An enantioselective electrochemical sensor for simultaneous determination of mandelic acid enantiomers using dexamethasone-based chiral nanocomposite coupled with chemometrics method

Journal of Electroanalytical Chemistry 805 (2017) 83–90

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An enantioselective electrochemical sensor for simultaneous determination of mandelic acid enantiomers using dexamethasone-based chiral nanocomposite coupled with chemometrics method

MARK

Marjan Borazjania, Ali Mehdiniab,⁎, Ali Jabbaria a

Department of Chemistry, Faculty of Science, K. N. Toosi University of Technology, P. O. Box 16315-1618, Tehran, Iran Department of Marine Living Science, Ocean Sciences Research Center, Iranian National Institute for Oceanography and Atmospheric Science, P.O. Box 14155-4781, Tehran, Iran

b

A R T I C L E I N F O

A B S T R A C T

Keywords: Chiral recognition Partial least squares regression Genetic algorithm Graphene

A nanocomposite-based sensor that responds selectively to mandelic acid (MA) enantiomers was constructed with Dexamethasone (DEX) as a chiral recognition element. The DEX-modified electrode was selective towards MA enantiomers compared with the glassy carbon electrode (GCE) modified by graphene (GR) nanosheets or bare GCE. The entrapment of DEX within the overoxidised polypyrrole (OPPy) film occurred on the surface of the GR-modified electrode using a two-step electrodeposition approach. The deposited film was characterized by scanning electron microscopy (SEM), differential pulse voltammetry (DPV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The OPPy-DEX/GR/GCE sensor for sensing of MA enantiomers required ∼0.3 V less overpotential than the GC electrode. Upon exposure to (S)- and (R)-MA, the sensor showed the current signals with different ratios, which were differentiated at the voltages of 1.38 and 1.43 V (vs. Ag/ AgCl), respectively. Under optimal conditions, the chiral sensor exhibited a good linear relationship with MA enantiomers concentrations ranging from 1 to 25 mM with a detection limit of 0.25 mM. Due to the highly overlapping signals, partial least squares (PLS) regression was applied to distinguish the MA enantiomers in synthetic mixtures. In addition, the genetic algorithm-based potential selection procedure was applied to optimize the number of PLS factors used for the building of the PLS calibration models, and also its predictive ability was studied.

1. Introduction One of the applications of nanomaterials in electrochemistry is modification of electrode surface with the chiral nanostructures to design the chiral electrochemical sensors. The chiral selectors can be immobilized on a nanocomposite-based matrix for application in enantioselective sensors. The nanomaterials with intrinsic chiroptical properties, such as multi-walled carbon nanotubes, have been emerged as a new category of chiral selectors with multipurpose usage in the field of electrochemistry, because of their helical structures [1]. Generally, the unique features of nanocomposite-modified electrodes make them more efficient for sensor applications, resulting in lower detection limit, fast response time, high sensitivity, and increased signal-to-noise ratio for the sensors. The surface nano-modification not only maximize availability of the nanosized surface area for electron transfer, but also provide better mass transport of the reactants to the electroactive sites on the electrode surface [2]. Although the chiral electrochemical

⁎

sensors have been attracted considerable attentions for the enantioselective detection, most of the architectures cannot be reused, which is the major problem of the chiral sensing platforms for stereoselective analysis. The sensing platform must be reconstructed after measurement, due to inactivation of the chiral biorecognition elements during determination. Unfortunately, reconstruction of the sensing architecture requires a few hours or even a few days, which makes the potential application of the sensors costly and inconvenient. Therefore, it is of great importance to reproduce a sensing platform for the enantioselective analysis with practical applications. It is already demonstrated the usefulness of the overoxidized polypyrrole (OPPy) film to provide a reusable porous and permselective membrane in the case of glucose [3,4], alcohol [5], hydrazine [6,7], dopamine [8–13], and naproxen [14] sensors. The exploitation of Graphene (GR) nanosheets with the OPPy matrix to immobilize chiral selector molecules may enable the design of more reusable, stable and sensitive enantioselective sensors. However, a few studies have been performed on

Corresponding author. E-mail address: [email protected] (A. Mehdinia).

http://dx.doi.org/10.1016/j.jelechem.2017.10.017 Received 13 March 2017; Received in revised form 4 October 2017; Accepted 10 October 2017 Available online 13 October 2017 1572-6657/ © 2017 Elsevier B.V. All rights reserved.

