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Retention-time locked methods in gas chromatography
Journal of Chromatography A, 1216 (2009) 1624–1629
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Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Review
Retention-time locked methods in gas chromatography Nestor Etxebarria ∗ , Olatz Zuloaga, Maitane Olivares, Luis J. Bartolomé, Patricia Navarro Department of Analytical Chemistry, Faculty of Sciences and Technology, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Basque Country, Spain
a r t i c l e
i n f o
Article history: Available online 25 December 2008 Keywords: Gas chromatography Retention-time locking
a b s t r a c t Retention time is one of the most important chromatographic features for analytical chemists since it is the key parameter to separate, identify and quantify compounds of interest from complex mixtures. Although detectors with higher-dimensional signals ease the identification of many components, there are demanding requirements on the retention time, particularly when high-throughput methods are considered. In addition to this, gas chromatographic elution shows significant run-to-run variations due to fluctuations in temperature and pressure, column degradation or matrix effects. In this sense, different approaches have been developed to minimise those variations: the introduction of electronic pneumatic control (EPC) systems, which allow a very efficient control of the flow of the carrier gas, the use of peak alignment algorithms to treat the chromatograms, or the use of retention-time locking (RTL). The RTL is a feature of the Agilent ChemStation software available for those GC instruments equipped with EPC systems. Originally it was developed to assure method translation but it has extended to fix the retention time and to implement peak deconvolution algorithms and database building and searching facilities. In this manuscript, the RTL basis and practical aspects are summarised together with a brief description of some applications. © 2008 Elsevier B.V. All rights reserved.
Contents 1. 2. 3. 4. 5.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The basics of retention-time locking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case study: analysis of petroleum biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Retention time – or maybe more accurately, retention volume – is to analytical chemists almost the most important feature of the chromatogram since it is the key to separate, identify and to quantify the analytes of interest from any complex media. In fact, peak identification is initially accomplished by comparing the retention time of the unknown component to that of a standard. As a consequence of this prime consideration, great efforts have been dedicated to develop standard chromatographic methods that allow the identification of mixture components based on their retention times [1]. Gas chromatographic elution, however, shows
∗ Corresponding author. E-mail address: [email protected] (N. Etxebarria). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.12.038
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a significant variation from run-to-run in the time domain due to uncontrollable fluctuations in temperature and pressure, and from sample to sample due to matrix effects and column degradation. Although the routine use of detectors with higher-dimensional signals allows the identification of many components, not only the different types of mass spectrometers that are already available, but also Fourier transform infrared (FTIR) or atomic emission detection (AED) as well, the requirements on the retention times are even more severe. This scenario is particularly challenging when high throughput methods are considered since the efficient management of both samples and the resulting chromatograms is based on the repeatability and reproducibility of the retention times. One should consider, for instance, the column trimming in routine maintenance procedures, or the development of a selected ion monitoring (SIM) method where all the time windows should be as constant as possible in order to keep the method operative.
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In order to avoid or minimise those effects several approaches have been developed. In addition to the well-known relative retention time (tRr ) or retention index (I), based on the used of a single or a set of reference standards, respectively [1,2], the elution profiles do no satisfy the new requirements of a repetitive retention time. One of the suggested developments arises from the instrumental approach. As a consequence of the introduction of electronic pneumatic control systems, a very efficient control of the flow of the carrier gas along the single run and from run-to-run is now allowable, but it is still not enough when large sets of chromatographic data are processed over a long time span [3,4]. The second strategy is directly linked to the development of multivariate chemometric procedures to treat the chromatograms [5]. The need of these types of data treatments comes especially from the analysis of complex mixtures, where the chromatograms are taken as fingerprints and are used in the same way as other instrumental signals (e.g. near infrared (NIR) spectra) for further data analysis (calibration, classification, etc.). But multivariate analysis of a set of chromatograms requires a systematic correspondence among the retention times of all the components in all the chromatograms, as it has been pointed out [6–8]. Consequently, retention time alignment algorithms have been reported in order to correct the subtle variations found in run-to-run analysis [6,9–11]. The third strategy, namely the retention-time locking (RTL), is partially a consequence of the instrumental development, especially the increasing accuracy on pressure and flow control. Originally, the retention-time locking was aimed to assure the same retention times for the same analytes when they were analysed in several GC systems [12]. In addition to the method portability, as primarily aimed, the use of RTL has been extended to increase the analytical productivity in screening methods, to aid multivariate analysis, and to ease the validation of methods [13–16]. In this review article, the basics of retention-time locking are summarized including an outline of some applications published in different analytical fields.
