiketopiperazine, IL; (2R,3R)-2,3-bis(benzoyloxy)succinic acid, IM). MEKC conditions: BGE, 20
1088
mM borate pH 9.50, 30 mM SDS; detection wavelength, 210 nm. (a) Capillary length, 64.5 cm; voltage, 30
1089
kV; temperature, 30 °C. RM migrated as a pair of peaks corresponding to the cis-trans isomers, connected by
1090
a broadened migration zone generated by the mixture of the isomeric forms. (b) Capillary length, 48.5 cm;
1091
voltage, 25 kV; temperature, 20 °C. The separation of the two isomers of RM and of the three proline-
1092
containing impurities (IA, IB, IC) is shown. Modified from Ref. [79].
Optimization of the separation of ramipril (RM) and related impurities. Electropherogram of (a) 2
ramipril acid],
diketopiperazine, IF;
ramipril
ID;
(S)-2-[[(S)-1-(ethoxycarbonyl)-3-
diketopiperazine
ip t
IC;
acid,
IK;
ramipril
M
an
us
cr
hexahydroramipril,
1093
Fig. 2. Desirability function (D) graphs in the optimization of the separation of ramipril and related
1095
impurities, obtained by plotting: (a) phosphate concentration (BGE conc.) vs. voltage (V); (b) phosphate
1096
concentration (BGE conc.) vs. temperature (T); (c) voltage (V) vs. temperature (T). Partial desirability
1097
functions were defined for critical resolution values and analysis time. Modified from Ref. [79].
1098
ce pt
ed
1094
1099
Fig. 3.
1100
(RM) and related impurities, obtained by mixture design and response surface methodology with the aid of
1101
desirability function. RM, 4 mg mL-1; RM impurities, 0.04 mg mL-1. Internal standard: flufenamic acid (FL)
1102
0.1 mg mL-1. BGE, microemulsion made by 88.95% 90 mM phosphate pH 2.5, 1.05% n-heptane, 10.00%
1103
SDS/n-butanol in 1:2 ratio; capillary length, 64.5 cm; detection wavelength 210 nm; voltage, -26 kV (reverse
1104
polarity); temperature, 17 °C. Symbols as in Fig. 1. Modified from Ref. [79].
Ac
Electropherogram referring to the optimal RF-MEEKC conditions for the separation of ramipril
1105 1106
Fig. 4.
1107
related substances indicated herein as I1, I2, I3, I4, I5 (for structures and names please see Ref. [95]). W,
Contour plots obtained by mixture design referring to the analysis of oxybutynin (OXY) and
Page 43 of 62
42 aqueous phase (10 mM sodium borate); O, oil phase (n-heptane); S/CoS, surfactant/cosurfactant (SDS/n-
1109
butanol in 1:2 ratio). (a) response I3/I4 resolution (R5); (b) response I4/OXY resolution (R6); (c) response
1110
OXY/I5 resolution (R7); (d) response analysis time (t). Voltage, 27 kV; (2-hydroxypropyl)-β-cyclodextrin 15
1111
mM. The considered resolution values R5, R6 and R7 were those found critical for the separation. The
1112
isoresponse lines correspond to the same predicted values of the considered response. Modified from Ref.
1113
[95].
ip t
1108
1114
Fig. 5. Response surfaces in the optimization of the separation of oxybutynin (OXY) and related impurities,
1116
obtained by plotting (2-hydroxypropyl)-β-cyclodextrin concentration (CD conc.) vs. voltage (V): (a) response
1117
I3/I4 resolution (R5); (b) response I4/OXY resolution (R6); (c) response OXY/I5 resolution (R7); (d) response
1118
analysis time (t). For structure and names of the impurities (Ii), please see the Ref. [95]. Modified from Ref.