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2.3. Electrochemical measurements

the modified GR with selective materials to form a functionalized composite for electrochemical detection of chiral molecules [15]. Given the unique properties of OPPy matrix and electrocatalytic activity of graphene, it can be introduced into the OPPy texture to improve the performance of the sensor. The high electronic conductivity and large surface area of the graphene nanomaterial can significantly improve the sensitivity of the GR/porous OPPy-modified electrode. Dexamethasone (DEX), as a chiral modifier of biosensing platform, can provide a chiral reactive environment for the reactant molecules. Steroids including hormones and steroid pharmaceuticals are chiral compounds with multiple stereogenic centers. DEX, as a chiral molecule with eight stereogenic centers, is a steroid compound that has been commonly used as an anti-inflammatory drug for treatment of allergies, arthritis, asthma and etc. (Fig. D.1) [16]. Recently, D-(+)-biotin (a vitamin) was proposed as a new chiral selector for enantioselective sensing of MA, allowing specific detection of (R)-MA in the presence of (S)-MA [17]. The preferences of DEX modified electrode over D(+)-biotin are (i) availability of DEX as an inexpensive chiral selector that makes it applicable to routine analysis and (ii) possibility of simultaneous determination of both enantiomers. These unique properties of the chiral modified electrode allowed elaboration of the sensor with advantageous of the chemometrics tools. To date, the majority of enantioselective electrochemical sensors for MA enantiomers detection described in the literature utilize proteins as the chiral recognition element [18–20]. These sensors successfully enable enantio-discrimination of MA, but require redesigning of the chiral platform and a time-consuming modification process. DEX, on the other hand, offers several advantages such as stability and cost-effectiveness. DEX can interact reversibly with MA enantiomers, a result of the loosely bound nature and the matrix effects. This research not only introduces the chiral drugs as a new category of enantiorecognition elements, but it may also provide a presupposition for stereoselective interaction preferences of the chiral drugs in natural conditions. Herein, by taking advantage of the unique features of both nanocomposite-based electrochemical sensor and chemometrics methods, a new label-free steroid-based enantioselective platform was proposed for MA enantiomers detection. Taking into account that MA enantiomers showed a very high overlap degree in their voltammograms, partial least squares (PLS-1 and PLS-2) regressions were used for simultaneous determination of MA enantiomers. For improvement the calibration model performance and its predictive ability, genetic algorithm (GA) was used to find the optimum potentials.

Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and differential pulse voltammetry (DPV) measurements were carried out on a μAutolab III (Ecochemie, Netherlands) with Ag/AgCl (saturated KCl solution) as a reference electrode and platinum rod as a counter electrode in a three-electrode configuration. The impedance spectra were recorded in a frequency range of 100 KHz to 0.1 Hz with 10 mV signal amplitude. Characterization of the morphologies and porosity of GR, PPy-DEX/ GR and OPPy-DEX/GR films were performed using scanning electron microscope (SEM, Oxford Instruments Ltd., UK) and MIP image analyzing software, a metallographic image processing software designed in Nahamin Pardazan Asia Co. (Mashhad, Iran). All DFT calculations were performed using Gaussian 98 suite of program. Among various modern functional for DFT calculation, Becke's three parameter hybrid functional combined with the Lee-YangParr correlation functional, designated as B3LYP, with 6-31 ++G (d, p) sets were used. 3. Results and discussion 3.1. Two-step fabrication of the sensing layer The procedures of electropolymerization and overoxidation of polypyrrole film have been discussed in details in the supporting information and a previous report [17]. The DEX as chiral selector was incorporated into the PPy during film growth. The feasibility of participating in H-bonding with monomers may be the reason for the incorporation of DEX into the film. Therefore, the mixture of DEX and pyrrole monomers was allowed to react an hour prior to electrodepositing. The F, OH and CO groups in DEX could form hydrogen bonds with the monomers, resulting in prearrangement of monomers around DEX molecules. As shown in Fig. S1 (Supplementary data), during overoxidation of PPy film, the film loses its conductivity and gains porosity and permselectivity. Combining these properties of OPPy film with chiral recognition property of the entrapped DEX improved the selectivity of the sensor. Furthermore, the sensor exhibited remarkable electrocatalytic activity towards MA enantiomers oxidation, improved response currents and decreased oxidation overpotentials (0.3 V) compared to the untreated GCE. It can be attributed to the synergic effect of the OPPy and graphene nanosheets. Since the sensitivity of OPPy-DEX-modified electrode was poor because of its low conductivity, a nanocomposite of OPPy and graphene nanomaterials with high electronic conductivity can improve the analytical performance of the sensor.