2. The basics of retention-time locking The background theory of RTL method is thoroughly described in the literature [12] and in some USA patents as well [17,18]. In this section we will summarise the basics of the retention-time locking trying to minimise as much as possible the use of the equations describing the retention mechanisms and the elution processes. For technical details further references will be included. RTL is a feature of the Agilent ChemStation software [19,20] for Agilent 6890 and 6850 and further GC systems equipped with electronic pneumatic control (EPC), and it was developed to assure a closely match retention time, especially when different GC systems were working with the same nominal column. As it is generally known, temperature program separations can be performed either under constant flow conditions or constant pressure. In the former case, the head pressure should be increased with increasing temperature to compensate for the change of carrier viscosity. In the other case, when the head pressure is kept constant along the chromatographic run, the mass flow through the column will decrease with the increasing temperature. The advances in oven temperature control and pneumatics, especially in the last generations of EPC devices, make possible the ability of fine tuning the head pressure and reaching to tR precisions better than 0.005 min. It is precisely the fundamental principle of RTL: the retention times on a given GC setup can be configured by making the required adjustments to the head pressure. To lock a given method one should calibrate the retention time against the head pressure. The step-by-step procedure to develop a RTL method can be summarised as follows:
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(1) A target analyte is selected from the sample mixture. It is very convenient to choose one included in the calibration standards, easily identifiable and eluting in the most critical part of the chromatogram (neither too soon nor too late). (2) Taking the head pressure of the nominal method (Pn ) as the reference value, five calibration runs are performed at five different head pressures: Pn − 20%, Pn − 10%, Pn , Pn + 10%, Pn + 20% (3) The retention time of the target analyte is determined for each calibration run and the corresponding set of retention times and head pressures are fitted with a polynomial of degree 2. Once the fit is accepted, the calibration is stored and becomes part of the GC method. (4) The user is prompted to fix the desired retention time of the target analyte and, this way, the retention time will become locked and the GC method updated. As an example, the calibration plot of 5-(H)-cholane obtained locking the separation method of petroleum biomarkers is shown in Fig. 1. Based on this calibration, it was possible to fix the retention time of that compound at 21.80 min with a head column pressure of 12.4 psi. In addition to this, as shown in Table 1 and discussed later on in the case study, once this method was locked it was possible to analyse in SIM mode up to 30 different terpenes (m/z 191), steranes, diasteranes (m/z 217 and 218) and triaromatic steroids (m/z 231) in petroleum, sediment and biota extracts [14]. Once the calibration is done the only requirement is to relock the retention time of the target analyte to the desired head pressure – or the other way around, i.e. to fix the pressure to the desired retention time – and to validate the method by injecting a sample mixture and comparing the retention times. Additionally, once a method is locked, the user can unlock or relock it using the same or different compounds or after method maintenance procedures. According to the detector, the selection of the inlet pressure can introduce complications since the flux of the carrier gas will decrease as temperature increases. In case of flow-sensitive detectors, as it is the case of MS where the suggested flow rates are between 1 and 1.5 ml/min, when the total elution time is too large, the flux at the end of the chromatogram can become too low affecting significantly to the peak geometry, the chromatographic resolution or even the chemical stability of the stationary phase especially when the flow is lower than 0.4 ml/min. As foreseen in the original sources about RTL [12,19,20], this instrumental feature was found interestingly useful because RTL eliminates the need to update the timed events table, integration table or calibration table when routine maintenance are performed or routine analysis are simultaneously performed in several instruments, allowing simplified validation schemes, the development of retention time databases for screening purposes and the implemen-
Fig. 1. Plot of the calibration data of the RTL software.
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Table 1 Names and abbreviations of the petroleum biomarkers analysed. No.
Compound name
Abbreviation
Terpenes 1 18␣(H)-22,29,30-trisnorhopane 2 17␣(H)-22,29,30-trisnorhopane 3 17␣(H),21(H)-30-norhopane 4 17(H),21␣(H)-30-norhopane 5 17␣(H),21(H)-hopane 6 17␣(H),21(H),22S-norhopane 7 17␣(H),21(H),22R-norhopane 8 Gammacerane 9 17␣(H),21(H),22S-bishomohopane 10 17␣(H),21(H),22R-bishomohopane 11 17␣(H),21(H),22S-trishomohopane 12 17␣(H),21(H),22R-trishomohopane 13 17␣(H),21(H),22S-tetrakishomohopane 14 17␣(H),21(H),22R-tetrakishomohopane
27Ts 27Tm 29ab 29ba 30ab 31abS 31abR 30G 32abS 32abR 33abS 33abR 34abS 34abR
Steranes and diasteranes 15 13(H),17␣(H),20S-cholestane 16 13(H),17␣(H),20R-cholestane 17 5␣(H),14(H),17(H),20R-cholestane 18 5␣(H),14(H),17(H),20S-cholestane 19 24-Methyl-5␣(H),14(H),17(H),20R-cholestane 20 24-Methyl-5␣(H),14(H),17(H),20S-cholestane 21 24-Methyl-5␣(H),14␣(H),17␣(H),20R-cholestane 22 24-Ethyl-5␣(H),14␣(H),17␣(H),20S-cholestane 23 24-Ethyl-5␣(H),14(H),17(H),20R-cholestane 24 24-Ethyl-5␣(H),14(H),17(H),20S-cholestane 25 24-Ethyl-5␣(H),14␣(H),17␣(H),20R-cholestane
27dbS 27dbR 27bbR 27bbS 28bbR 28bbS 28aaR 29aaS 29bbR 29bbS 29aaR
Triaromatic steroids 26 C26,20S-triaromatic steroid hydrocarbon 27 C26,20R + C27,20S-triaromatic steroid hydrocarbon 28 C28,20S-triaromatic steroid hydrocarbon 29 C27,20R-triaromatic steroid hydrocarbon 30 C28,20R-triaromatic steroid hydrocarbon
SC26TA RC26TA + SC27TA SC28TA RC27TA RC28TA