1119
[95].
an
us
cr
1115
M
1120
Fig. 6. Perturbation plots obtained by the MPV approach for optimizing the response Q10 efficiency. The
1122
effect of varying each PV from its lower bound (-1.0) to its upper bound (1.0) with the other PVs at their
1123
middle values (0.0) for each of the four vertices of the constrained mixture region is shown. B, buffer
1124
(acetate buffer); S, surfactant/cosurfactant (SDS/n-butanol in 1:4.5 ratio); O, oil (n-octane). V, Applied
1125
voltage; BC, Buffer concentration; pH, Buffer pH. The different effects of the PVs for the different
1126
microemulsion formulations are due to the interaction effects of MCs and PVs on the response. Modified
1127
from Ref. [96].
1129
ce pt
Ac
1128
ed
1121
1130 1131
Page 44 of 62
ip t
Table(s)
cr
Table 1
us
Applications of multivariate strategies to the optimization of CZE and related techniques (Affinity Capillary Electrophoresis, ACE; Nonaqueous Capillary Electrophoresis, NACE; Flow-Injection CE, FI-CE; Capillary Isoelectric Focusing, CIEF; Capillary Electrochromatography, CEC;
an
pressure-assisted CEC, p-CEC; Electrophoretically Mediated MicroAnalysis, EMMA; CE-MS, Capillary Electrophoresis-Mass Spectrometry)
Type of sample Magnolia Officinalis (TCM)
Resveratrol, catechin, rutin, syringic acid, kaempferol, coumaric acid, myricetin, quercetin, caffeic acid, gallic acid
Wines
Resveratrol
Nutraceuticals
Nitrites, oxalate
nitrates
and
ep te
Ac c
Acetic, citric, formic, lactic, malic, oxalic, pyruvic, succinic, tartaric, aspartic acid
Brachiaria Brizantha extracts
Vegetables
Phenolic compounds
Extra-virgin olive oil
Lactic,
Cosmetics
malic,
tartaric,
Factors Borate concentration; MeOH %; Voltage Borate concentration; SDS concentration; Brij 35 concentration; MeOH %; Injection time; Voltage; Temperature Borate concentration; ACN %; Voltage Buffer pH; Phthalic acid concentration
d
Analytes Honokiol and magnolol
M
methods.
CTAB concentration; Voltage; Temperature Buffer pH; Temperature; Voltage Boric acid concentration; Buffer pH; Voltage Phosphate concentration;
Responses Rs/analysis time ratio
Type of design BBD
Ref [112]
CRS (Rs and analysis time)
fFD
[40]
Rs; Analysis time
CCD
[39]
S/N; Separation; Analysis time; Repeatability (data not shown)
FFD
[27]
Corrected peak area
FFD
[127]
Rs; Analysis time
CCD
[37]
CRS (Rs and analysis time)
fFD; CCD
[59]
FFD
Page 45 of 62
Antimicrobials (chloramphenicol, danofloxacin, ciprofloxacin, enrofloxacin, sulfamethazine, sulfaquinoxaline, sulfamethoxazole)
Pharmaceuticals
Rosiglitazone, glimepiride
Pharmaceuticals and spiked plasma samples Test mixture
ip t cr Rs; Analysis time; Electric current
[136]
Peak number
CCD
[28]
Phosphate concentration; Voltage; Temperature I) BGE parameters, i.e.,: Buffer pH; Conductivity; Viscosity; Ionic strength II) Borate concentration; MeOH %; Voltage; Temperature Phosphate concentration; Buffer pH
Rs; Analysis time; Electric current I) PCA
CCD
[118]
II) DD
[41]
Rs; Peak area; Migration time
FFD
[113]
Phosphate concentration; ACN %; pH; Voltage Phosphate/TEAOH concentration; TM--CD concentration; Temperature; Voltage
Rs
OD and UD
[34]
Rs
DD
[58]
d
ep te
and
Aconitine, hypaconitine
Mizolastine and related substances
PBD; CCD
seven
Ac c
Arylpropionic drugs
Metoprolol hydrochlorothiazide
us
Milk
M
Antibiotic residues