2. Experimental 2.1. Chemicals

3.2. Faradaic impedance spectroscopy investigation

(S)- or (R)-MA were purchased from Sigma-Aldrich (St. Louis, MO, www.sigmaaldrich.com). Dexamethasone (as disodium phosphate salt) was purchased from Sina Darou Pharmaceutical Company, Iran. Pyrrole and other chemical reagents were purchased from Merck (Darmstadt, Germany, www.merckmillipore.com). Double-distilled water was used in all experiments.

Faradaic impedance spectroscopy (FIS) was used to monitor changes of the interfacial properties of the modified electrodes (Fig. 1). The Nyquist plots of the bare GCE, GR-modified GCE, OPPy-DEX/GRmodified GCE, OPPy-DEX-modified GCE and OPPy/GR-modified GCE in 5 mM Fe(CN)63 −/4 − redox couple are displayed in Fig. A.1. All of the coatings include a time constant phase element, appeared from one semicircle in Nyquist plot (Fig. A.1), a peak in phase Bode plot and a slope in magnitude Bode plot (Fig. A.S2 and B.S2, Supplementary data). Therefore, a modified Randles circuit model, including the solution resistance (Rs), the charge transfer resistance (Rct), coating capacitance (CPE) and diffusion process (w) is applied to extract different FIS parameters (Fig. B.1). The CPE, as non-ideal capacitor, was used for fitting the experimental data. Observations including the lower slope of a straight line and a depressed semicircle of Nyquist plot and lower phase angle in the Bode plot indicate deviations from the ideal capacitive behavior. This non-ideality origins from the porosity of the film and slow recognition and adsorption of the ions at the electrode surface

2.2. Fabrication of chiral modified surface The details of electrode modification and electrical measurements are found in the supporting information. Briefly, after modification of GCE surface with graphene (GR), DEX-loaded PPy film was electrochemically deposited on the GR-modified electrode by cyclic voltammetry (CV). The obtained modified electrode, denoted as PPy-DEX/GR/ GCE, was rinsed with distilled water and then transferred to a KCl aqueous phosphate buffered solution for electrochemical oxidation using CV technique. The obtained electrode, which is denoted as OPPyDEX/GR/GCE, was ready to use after washing with water, and then drying. 84

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compactness of the coating [25]. The occurrence of lower phase angle value for the film without GR, demonstrates the existence of a compact coating on the GCE surface. It is inferred that the presence of GR nanosheets causes the fabrication of a polymer coating with higher porosity and less compactness (Fig. A.S3, Supplementary data). In addition, the magnitude Bode plot in low frequency region is representative of barrier property of the system with high impedance modulus (high charge transfer resistance, Rct) [25]. As shown in Fig. B.S2 (Supplementary data), a significantly high magnitude of impedance (| Z |) for the film without GR signifies the role of GR in reducing barrier property of the surface film. The respective region for the film without DEX shows a significantly lower | Z |, confirming the incorporation of DEX into the film. 3.3. Cyclic voltammetry study Characterization of the modified electrodes was also performed by cyclic voltammetry which is an effective and sensitive method for investigating the feature of the electrode surface (Fig. S3, Supplementary data). The bare GCE gave a typical reversible electrochemical response for 5 mM Fe(CN)63 −/4 − redox couple at 224 mV with peak-to-peak potential difference (ΔEp) of 100 mV [26]. Compared with the bare GCE, the GR-modified electrode exhibited an increase in sensitivity, due to high surface area of GR nanosheets. In a further step, OPPy-DEX modification on the GR/GCE caused a significant change on the electrochemical behavior of the Fe(CN)63 −/4- couple, leading to a significant decrease in the current. The results suggest that the film of OPPy-DEX acts as an insulating layer, resulting difficult interfacial electron transfer, and tend to blockage of the electrode surface for oxidation/reduction of the redox probe of Fe(CN)63 −/4-. The increase in the current was observed for a film without DEX. It is concluded that the presence of DEX can decrease the conductivity of the film. Although the OPPy film has no conductivity, the pinholes and defects in the film make possible ion diffusion thorough the film [27]. The results were consistent with the FIS observation.