tation of peak deconvolution algorithms to increase the efficiency of data analysis [21,22]. In addition to those benefits, RTL has been very promising when one has to port one method from one column to another, or when the translation requires adapting a method with different pressure drop along the column [high pressure for AED, ambient pressure for flame ionization detection (FID) or vacuum for MS]. Basically, the aim of obtaining translatable methods means the calculation technique to scale a method to different column sizes, speeds, carrier gases, and detectors while preserving the peak elution pattern, i.e. the ratios between the retention times of the same compounds should be constant. As reported in the literature [12,23,24], the concept of method translation makes use of the void time (tm ) as the basic time unit that allows the expressions of all time-related events. Software to perform the method translation is freely available [25] and it computes translations of temperature programs and head pressures for any change in column dimensions, carrier gas or pneumatic conditions. Based on this ability and combined with RTL it is feasible to design scalable methods for optimal speed or resolution and for use in more than one configuration [FID, MS, programmed-temperature vaporization (PTV), etc.]. 3. Case study: analysis of petroleum biomarkers In this section we will briefly describe the development and application of a RTL method in the already mentioned case of the analysis of petroleum biomarkers in environmental sample extracts [14]. The aim of this study was to evaluate the impact of a given oil spill in different types of samples (sediments and biota) by comparing directly the chromatograms of the different extracts. In this particular case, the experimental options were at least two: to measure all the biomarkers shown in Table 1 and to calculate the
diagnostic ratios between isomers as shown in the literature [26], or to treat the chromatograms thoroughly making use of multivariate methods. In the latter case, since highly accurate and repetitive retention times were required, we could make use of retention time alignment algorithms or to lock the retention times and to try a direct treatment of the resulting chromatograms. In relation to the chromatographic separation, we should keep on mind that RTL is a pre-run method and the retention time alignment methods are post-run methods, which could be time consuming. In this sense, what we want to show is that one RTL method not only assures efficiently the retention times of a large amount of compounds, but it makes also feasible the use of multivariate analysis to compare extracts of different sources. Briefly, the analysis of spilled oils and the geochemical characterization of petroleum include, among others, the so called biomarkers. Common biomarkers are terpanes, steranes and monoand triaromatic steroids and they represent a complex molecular fossils derived from once living organisms which are found in crude oil and the refined products. As a consequence of their stability under different geological or environmental conditions they remain practically changeless in rock and sediment. Owing to those features, the biomarkers are extremely useful markers in the characterization of petroleum [26]. In the framework of the analysis of oil spilled samples [14] several petroleum standards and sample extracts (sediments, mussels and limpets) were analysed in order to compare the fingerprints of the biomarkers. All the toluene extracts were analysed on a 6890N Agilent gas chromatograph coupled to a 5973N Agilent mass spectrometer with a 7683 Agilent autosampler. 2 L of the sample was injected in the splitless mode at 300 ◦ C into a HP-5MS (30 m × 0.25 mm, 0.25 m) capillary column. The temperature programme used for the chromatographic separation was as follows: 60 ◦ C for 1 min, temperature increase at 15 ◦ C min−1 to 150 ◦ C and a second ramp of 8 ◦ C min−1 up to 320 ◦ C, where it was finally held for 4 min. In order to show the suitability of the RTL method we have included the plots shown in Fig. 2. On the one hand, the accuracy in the retention times can be seen in Fig. 2a for the four petroleum standards available in this study. As can be seen, the elution pattern of the four samples overlays perfectly and the retention times of some of the biomarker peaks were highly repetitive during the whole experimental work. In addition to this, what we wanted to evaluate was the closeness between the chromatographic elution pattern of some environmental extracts and the chromatographic profiles obtained with the standard oils, as can be seen in Fig. 2b for terpenes (m/z 191). Finally, in the particular case of the triaromatic steroids (m/z 231), as a result of the whole data treatment obtained by means of a soft independent modelling of class analogy model (SIMCA), the sample to model distance against the distance to model centre of some selected samples is shown in Fig. 2c. According to that plot, all the samples included share the same chromatographic pattern that that observed in the oil standards, and therefore a common source could be concluded. Based on this RTL method, the chromatograms of the different sample extracts suspected to be contaminated by oil spills were satisfactorily compared by means of a supervised pattern recognition method without the need of chromatogram realignment. In our opinion, this work shows the efficiency of the retention-time locking procedure and the use of that feature to accomplish data treatments beyond the usual suggestions given in the literature. 4. Applications Since the RTL feature of the ChemStation (Agilent) software was announced, several applications can be found in the
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Fig. 2. Chemical fingerprints of petroleum biomarkers obtained from a RTL method and multivariate analysis: (a) detailed window of the elution of four different standard oil samples (Std1. . .Std4); (b) detailed window of the elution of four different types of sample extracts: oil standard, sediment, limpet and mussel; (c) sample to model distance (Si /S0 ) against the distance to model centre (Hi /H0 ) for the triaromatic steroids (m/z 231) with a significant level of 95%. Some of the points are generically labelled with the type of sample (sediment, mussel, limpet, sediment and oil).