Buffer pH; CTAB concentration; MeOH %; -CD concentration Borate concentration; Buffer pH; Voltage; Temperature Temperature; Voltage
an
glycolic, citric, mandelic and salicylic acids
Pharmaceuticals
TCM
Pharmaceuticals
II) Rs and analysis time
Page 46 of 62
Real plasma sample from a forensic case
Biogenic amines
Decomposition fluids from porcine tissues
Fluoxetine, norfluoxetine
Real samples
Anthistaminic drugs (carbinoxamine, chlorpheniramine, cyproheptadine, diphenhydramine, doxylamine, ephedrine) Beta-blocker drugs
Antipsychotic drugs
plasma
Spiked samples
urine
Test samples
Test samples
ip t
Propranolol
[116]
CCD
[130]
Rs; Analysis time
PBD; CCD
[35]
Signal (S); Noise (N); S/N; Efficiency CEF (Rs and analysis time)
Sequential BBDs
[33]
FFD; CCD
[26]
Product of resolution and peak height
fFD
[32]
cr
Human serum (spiked samples)
FFD; CCD
us
Epinastine
Rs of adjacent peaks; Analysis time; Electric current Peak width Effective mobility
an
Pharmaceuticals
M
Dorzolamide and timolol maleate
Phosphate concentration; Buffer pH; Temperature; Voltage Phosphate pH; Temperature; Voltage Phosphate concentration; Buffer pH; Injection voltage; Injection time; Separation voltage TRIS-phosphate ionic strength; Buffer pH; Temperature; Voltage Boric acid concentration; Buffer pH; MeOH %; Voltage Phosphate concentration; TEAOH concentration; MeOH %; Water plug; Injection voltage; Separation voltage TRIS-phosphate concentration; Buffer pH; Voltage
d
Pharmaceuticals
ep te
and
Ac c
Pridinol mesylate meloxicam
I) BGE parameters, i.e.,: Buffer pH; Conductivity; Viscosity; Ionic strength II) Voltage; Temperature Phosphate ionic strength;
Rs CCD CRS (Rs and analysis time)
CCD
[31]
I) PCA
DD
[42]
ASM
[29]
II) Rs and analysis time CRF (Rs, number of peaks, analysis
Page 47 of 62
Norfloxacine and decarboxylated degradant
Pharmaceuticals
Norfloxacine tinidazole
Pharmaceuticals
Phosphate concentration; Buffer pH
Glutenins
Wheat varieties
IDA concentration; HPMC concentration; ACN %; Voltage; Temperature
Leucine enkephalin and its immune-complex
Test samples
Poliovirus
Real samples
Phosphate concentration; Buffer pH; Voltage Borate concentration; Sample plug size; Water plug size; Temperature; Concentration of buffer for dialysis of the samples
Ac c
NACE Antihistaminics (chlorpheniramine, cyproheptadine, diphenhydramine, doxylamine, methapyrilene, terfenadine, triprolidine) Quinolizinium
ep te
d
and
ip t
cr
Pharmaceuticals
time) CRS (Rs and analysis time)
CCD
[30]
Migration time
FFD
[129]
Rs; Peak area and RSD; migration time and RSD Rs; Peak area and RSD; migration time and RSD Rs; Peaks number; Total peak area; Time of elution of a peaks cluster (last peak – first peak); Total analysis time; Baseline drift Rs; Efficiency (plate number); Migration time Rs; Peak height; Peak width
FFD
[126]
FFD
[107]
fFDs; CCD
[47]
BBD
[114]
PBD
[44]
us
Maprotiline, desipramine, moclobemide
M
Test samples
an
Buffer pH TRIS-phosphate concentration; Buffer pH; Voltage Phosphate pH; Voltage; Temperature Phosphate concentration; Buffer pH
Cardiovascular drugs
Test samples
Sample plug size; Voltage Sodium acetate (buffer) pH; TBAP concentration; Voltage
CCD CRS (Rs and analysis time)
CCD
[66]
Test samples
SBE-β-CD concentration;
Sum of resolutions of two pairs of
OD
[53]
Page 48 of 62
Synthesized sample
Propranolol and 4-hydroxypropranolol
Test samples
Tamsulosin
Test samples
Clopidogrel and related substances