Fig. 1. (A) Nyquist plots of different electrodes: (a) GR/GCE, (b) bare GCE, (c) OPPyDEX/GR/GCE, (d) OPPy-DEX/GCE, (e) OPPy/GR/GCE, in 5 mM Fe(CN)63 −/4 − in 50 mM PBS (pH 7.0) solution. (B) Equivalent circuits (modified Randles model) used for impedance data approximation.

[21]. In Nyquist plot, the semicircle diameter is representative of the charge transfer resistance (Rct), indicating one interfacial property of the electrode [22]. Considering the Nyquist plot, the RC semicircle for GR/GCE appears negligible, unlike the bare GCE. It also appears larger for the film without GR nanosheets (OPPy-DEX/GCE) (Rct = 642.71 ± 3.54 Ω) respect to the film containing GR nanosheets (Rct = 304.07 ± 5.62 Ω). Both observations confirm the role of GR in the improvement of conductivity and electrochemical activity of the film surface. As expected, a higher charge transfer resistance (Rct = 304.07 ± 5.62 Ω) of the OPPy-DEX/GR film was observed compared to the bare GCE (Rct = 200.50 ± 1.12 Ω), because of insulating property of OPPy [23]. It suggested that the film is effectively attached to the surface of the GCE. Interestingly, the diameter of the semicircle of Nyquist plot increased with the introduction of DEX into the OPPy/GR layer. This is due to the much lower conductivity properties of DEX, revealing incorporation of DEX as a chiral selector into the film structure. The above results characterize the electrochemical properties of the interfacial film, and prove successful immobilization of the chiral film. In angle Bode plot, the capacitive behavior in the middle frequency region shows the insulating properties of the surface film [24]. A wide capacitive behavior of the coatings without GR, with a high value of phase angle of 40° in this region, confirms the role of GR in improvement of the conductivity and charge transfer kinetic of the film. The phase angle at the low-frequency region is a measure of the

3.4. Surface morphology study Previous research has shown that the properties of the conducting polymers are strongly dependent on their morphology and structure [28]. It can be seen from Fig. 2 that the synthesized film has got porous and granular surface morphology, which provides permeable channels in the polymeric network. It enables the access to the electrochemically active surfaces and improves the electrocatalytic activity for the analyte. The OPPy-DEX/GR film prepared by electrodeposition method was almost in nano-scale (80–100 nm), with a strong tendency towards agglomeration. The MIP analysis of images showed that the overoxidized polypyrrole film is more porous than the polypyrrole film, as has been already claimed by the previous report [23]. The porosity of the coating, represented by α, increased from 5.58% to 20.07% after overoxidation (Fig. S4, Supplementary data). The electrochemical properties of polypyrrole films are intricately related to the Fig. 2. SEM images of GCE surfaces coated with: (a) GR/ PPy-DEX, (b) GR/OPPy-DEX.

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Fig. 3. Cyclic voltammograms of (A) (S)-MA and (B) (R)-MA on OPPy-DEX/GR/GCE at various scan rates from 5 to 100 mVs− 1, (C) plots of oxidation peak currents of MA enantiomers νr. the square root of scan rates.

morphology. A more ordered, smoother, denser and less porous film with nodular morphology can be generally more conductive due to the easier charge transfer possibility through the film [29]. Therefore, the low conductivity can be inferred from the porous and granular morphology of the film.

an overlapping of two oxidation peaks is suggested to justify the broad signal of this chiral sensor. The peak width, resulting from the overlapping of the two-stage electron transfer signals can be justified that difference in the energy between removal of the first and second electron is not sufficient for the separation of the signals from each other. Therefore, a broad peak is resulted from the overlapping of two oxidation peaks. There are two possibilities for the mandelic acid enantiomers electrooxidation process. The mechanisms presented in Fig. 3, offer two-stage oxidation process for enantiomers of mandelic acid. In one of these mechanisms, the “two stage-one electron” process is carried out and in another mechanism; the process is “two stage-two electron”. With regard to the enantiomers peak widths and the above formula, the first mechanism was confirmed (Fig. B.3). This two-stage electrooxidation process involves the oxidation of carboxylic acid groups at first, and then more oxidation of hydroxyl groups which produces aldehyde groups. Although, the half potential width of the reversible electrode reactions is independent of the kinetic properties of the electrode surface, the half potential width of semi-reversible and irreversible electrode reactions is influenced by the catalytic and kinetic properties of the electrode surface [31]. Commonly, with the same number of transferred electrons, the peak width of the irreversible