literature. In spite of the fact that many of the applications are notes offered by Agilent from the main web page (URL http://www.agilent.com/chem), detailed applications have been published in analytical journals. Table 2 summarises some of the applications featured with the RTL. As briefly mentioned above, based on RTL methods and especially when the GC is combined with MS detectors [21], it is possible to take further advantages of the repeatability of the retention times, and to use those retention times as the basis of database building process in order to identify unknown compounds or even to recognize known components from a given complex mixture. As can be seen in Table 2, the development of retention time and mass
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spectra databases can be carried out by the end users of the chromatographic systems to support screening tests. These databases can be combined with deconvolution databases [22] or can also be extended to a quantitation database. Surely as a direct consequence of the demanding requirements of regulatory agencies, one of the most extended uses of RTL is the development of multi-residue methods of analysis, i.e. the determination of as many compounds as possible with only one sample treatment and a single chromatographic run, as it is the case of pesticides and other compounds used in agricultural practice [15,16,27–35]. Originally based in an application note [15,48], where between 400 and 560 pesticides and suspected endocrine disrupters were efficiently screened by GC–AED and GC–MS systems and were identified by database search, several advance approaches have been published in the literature [15,27–30]. It is worthwhile saying that in addition to the systematic use of RTL, one of main contribution of these works comes from the use of different sample treatments coupled to the GC–MS: stir-bar sorptive extraction (SBSE) and thermal desorption (TD) [27–29], and large-volume injection (LVI) and PTV [30]. Moreover, as indicated previously, another significant contribution is the implementation of computer assisted deconvolution algorithms and database searching engines that can be useful to treat overlapping peaks and to identify known compounds [15,29]. In fact, automatic mass spectral deconvolution and identification system (AMDIS), freely available software program [http://chemdata.nist.gov/massspc/amdis] and provided by the US National Institute of Standards and Technology (NIST, Gaithesburg, MD, USA), is currently used as the deconvolution and identification software in RTL chromatographic methods [29,46]. In the field of multi-residue analysis there are three more works that make use of RTL methods to support the analysis of pyretroids [31], more than 100 pesticides in Chinese teas [32], organophosphorus pesticides [33], and pesticides in malt beverages and wines [34,35] In addition to the analysis of multi-residues in food, environmental analysis and analysis of drugs, especially abuse drugs, are two fields where RTL methods have been applied [34–44]. In the case of environmental analysis there are four different contributions: the analysis of endocrine disrupter compounds (pesticides, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, alkylphenols and phthalate esters) in Portuguese estuarine waters [36]; the analysis organotins in water and sediments [37]; the identification of non-target compounds in municipal wastewaters using GC and simultaneously AED and MS [38]; the analysis of nitrogen species in petroleum distillates [39]. In the case of abuse drug analysis, the implementation of screening methods in biological fluids is especially difficult owing to the poor availability of high-quality standards and the metabolic derivatives of most drugs [40–44]. Apart from the use of the RTL based approach to develop the GC methods, it is worth mentioning the development of the NAGINATA add-on of the ChemStation software [41] for the quality control and data analysis that combines the retention time, the electron ionization mass spectrum and the calibration of each compound. In the same field, the other two works deal with the analysis of narcotic drugs in biological fluids and blood [42–44]. Finally, in this group of applications it is worth including the application of RTL and method translation to develop fast GC methods for PCB analysis [43]. The last application field is the analysis of fatty acid methyl esters (FAMEs) in foods. In this case, the amount of subtle differences in mass spectra among the different compounds makes interesting the use of RTL based methods to ease the identification of many analytes [13,45]. In these two last cases, two good examples of the applicability of RTL methods are found. In the first case, a multidimensional database (MS spectra and retention time) of more than 130 FAMEs is collected [13]. In the second case, it is
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Table 2 RTL featured methods. In addition to the family of analytes and the type of samples, details about the sample preparation step and whether a database searching library is considered are also included. Analyte
Matrix
Pesticides Pesticides Pesticides Pesticides Pesticides Pesticides Pyrethroids Pesticides Pesticides Endocrine disrupters Organotins Non-target analytes Nitrogen compounds Abuse drugs Abuse drugs Narcotic drugs Biological markers and drugs Drugs PCB, FAME FAME Essential oils Volatile allergens
Fruit and vegetables Vegetables Multi-residues Food samples Food samples Apple juice Water samples Tea Cucumber Estuarine water and sediment Water/sediments Wastewater Petroleum distillates Urine Urine Biological fluids Biological fluids Blood Food samples Gin Fragranced products
Sample Preparation SBSE SBSE SBSE LVI SBSE
LVI SPME SPE
SBSE
LVI
described the translation of a reference method for polychlorinated biphenyl analysis (HP-5MS column, 30 m × 0.25 mm, 0.25 m,) to a high speed column (HP-5MS, 10 m × 0.10 mm, 0.10 m) by means of the method translation facility with a speed gain of 4.4 (from 36 to 8.2 min) [45]. Likewise, we can describe the analysis of essential oils in food. In this field, one application can be found that makes use of the RTL approach together with spectral deconvolution and database searching to separate and identify more than 100 essential oils [46]. Finally, there is another work describing the separation and identification of more than 30 suspected allergens in fragrance products [47]. In this particular case, the main point is the use of a LVI and a liner containing polydimethylsiloxane foam to retain the high molecular weight non-volatile compounds. 5. Conclusions RTL approach is still a promising tool to develop highly efficient GC methods with low effort and with noticeable added values, especially when a demanding throughput is required to identify compounds from complex mixtures. However, according to the literature tracking we cannot conclude that the use of RTL methods has become more widely known or used since the amount of references (∼20) including this tool are not so many, particularly if we consider the time span since this tool is available and the benefits of its use. In fact, even if we consider that this approach is only available with one specific brand of instruments, most of the references included in Table 2 can be easily attributed to a few research groups. We, otherwise, are aware of the fact that only few routine labs tend to publish in scientific journals and this may certainly mask the use of this approach by GC practitioners, far from the academic and research work. Finally, we would like to highlight the net returns in terms of time saving and repeatability that it is offered by the RTL based methods, and we can foresee that the optimization of many GC routine methods may end up locking the retention times. Acknowledgements This work has been partially financed by the Basque Government, through the funds dedicated to high-performance research groups (IT-245-07).