Commercial materials
Binaphtol and derivatives
Synthesized samples Pharmaceuticals
Antihistaminic drugs
Nuarimol (pesticide)
M
Pharmaceuticals
Test samples
CD-NACE Beta blockers
Test samples
CD-NACE Basic drugs
Test samples
d
ep te
raw
Ac c
Trimeprazine, promethazine
ip t
cr
enantiomers
Enantioresolution
OD
[52]
Enantioresolution; Analysis time
BBD
[56]
Enantioresolution; Analysis time
BBD; FCD; CCD (comparison)
[55]
Rs of two couples of adjacent peaks; Analysis time
Reduced FCD
[54]
Mobility ratio of enantiomers
CCD
[57]
Enantioresolution
BBD
[60]
Enantioresolution
BBD
[61]
Enantioresolution
BBD
[62]
Enantioresolution; Mobility difference; Selectivity
D-oD
[51]
Enantioresolution
D-oD
[68]
an
Dapoxetine
ACN %; Phosphate concentration; Buffer pH; Voltage; Temperature Acetate concentration; Buffer pH; M--CD concentration; MeOH %; Temperature; Voltage TEA-phosphate concentration; CM--CD concentration; Buffer pH; Voltage Sulfated--CD concentration; Voltage; Temperature TEA-phosphate concentration; Buffer pH; Sulfated--CD concentration; Voltage -CD concentration R-Etcholine NTf2concentration TRIS pH; HSA concentration; HSA plug length TRIS pH; HSA concentration; HSA plug length TRIS pH; HSA concentration; HSA plug length CD type (HDMS-β-CD, HDAS-βCD); Nature of BGE anion (formate; acetate; camphorsulfonate); BGE concentration; CD concentration Nature of BGE anion (formate; acetate; camphorsulfonate);
us
compounds derived from berberine
Page 49 of 62
CIEF Standard proteins (ribonuclease, carbonic anhydrase II, lactoglobulin A, cholecystokinin flanking peptide) FI-CE NADH and benzesulfonamide FI-CE Paracetamol, pseudoephedrine, dextromethorphan, chlorphenamine EMMA NAD and NADH, glucose-6-phosphate dehydrogenase (G6PDH) CEC Acetylsalicylic acid and impurities
Test samples
PEO concentration; PEO molecular weight; Voltage; Mobilization pressure
Test samples
Capillary length; Voltage; Injection volume Borate concentration; ACN %; Buffer pH; Voltage
CEC and p-CEC Warfarin, ketoprofen, praziquantel, paracetamol, metoprolol, pyrene, oxazepam CE-MS
ep te
d
M
Test samples
Pharmaceuticals
Mixing time; Voltage; Enzyme (G6PDH) concentration
Ac c
Test samples
ip t cr
Test samples
Relative migration time ratio obtained by using two markers for mobility calculation
BBD
[139]
Rs of adjacent pI markers, S/N
CCD
[137]
Migration time of two markers (ribonuclease, cholecystokinin); Difference in migration time of the two markers
FFDs
[132]
Absorbance
BBD
[138]
MCEF; Peak area of chlorphenamine; Rs chlorphenamine/dextromethorphan; Analysis time % of conversion of NAD to NADH
OD
[125]
BBD
[140]
Retention factor; Number of theoretical plates; Peak symmetry factor; Rs; Electrokinetic porosity; Equivalent length Retention time; Theoretical plate number; Peak asymmetry, Retention factor
Three level screening design
[101]
CCD
[102]
Signal intensity
Taguchi
us
ACE carbonic anhydrase B and a sulfonamide (estimation of Kb) CIEF Carbonic anhydrase isoforms
an
HMAS--CD concentration; BGE concentration Injection time; Voltage; Ligand (sulfonamide) concentration Content of ampholyte for CIEF; Focusing time; Mobilization pressure
Test samples
Amount of TMOS; Amount of PEG; Gelation temperature; Modifying time
Test samples
Pore-forming solvent (PFS, a mixture of water, 1,4-butanediol and 1-propanol) amount; 1,4-butanediol % in PFS.