3.5. Mechanism of mandelic acid enantiomers electrooxidation An increase in the number of transferred electrons in electrochemical processes reduces the voltammogram peak width which represents the sensitivity of the voltammogram shape to the number of transferred electrons. As more electrons are exchanged, the peak width becomes narrower. In general, the half potential width is dependent on the reversibility of redox couple, and is equal to 90.6/αn mV and 62.4/ αn mV for the Nernstian ideal reversible reactions and irreversible reactions, respectively [30]. If the number of transferred electrons is equal to one, the peak width of the reversible electrode reaction (α = 1) will be 90.6 mV. It also will be 125 mV for irreversible electrode reactions (α = 0.5). Due to the irreversibility of electrooxidation of the mandelic acid enantiomers, and the widths of the anodic oxidation peaks of the (S)-MA (223 mV) and (R)-MA (273 mV) (Fig. A.3),

Fig. 4. Cyclic voltammograms of (R)- and (S)-MA in 50 mM PBS (pH 7.0) solution on (A) bare GCE, (B) GR/GCE, (C) OPPy/GR/GCE (D) OPPy-DEX/GR/GCE. The scan rate was 50 mVs− 1.

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electrode reaction depends on the electron transfer coefficient. The half potential widths of the DPV signals of (R)-MA and (S)-MA were different, which is due to the difference in the electron transfer coefficients of the oxidation process of the enantiomers. Therefore, based on the above relations and the two-stage electromechanical mechanism of enantiomers electrooxidation, the calculated coefficients of α for each of the (S)-MA and (R)-MA enantiomers are 0.56 and 0.46, respectively. 3.6. Cyclic voltammetry studies of enantiorecognition of the sensing platform The enantioselective recognition in electrochemical sensors and biosensors follows from different affinity of a chiral selector towards the stereoisomers [32]. To investigate the role of DEX in surface chirality and enantioselectivity towards MA, the interaction between MA enantiomers and immobilized DEX, as OPPy-DEX/GR/GC electrode, was evaluated (Fig. 4). In addition, control experiments were performed by studying the behavior of MA enantiomers on the bare GCE, GR/GCE and OPPy/GR/GCE. The electrochemical responses of (S)-MA and (R)MA are almost the same on the untreated GC electrode (Fig. A.4), meaning the response of chiral molecules on unmodified electrode is almost identical. Significantly, it is observed that the signal intensity on GR modified GCE is higher than that on the bare GCE; however, the responses of enantiomers on this surface were similar (Fig. B.4). The signals of MA enantiomers on the OPPy/GR-modified surface were also investigated. The peak currents are decreased after treating of the GCE surface with OPPy/GR, indicating the OPPy became insulated. The electrochemical behavior of (S)-MA was similar to (R)-MA on OPPy/ GR-modified GC electrode (Fig. C.4). It reveals poor selectivity of the OPPy/GR-modified electrode towards (S)- and (R)-MA. However, the response of chiral surface towards (R)- and (S)-enantiomers was different. The oxidation peak intensity of (S)-MA was larger than that of (R)-MA, with a little potential shift. It indicates that DEX can act as a chiral selector, providing an efficient enantioselective interface (Fig. D.4). The above results confirm that the OPPy-DEX/GR chiral sensing platform showed distinguishable enantioselectivity for MA detection, whereas no enantioselectivity was observed for other modified electrodes and bare GCE. These results prove the enantioselectivity character of DEX. 3.7. Effects of scan rates Scan rate studies is an important diagnostic criterion to assess reversibility of the reactions and to determine whether a process on biosensor surface is diffusion-controlled or adsorption-controlled [33]. As the scan rate increased from 5 to 100 mVs− 1, the peak potential shifted towards more positive potential, as expected for an irreversible oxidation process. A linear relationship between peak current and the square root of the scan rate, ranged from 5 to 100 ms− 1 (Fig. 5), showed that the MA enantiomers electrooxidation are diffusion-controlled and is described by the following equations:

Fig. 5. (A) DP voltammograms of (R)-MA, (S)-MA and their racemate on OPPy-DEX/GR/ GCE at concentrations of 20 mM after moving average baseline correction, (B) Mechanisms for mandelic acid enantiomers electrooxidation.

equilibrium in the porous matrix is a slow process, which is caused by the small “effective” diffusion coefficient values of MA enantiomers in the nanopores, due to its comparatively large molecular mass.