Instrument
Database (Y/N)
Reference
GC–AED/MS TD–GC–MS GC–MS TD–GC–MS TD–GC–MS GC–MS TD–GC–MS GC–MS GC–PF, GC–MS GC–MS GC–MS GC–AED/MS GC–AED/MS GC–MS GC–MS GC–MS TD–GC–MS GC–NPD GC–MS GC–MS (1D/2D) GC–MS GC–MS
Y N N N N Y N N N N N N N N Y N N N N Y N N
[16] [27] [15,34,35] [28] [29] [30] [31] [32] [33] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [13] [46] [47]
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Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Review
Retention-time locked methods in gas chromatography Nestor Etxebarria ∗ , Olatz Zuloaga, Maitane Olivares, Luis J. Bartolomé, Patricia Navarro Department of Analytical Chemistry, Faculty of Sciences and Technology, University of the Basque Country, P.O. Box 644, E-48080 Bilbao, Basque Country, Spain
a r t i c l e
i n f o
Article history: Available online 25 December 2008 Keywords: Gas chromatography Retention-time locking
a b s t r a c t Retention time is one of the most important chromatographic features for analytical chemists since it is the key parameter to separate, identify and quantify compounds of interest from complex mixtures. Although detectors with higher-dimensional signals ease the identification of many components, there are demanding requirements on the retention time, particularly when high-throughput methods are considered. In addition to this, gas chromatographic elution shows significant run-to-run variations due to fluctuations in temperature and pressure, column degradation or matrix effects. In this sense, different approaches have been developed to minimise those variations: the introduction of electronic pneumatic control (EPC) systems, which allow a very efficient control of the flow of the carrier gas, the use of peak alignment algorithms to treat the chromatograms, or the use of retention-time locking (RTL). The RTL is a feature of the Agilent ChemStation software available for those GC instruments equipped with EPC systems. Originally it was developed to assure method translation but it has extended to fix the retention time and to implement peak deconvolution algorithms and database building and searching facilities. In this manuscript, the RTL basis and practical aspects are summarised together with a brief description of some applications. © 2008 Elsevier B.V. All rights reserved.
Contents 1. 2. 3. 4. 5.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The basics of retention-time locking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case study: analysis of petroleum biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Retention time – or maybe more accurately, retention volume – is to analytical chemists almost the most important feature of the chromatogram since it is the key to separate, identify and to quantify the analytes of interest from any complex media. In fact, peak identification is initially accomplished by comparing the retention time of the unknown component to that of a standard. As a consequence of this prime consideration, great efforts have been dedicated to develop standard chromatographic methods that allow the identification of mixture components based on their retention times [1]. Gas chromatographic elution, however, shows
∗ Corresponding author. E-mail address: [email protected] (N. Etxebarria). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.12.038
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a significant variation from run-to-run in the time domain due to uncontrollable fluctuations in temperature and pressure, and from sample to sample due to matrix effects and column degradation. Although the routine use of detectors with higher-dimensional signals allows the identification of many components, not only the different types of mass spectrometers that are already available, but also Fourier transform infrared (FTIR) or atomic emission detection (AED) as well, the requirements on the retention times are even more severe. This scenario is particularly challenging when high throughput methods are considered since the efficient management of both samples and the resulting chromatograms is based on the repeatability and reproducibility of the retention times. One should consider, for instance, the column trimming in routine maintenance procedures, or the development of a selected ion monitoring (SIM) method where all the time windows should be as constant as possible in order to keep the method operative.
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In order to avoid or minimise those effects several approaches have been developed. In addition to the well-known relative retention time (tRr ) or retention index (I), based on the used of a single or a set of reference standards, respectively [1,2], the elution profiles do no satisfy the new requirements of a repetitive retention time. One of the suggested developments arises from the instrumental approach. As a consequence of the introduction of electronic pneumatic control systems, a very efficient control of the flow of the carrier gas along the single run and from run-to-run is now allowable, but it is still not enough when large sets of chromatographic data are processed over a long time span [3,4]. The second strategy is directly linked to the development of multivariate chemometric procedures to treat the chromatograms [5]. The need of these types of data treatments comes especially from the analysis of complex mixtures, where the chromatograms are taken as fingerprints and are used in the same way as other instrumental signals (e.g. near infrared (NIR) spectra) for further data analysis (calibration, classification, etc.). But multivariate analysis of a set of chromatograms requires a systematic correspondence among the retention times of all the components in all the chromatograms, as it has been pointed out [6–8]. Consequently, retention time alignment algorithms have been reported in order to correct the subtle variations found in run-to-run analysis [6,9–11]. The third strategy, namely the retention-time locking (RTL), is partially a consequence of the instrumental development, especially the increasing accuracy on pressure and flow control. Originally, the retention-time locking was aimed to assure the same retention times for the same analytes when they were analysed in several GC systems [12]. In addition to the method portability, as primarily aimed, the use of RTL has been extended to increase the analytical productivity in screening methods, to aid multivariate analysis, and to ease the validation of methods [13–16]. In this review article, the basics of retention-time locking are summarized including an outline of some applications published in different analytical fields.