Test samples
CH3COONH4 concentration;
L18
[105]
Page 50 of 62
ip t
Capillary voltage; screening design; Temperature (ESI); CCD Nebulizing gas pressure; Drying gas flow rate; Sheath liquid: Organic modifier nature; Organic modifier concentration; Acid nature; Acid concentration Acetonitrile (ACN); Asymmetric Screening Matrix (ASM); Background electrolyte (BGE); Binding constant (Kb); Box-Behnken Design (BBD); Carboxymethyl--cyclodextrin (CM--CD); Central Composite Design (CCD); Cetyltrimethylammonium bromide (CTAB); Chromatographic Exponential Function (CEF); Chromatographic Response Factor (CRF); Chromatographic Response Function (CRS); Cyclodextrin (CD); Cyclodextrin-modified Nonaqueous Capillary Electrophoresis (CD-NACE); Doehlert Design (DD); Doptimal Design (D-oD); Electrospray interface (ESI); Face Centered Design (FCD); Fractional Factorial Design (fFD); Full Factorial Design (FFD); Heptakis(2,3-di-O-acetyl-6O-sulfo)-βCD (HDAS-β-CD); Heptakis(2,3-di-O-methyl-6-O-sulfo)-β-CD (HDMS-β-CD); Heptakis(2-O-methyl-3-O-acetyl-6-O-sulfo)--CD (HMAS--CD); Heptakis(2,3,6tri-O-methyl)--CD (TM--CD); Human serum albumin (HSA); Hydroxypropyl methylcellulose (HPMC); Iminodiacetic acid (IDA); Isoeletric point, (pI); Methanol (MeOH); Methylated-γ-CD (M-γ-CD); Modified chromatographic exponential function (MCEF); Nicotinamide adenine dinucleotide (NAD); Nicotinamide adenine dinucleotide reduced form (NADH); Orthogonal Design (OD); Principal Component Analysis (PCA); Plackett-Burman Design (PBD); Polyethylene oxide (PEO); Polyethylenglicol (PEG); Relative Standard Deviation (RSD); Resolution (Rs); (R)()- 1-hydroxy-N,N,N-trimethylbutan-2-aminium bis(trifluoromethylsulfonyl) imide (R-Etcholine NTf2); Signal to Noise ratio (S/N); Sulfobutyl ether-β-CD (SBE-β-CD); Tetrabutylammonium perchlorate (TBAP); Tetramethylorthosilicate (TMOS); Traditional Chinese Medicine (TCM); Triethanolamine (TEAOH); Triethylamine (TEA); Tris(hydroxymethyl)aminomethane (TRIS); Uniform Design (UD).
Ac c
ep te
d
M
an
us
cr
Hepcidin
Page 51 of 62
ip t
Table(s)
cr
Table 2
Spiked samples
Budesonide and related substances
Raw materials pharmaceuticals
Baicalin, baicalein, wogonin
Scutellaria baicalensis herb
Nonsteroidal antiinflammatory drugs, lipid regulators, antiepileptics, fluoroquinolones, sulphonamides Caffeine
Drinking water
Melatonin metabolites
and
biological
Type of Design FFD
Ref [133]
Rs
FFD
[115]
Rs
D-oD; DD
[38]
Rs
PBD for robustness testing CCD
[80]
Rs; Analysis time
FCD
[84]
Sodium carbonate concentration; SDS concentration; Voltage
Interferent separation; Peak area; Noise intensity; Baseline variation; Analysis time; System current
CCD
[109]
Injection time; Injection pressure Phosphate concentration; Buffer pH; SDS concentration;
RSD (peak area)
FFD
Rs; Analysis time; Peak width
FFD
As above plus temperature Borate concentration; phosphate concentration; SDS concentration; ACN %; 2-propanol % SDS concentration; IPA %; Temperature
Ac c
ep te
and
Coffee samples
Test samples
Responses Rs
an
Betamethasone and desamethasone
Factors Phosphate concentration; Buffer pH; SDS concentration Borate concentration; Buffer pH; SDS concentration Borate concentration; cholate concentration; MAPS concentration; Buffer pH; Voltage
M
Type of sample Whitening cosmetics
d
Analytes Arbutin, kojic acid, hydroquinone
us
Applications of multivariate strategies to the optimization of MEKC and MEEKC methods.