2. The basics of retention-time locking The background theory of RTL method is thoroughly described in the literature [12] and in some USA patents as well [17,18]. In this section we will summarise the basics of the retention-time locking trying to minimise as much as possible the use of the equations describing the retention mechanisms and the elution processes. For technical details further references will be included. RTL is a feature of the Agilent ChemStation software [19,20] for Agilent 6890 and 6850 and further GC systems equipped with electronic pneumatic control (EPC), and it was developed to assure a closely match retention time, especially when different GC systems were working with the same nominal column. As it is generally known, temperature program separations can be performed either under constant flow conditions or constant pressure. In the former case, the head pressure should be increased with increasing temperature to compensate for the change of carrier viscosity. In the other case, when the head pressure is kept constant along the chromatographic run, the mass flow through the column will decrease with the increasing temperature. The advances in oven temperature control and pneumatics, especially in the last generations of EPC devices, make possible the ability of fine tuning the head pressure and reaching to tR precisions better than 0.005 min. It is precisely the fundamental principle of RTL: the retention times on a given GC setup can be configured by making the required adjustments to the head pressure. To lock a given method one should calibrate the retention time against the head pressure. The step-by-step procedure to develop a RTL method can be summarised as follows:
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(1) A target analyte is selected from the sample mixture. It is very convenient to choose one included in the calibration standards, easily identifiable and eluting in the most critical part of the chromatogram (neither too soon nor too late). (2) Taking the head pressure of the nominal method (Pn ) as the reference value, five calibration runs are performed at five different head pressures: Pn − 20%, Pn − 10%, Pn , Pn + 10%, Pn + 20% (3) The retention time of the target analyte is determined for each calibration run and the corresponding set of retention times and head pressures are fitted with a polynomial of degree 2. Once the fit is accepted, the calibration is stored and becomes part of the GC method. (4) The user is prompted to fix the desired retention time of the target analyte and, this way, the retention time will become locked and the GC method updated. As an example, the calibration plot of 5-(H)-cholane obtained locking the separation method of petroleum biomarkers is shown in Fig. 1. Based on this calibration, it was possible to fix the retention time of that compound at 21.80 min with a head column pressure of 12.4 psi. In addition to this, as shown in Table 1 and discussed later on in the case study, once this method was locked it was possible to analyse in SIM mode up to 30 different terpenes (m/z 191), steranes, diasteranes (m/z 217 and 218) and triaromatic steroids (m/z 231) in petroleum, sediment and biota extracts [14]. Once the calibration is done the only requirement is to relock the retention time of the target analyte to the desired head pressure – or the other way around, i.e. to fix the pressure to the desired retention time – and to validate the method by injecting a sample mixture and comparing the retention times. Additionally, once a method is locked, the user can unlock or relock it using the same or different compounds or after method maintenance procedures. According to the detector, the selection of the inlet pressure can introduce complications since the flux of the carrier gas will decrease as temperature increases. In case of flow-sensitive detectors, as it is the case of MS where the suggested flow rates are between 1 and 1.5 ml/min, when the total elution time is too large, the flux at the end of the chromatogram can become too low affecting significantly to the peak geometry, the chromatographic resolution or even the chemical stability of the stationary phase especially when the flow is lower than 0.4 ml/min. As foreseen in the original sources about RTL [12,19,20], this instrumental feature was found interestingly useful because RTL eliminates the need to update the timed events table, integration table or calibration table when routine maintenance are performed or routine analysis are simultaneously performed in several instruments, allowing simplified validation schemes, the development of retention time databases for screening purposes and the implemen-
Fig. 1. Plot of the calibration data of the RTL software.
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Table 1 Names and abbreviations of the petroleum biomarkers analysed. No.
Compound name
Abbreviation
Terpenes 1 18␣(H)-22,29,30-trisnorhopane 2 17␣(H)-22,29,30-trisnorhopane 3 17␣(H),21(H)-30-norhopane 4 17(H),21␣(H)-30-norhopane 5 17␣(H),21(H)-hopane 6 17␣(H),21(H),22S-norhopane 7 17␣(H),21(H),22R-norhopane 8 Gammacerane 9 17␣(H),21(H),22S-bishomohopane 10 17␣(H),21(H),22R-bishomohopane 11 17␣(H),21(H),22S-trishomohopane 12 17␣(H),21(H),22R-trishomohopane 13 17␣(H),21(H),22S-tetrakishomohopane 14 17␣(H),21(H),22R-tetrakishomohopane
27Ts 27Tm 29ab 29ba 30ab 31abS 31abR 30G 32abS 32abR 33abS 33abR 34abS 34abR
Steranes and diasteranes 15 13(H),17␣(H),20S-cholestane 16 13(H),17␣(H),20R-cholestane 17 5␣(H),14(H),17(H),20R-cholestane 18 5␣(H),14(H),17(H),20S-cholestane 19 24-Methyl-5␣(H),14(H),17(H),20R-cholestane 20 24-Methyl-5␣(H),14(H),17(H),20S-cholestane 21 24-Methyl-5␣(H),14␣(H),17␣(H),20R-cholestane 22 24-Ethyl-5␣(H),14␣(H),17␣(H),20S-cholestane 23 24-Ethyl-5␣(H),14(H),17(H),20R-cholestane 24 24-Ethyl-5␣(H),14(H),17(H),20S-cholestane 25 24-Ethyl-5␣(H),14␣(H),17␣(H),20R-cholestane
27dbS 27dbR 27bbR 27bbS 28bbR 28bbS 28aaR 29aaS 29bbR 29bbS 29aaR
Triaromatic steroids 26 C26,20S-triaromatic steroid hydrocarbon 27 C26,20R + C27,20S-triaromatic steroid hydrocarbon 28 C28,20S-triaromatic steroid hydrocarbon 29 C27,20R-triaromatic steroid hydrocarbon 30 C28,20R-triaromatic steroid hydrocarbon
SC26TA RC26TA + SC27TA SC28TA RC27TA RC28TA