[141]
Page 52 of 62
Polycyclic musks
Perfumes
Nicotinic acid and nicotinamide
Food
Five bisphenols
Test samples
Lignanoids
Sheng-Mai-San (TCM)
d
ep te
Ac c Abused drugs and metabolites
ip t cr
Food and beverages
Multiple response function as combination of efficiency
Draper-Lin small FCD
[128]
CCD
[134]
MCRF (Rs and analysis time)
BBD
[122]
Rs/analysis time ratio
PBD; BBD
[83]
CRF (Rs and analysis time)
BBD
[81]
Peak number
OD; sequential UDs
[82]
Relative peak area and relative migration times of selected peaks; Peak number
PBD for robustness testing
Peak numbers; Average peak areas
fFD
CEF for Rs and analysis
CCD
us
Carbohydrates
Voltage Borate concentration; Buffer pH; SDS concentration; Voltage Sorbate concentration; Electrolyte pH; SDS concentration Phosphate concentration; SDS concentration; Voltage Buffer composition (borate/phosphate); Buffer concentration; Buffer pH; SDS concentration; MeOH %; Voltage; Temperature; Injection time Borate concentration; Buffer pH; SDS concentration; ACN %; Borate concentration; Buffer pH; SDS concentration; MeOH %; Voltage; Temperature
Rs
an
Pharmaceuticals
M
-lactams antibiotics
Spiked urine
Borate concentration; Buffer pH; SDS concentration; Voltage; Temperature; Injection time; Wavelength Buffer concentration; Buffer pH; MeOH % (in both separation buffer and sweeping buffer);
[85]
Page 53 of 62
Biological samples
Test samples
ip t cr
fFD
[87]
Rs; Migration times
fFD; CCD
[93]
In addition to the above, ratio of corrected peak area to that of internal standard Difference in migration time of -tocopherol from the cluster + tocopherols; Analysis time; Electric current Enantioresolution and analysis time (for MEKC optimization)
fFD for testing robustness
Sheath-liquid parameters: MeOH %; Buffer concentration; pH
Average peak area; Average S/N ratio (sheath liquid optimization)
CCD for sheath liquid optimization
Spray chamber parameters: Drying gas flow rate;
Average peak area; Average S/N ratio (spray
CCD for optimization
ep te
Vegetable oils
[86]
Corrected peak areas; Peak height
Borate concentration; Cholate concentration; Sodium hydroxide concentration
Ac c
Binaphthyl derivatives
by
fFD CCD
As above, plus MeOH%
Tocopherols NACE-MEKC
us
Test samples
Rs
CEF for Rs and analysis time
d
Cationic model compounds (e.g., quinine, propranolol, atropine etc.) Arachidonic acid metabolites
time
an
Cosmetic creams
M
Cosmetics preservatives
SDS concentration in sweeping buffer; Sample matrix concentration Phosphate concentration; Buffer pH; SDS concentration; MeOH %; Voltage Buffer conductivity; Zone length; Injection time; Sample conductivity; Water plug length Borate concentration; Buffer pH; SDS concentration; CD concentration; Temperature; Voltage
CH3COONH4 concentration; Buffer pH; Surfactants concentration; Voltage; Temperature Nebulization pressure
CCD
[75]
CCD for MEKC optimization
[76]
spray
chamber
Page 54 of 62
Sheath-liquid parameters: MeOH %; pH; Buffer concentration
Oxybutynin and related substances
Average peak area; Average S/N ratio (spray chamber optimization) Enantioresolution and analysis time (for MEKC optimization)
CCD for optimization
Sheath-liquid parameters: MeOH/H2O ratio; Buffer concentration
Average S/N ratio (sheath liquid optimization)
CCD for sheath liquid optimization