tation of peak deconvolution algorithms to increase the efficiency of data analysis [21,22]. In addition to those benefits, RTL has been very promising when one has to port one method from one column to another, or when the translation requires adapting a method with different pressure drop along the column [high pressure for AED, ambient pressure for flame ionization detection (FID) or vacuum for MS]. Basically, the aim of obtaining translatable methods means the calculation technique to scale a method to different column sizes, speeds, carrier gases, and detectors while preserving the peak elution pattern, i.e. the ratios between the retention times of the same compounds should be constant. As reported in the literature [12,23,24], the concept of method translation makes use of the void time (tm ) as the basic time unit that allows the expressions of all time-related events. Software to perform the method translation is freely available [25] and it computes translations of temperature programs and head pressures for any change in column dimensions, carrier gas or pneumatic conditions. Based on this ability and combined with RTL it is feasible to design scalable methods for optimal speed or resolution and for use in more than one configuration [FID, MS, programmed-temperature vaporization (PTV), etc.]. 3. Case study: analysis of petroleum biomarkers In this section we will briefly describe the development and application of a RTL method in the already mentioned case of the analysis of petroleum biomarkers in environmental sample extracts [14]. The aim of this study was to evaluate the impact of a given oil spill in different types of samples (sediments and biota) by comparing directly the chromatograms of the different extracts. In this particular case, the experimental options were at least two: to measure all the biomarkers shown in Table 1 and to calculate the
diagnostic ratios between isomers as shown in the literature [26], or to treat the chromatograms thoroughly making use of multivariate methods. In the latter case, since highly accurate and repetitive retention times were required, we could make use of retention time alignment algorithms or to lock the retention times and to try a direct treatment of the resulting chromatograms. In relation to the chromatographic separation, we should keep on mind that RTL is a pre-run method and the retention time alignment methods are post-run methods, which could be time consuming. In this sense, what we want to show is that one RTL method not only assures efficiently the retention times of a large amount of compounds, but it makes also feasible the use of multivariate analysis to compare extracts of different sources. Briefly, the analysis of spilled oils and the geochemical characterization of petroleum include, among others, the so called biomarkers. Common biomarkers are terpanes, steranes and monoand triaromatic steroids and they represent a complex molecular fossils derived from once living organisms which are found in crude oil and the refined products. As a consequence of their stability under different geological or environmental conditions they remain practically changeless in rock and sediment. Owing to those features, the biomarkers are extremely useful markers in the characterization of petroleum [26]. In the framework of the analysis of oil spilled samples [14] several petroleum standards and sample extracts (sediments, mussels and limpets) were analysed in order to compare the fingerprints of the biomarkers. All the toluene extracts were analysed on a 6890N Agilent gas chromatograph coupled to a 5973N Agilent mass spectrometer with a 7683 Agilent autosampler. 2 L of the sample was injected in the splitless mode at 300 ◦ C into a HP-5MS (30 m × 0.25 mm, 0.25 m) capillary column. The temperature programme used for the chromatographic separation was as follows: 60 ◦ C for 1 min, temperature increase at 15 ◦ C min−1 to 150 ◦ C and a second ramp of 8 ◦ C min−1 up to 320 ◦ C, where it was finally held for 4 min. In order to show the suitability of the RTL method we have included the plots shown in Fig. 2. On the one hand, the accuracy in the retention times can be seen in Fig. 2a for the four petroleum standards available in this study. As can be seen, the elution pattern of the four samples overlays perfectly and the retention times of some of the biomarker peaks were highly repetitive during the whole experimental work. In addition to this, what we wanted to evaluate was the closeness between the chromatographic elution pattern of some environmental extracts and the chromatographic profiles obtained with the standard oils, as can be seen in Fig. 2b for terpenes (m/z 191). Finally, in the particular case of the triaromatic steroids (m/z 231), as a result of the whole data treatment obtained by means of a soft independent modelling of class analogy model (SIMCA), the sample to model distance against the distance to model centre of some selected samples is shown in Fig. 2c. According to that plot, all the samples included share the same chromatographic pattern that that observed in the oil standards, and therefore a common source could be concluded. Based on this RTL method, the chromatograms of the different sample extracts suspected to be contaminated by oil spills were satisfactorily compared by means of a supervised pattern recognition method without the need of chromatogram realignment. In our opinion, this work shows the efficiency of the retention-time locking procedure and the use of that feature to accomplish data treatments beyond the usual suggestions given in the literature. 4. Applications Since the RTL feature of the ChemStation (Agilent) software was announced, several applications can be found in the
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Fig. 2. Chemical fingerprints of petroleum biomarkers obtained from a RTL method and multivariate analysis: (a) detailed window of the elution of four different standard oil samples (Std1. . .Std4); (b) detailed window of the elution of four different types of sample extracts: oil standard, sediment, limpet and mussel; (c) sample to model distance (Si /S0 ) against the distance to model centre (Hi /H0 ) for the triaromatic steroids (m/z 231) with a significant level of 95%. Some of the points are generically labelled with the type of sample (sediment, mussel, limpet, sediment and oil).
literature. In spite of the fact that many of the applications are notes offered by Agilent from the main web page (URL http://www.agilent.com/chem), detailed applications have been published in analytical journals. Table 2 summarises some of the applications featured with the RTL. As briefly mentioned above, based on RTL methods and especially when the GC is combined with MS detectors [21], it is possible to take further advantages of the repeatability of the retention times, and to use those retention times as the basis of database building process in order to identify unknown compounds or even to recognize known components from a given complex mixture. As can be seen in Table 2, the development of retention time and mass
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spectra databases can be carried out by the end users of the chromatographic systems to support screening tests. These databases can be combined with deconvolution databases [22] or can also be extended to a quantitation database. Surely as a direct consequence of the demanding requirements of regulatory agencies, one of the most extended uses of RTL is the development of multi-residue methods of analysis, i.e. the determination of as many compounds as possible with only one sample treatment and a single chromatographic run, as it is the case of pesticides and other compounds used in agricultural practice [15,16,27–35]. Originally based in an application note [15,48], where between 400 and 560 pesticides and suspected endocrine disrupters were efficiently screened by GC–AED and GC–MS systems and were identified by database search, several advance approaches have been published in the literature [15,27–30]. It is worthwhile saying that in addition to the systematic use of RTL, one of main contribution of these works comes from the use of different sample treatments coupled to the GC–MS: stir-bar sorptive extraction (SBSE) and thermal desorption (TD) [27–29], and large-volume injection (LVI) and PTV [30]. Moreover, as indicated previously, another significant contribution is the implementation of computer assisted deconvolution algorithms and database searching engines that can be useful to treat overlapping peaks and to identify known compounds [15,29]. In fact, automatic mass spectral deconvolution and identification system (AMDIS), freely available software program [http://chemdata.nist.gov/massspc/amdis] and provided by the US National Institute of Standards and Technology (NIST, Gaithesburg, MD, USA), is currently used as the deconvolution and identification software in RTL chromatographic methods [29,46]. In the field of multi-residue analysis there are three more works that make use of RTL methods to support the analysis of pyretroids [31], more than 100 pesticides in Chinese teas [32], organophosphorus pesticides [33], and pesticides in malt beverages and wines [34,35] In addition to the analysis of multi-residues in food, environmental analysis and analysis of drugs, especially abuse drugs, are two fields where RTL methods have been applied [34–44]. In the case of environmental analysis there are four different contributions: the analysis of endocrine disrupter compounds (pesticides, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, alkylphenols and phthalate esters) in Portuguese estuarine waters [36]; the analysis organotins in water and sediments [37]; the identification of non-target compounds in municipal wastewaters using GC and simultaneously AED and MS [38]; the analysis of nitrogen species in petroleum distillates [39]. In the case of abuse drug analysis, the implementation of screening methods in biological fluids is especially difficult owing to the poor availability of high-quality standards and the metabolic derivatives of most drugs [40–44]. Apart from the use of the RTL based approach to develop the GC methods, it is worth mentioning the development of the NAGINATA add-on of the ChemStation software [41] for the quality control and data analysis that combines the retention time, the electron ionization mass spectrum and the calibration of each compound. In the same field, the other two works deal with the analysis of narcotic drugs in biological fluids and blood [42–44]. Finally, in this group of applications it is worth including the application of RTL and method translation to develop fast GC methods for PCB analysis [43]. The last application field is the analysis of fatty acid methyl esters (FAMEs) in foods. In this case, the amount of subtle differences in mass spectra among the different compounds makes interesting the use of RTL based methods to ease the identification of many analytes [13,45]. In these two last cases, two good examples of the applicability of RTL methods are found. In the first case, a multidimensional database (MS spectra and retention time) of more than 130 FAMEs is collected [13]. In the second case, it is
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Table 2 RTL featured methods. In addition to the family of analytes and the type of samples, details about the sample preparation step and whether a database searching library is considered are also included. Analyte
Matrix
Pesticides Pesticides Pesticides Pesticides Pesticides Pesticides Pyrethroids Pesticides Pesticides Endocrine disrupters Organotins Non-target analytes Nitrogen compounds Abuse drugs Abuse drugs Narcotic drugs Biological markers and drugs Drugs PCB, FAME FAME Essential oils Volatile allergens
Fruit and vegetables Vegetables Multi-residues Food samples Food samples Apple juice Water samples Tea Cucumber Estuarine water and sediment Water/sediments Wastewater Petroleum distillates Urine Urine Biological fluids Biological fluids Blood Food samples Gin Fragranced products
Sample Preparation SBSE SBSE SBSE LVI SBSE
LVI SPME SPE
SBSE
LVI
described the translation of a reference method for polychlorinated biphenyl analysis (HP-5MS column, 30 m × 0.25 mm, 0.25 m,) to a high speed column (HP-5MS, 10 m × 0.10 mm, 0.10 m) by means of the method translation facility with a speed gain of 4.4 (from 36 to 8.2 min) [45]. Likewise, we can describe the analysis of essential oils in food. In this field, one application can be found that makes use of the RTL approach together with spectral deconvolution and database searching to separate and identify more than 100 essential oils [46]. Finally, there is another work describing the separation and identification of more than 30 suspected allergens in fragrance products [47]. In this particular case, the main point is the use of a LVI and a liner containing polydimethylsiloxane foam to retain the high molecular weight non-volatile compounds. 5. Conclusions RTL approach is still a promising tool to develop highly efficient GC methods with low effort and with noticeable added values, especially when a demanding throughput is required to identify compounds from complex mixtures. However, according to the literature tracking we cannot conclude that the use of RTL methods has become more widely known or used since the amount of references (∼20) including this tool are not so many, particularly if we consider the time span since this tool is available and the benefits of its use. In fact, even if we consider that this approach is only available with one specific brand of instruments, most of the references included in Table 2 can be easily attributed to a few research groups. We, otherwise, are aware of the fact that only few routine labs tend to publish in scientific journals and this may certainly mask the use of this approach by GC practitioners, far from the academic and research work. Finally, we would like to highlight the net returns in terms of time saving and repeatability that it is offered by the RTL based methods, and we can foresee that the optimization of many GC routine methods may end up locking the retention times. Acknowledgements This work has been partially financed by the Basque Government, through the funds dedicated to high-performance research groups (IT-245-07).
Instrument
Database (Y/N)
Reference
GC–AED/MS TD–GC–MS GC–MS TD–GC–MS TD–GC–MS GC–MS TD–GC–MS GC–MS GC–PF, GC–MS GC–MS GC–MS GC–AED/MS GC–AED/MS GC–MS GC–MS GC–MS TD–GC–MS GC–NPD GC–MS GC–MS (1D/2D) GC–MS GC–MS
Y N N N N Y N N N N N N N N Y N N N N Y N N
[16] [27] [15,34,35] [28] [29] [30] [31] [32] [33] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [13] [46] [47]
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