Spray chamber parameters: Drying gas flow rate Drying gas temperature Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; M-β-CD concentration; DM-β-CD concentration; Voltage
Average S/N ratio (spray chamber optimization)
CCD for optimization
Rs; Analysis time
MD; CCD
M-β-CD concentration; DM-β-CD concentration; Voltage; Temperature Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; HP-β-CD concentration;
Rs
PBD for robustness
Rs; Analysis time
MD; DD
M
Spray chamber parameters: Drying gas flow rate; Drying gas temperature CH3COONH4 concentration; Buffer pH; Surfactant concentration
d
Pharmaceuticals
Pharmaceuticals
ip t CCD for sheath liquid optimization
ep te
Clemastine and related substances
Test samples
CCD for MEKC optimization
Average peak area; Average S/N ratio (sheath liquid optimization)
Ac c
Mephobarbital, pentobarbital, secobarbital
chamber optimization) Enantioresolution and analysis time (for MEKC optimization)
cr
Drying gas temperature CH3COONH4 concentration; Buffer pH; Surfactant concentration; ACN %; Voltage
us
Test samples
an
Binaphthyl diamine
spray
chamber
CCD for MEKC optimization
spray
[77]
[78]
chamber
[94]
[95]
Page 55 of 62
ip t us
Rh measuring interference between isomerization and electrophoretic migration; Number of peaks
MEKC: ASM; BBD
Rs; Analysis time
RF-MEEKC: MD; DD
[79]
ep te
d
Aqueous phase %; Oil phase %; Surfactant/cosurfactant %; Phosphate concentration; Voltage; Temperature
FFD for robustness
an
Pharmaceuticals
As above
M
Ramipril and related substances
HP-β-CD concentration; Voltage; Temperature Borate concentration; Buffer pH; SDS concentration; THF %; Voltage; Capillary length; Temperature
cr
Voltage
Ac c
pH; Rs; RF-MEEKC: PBD for robustness Phosphate concentration; Analysis time testing Voltage; Temperature Coenzyme Q10, Nutraceuticals Aqueous phase %; Peak efficiency MPV [96] ascorbic acid, folic Oil phase %; acid, fumaric acid, Surfactant/cosurfactant %; acesulfame Acetate (buffer) concentration; Buffer pH Voltage Heptakis(2,6-di-O-methyl)--CD (DM--CD); Hydroxypropyl--CD (HP--CD); Isopropanol (IPA); Methyl--CD (M--CD); Mixture Design (MD); Mixture-Process Variable Design (MPV); Modified Chromatographic Response Factor (MCRF); 3-(N,N-dimethylmyristylammonio)propanesulfonate (MAPS); Reversed flow (RF); Sodium dodecyl sulfate (SDS); Tetrahydrofurane (THF). For other abbreviations, please see Table 1.
Page 56 of 62
Ac
ce
pt
ed
M
an
us
cr
ip t
Figure(s)
Fig. 1, Orlandini etPage al.57 of 62
Figure(s)
D
1.00
D
1.00
(a)
1.00
(b)
0.50
(c)
0.50
V
0.50
T
0.00
T
0.00
BGE conc.
V
Ac
ce
pt
ed
M
an
us
cr
BGE conc.
0.00
ip t
D
Fig.2, Orlandini etPage al.58 of 62
Ac
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ed
M
an
us
cr
ip t
Figure(s)
Fig.3, Orlandini etPage al. 59 of 62
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ed
M
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cr
ip t
Figure(s)
Fig. 4, Orlandini etPage al.60 of 62
Ac
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M
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ip t
Figure(s)
Fig.5, Orlandini etPage al.61 of 62
Ac
ce
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M
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ip t
Figure(s)
Fig.6, Orlandini etPage al. 62 of 62