i p (S − MA) = 5.28E − 04 ν1/2 − 4E − 06 (V s−1) (R2 = 0.9976)

3.8. Density functional theory study

i p (R − MA) = 4.76E − 04 ν1/2 − 3E − 06 (V s−1) (R2 = 0.9976)

Based on the Dagliesh three-point interaction model, a multitude interaction between the chiral selector and analyte is required for chiral discrimination, and at least one of these interactions has to be stereoselective [34]. In regard to configuration of MA enantiomers in the face to DEX, there are different possibility of H-bonding formation between the fluorine, hydroxyl and carbonyl units of DEX and the hydroxyl and carboxyl groups of the analyte. Docking analysis showed that the Renantiomer adopts a more favorable geometry for hydrogen bonding than the S - enantiomer, which is evidenced by the formation of two hydrogen bonds. The lengths and schematic of these H-bonds are shown in Fig. 6. The calculated energies of various models using DFT calculations, confirmed that the model in which the hydroxyl moiety of (R)-

The diffusion coefficients for (S)- and (R)-MA were calculated based on the slopes of the linear dependence of the anodic peak currents on the square root of the potential sweep rates (Fig. C.5) and the RandlesSevcik Eq. [30]:

IP = (2.99 × 105) (αnα )1/2C∗D1/2ν1/2 The slightly smaller diffusion coefficient of the (R)-enantiomer in comparing with (S)-enantiomer (DR / DS = 0.813) at pH 7.0 may be due to the stronger interaction between DEX as chiral selector and (R)MA, resulting in a slower diffusion rate. Generally, analysis of experimental data demonstrates that the establishment of diffusion 87

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Fig. 6. Models of the binding between dexamethasone and (R), (S)-mandelic acid obtained from the Docking approach. The hydrogen bonding interactions of (A) (S)-mandelic acid (B) (R)mandelic acid with dexamethasone. The chemical structure of (C) Dexamethasone, (D) (R)-Mandelic acid and (E) (S)-Mandelic acid.

Mandelic acid has been entrusted with hydroxyl and the fluorine groups of DEX, had the smallest value of energy. Thus, the (R)-MA can be retained stronger than (S)-MA. Based on the results of docking analysis and DFT calculations, despite more chiral sites of DEX, this chiral selector has the ability to establish lower hydrogen bonds compared to biotin in our previous work [17]. This difference in affinity can be due to different functional groups of the two chiral structures. Therefore, taking into account the different characteristics of the matrix and affinity of the chiral selectors, the observed different enantioselectivity can be justified.

electrode. This accessibility can be attributed to the thermodynamic characteristics of binding affinities of enantiomers. Due to the higher binding affinity between (R)-MA and DEX, the mass transfer kinetic of (R)-MA is slower than (S)-MA. In other words, while the MA enantiomers diffuse through the porous matrix of OPPy, the DEX within the bulk matrix is responsible for enantioselective interactions. It contributes to various permeability and then accessibility to GR, which can be the preferred site for electrooxidation of MA enantiomers. 3.10. Optimization of the method The following parameters were optimized: (a) film thickness; (b) measurement time and (c) sample pH, and the experimental conditions which found to give the best results were as: film thickness of five CV cycles; measurement time of 30 s and sample pH of 7.

3.9. Mechanism of enantiorecognition Based on the obtained theoretical and experimental results, the chiral recognition mechanism can be proposed in regard to thermodynamic and kinetic behaviors of MA enantiomers on the chiral-modified platform. The stronger oxidation peak of the (S)-enantiomer implies that it is more accessible to electroactive sites of the modified

3.11. Multivariate analysis of synthetic mixtures of Mandelic acid enantiomers Despite the minor discrimination of DPV signals of MA enantiomers

Table 1 The composition of the calibration samples. Sample no.

(S)-MA

(R)-MA

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 1 1 1 6 6 6 6 11 11 11 11 16 16 16 16

1 6 11 16 1 6 11 16 1 6 11 16 1 6 11 16

Fig. 7. Voltammograms of the prepared MA enantiomers mixtures (according to Table 1).

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calibration set, but randomly designed. The next important step to enhance the prediction power of calibration model is the preprocessing of raw data to remove irrelevant data [36]. In this study, three data preprocessing methods were used to achieve better results: (a) autoscaling, (b) first derivative preprocessing, and (c) moving-average baseline correction. The results of the first derivative data and the raw data were approximately the same. Since the potential shift in sample profiles is a common problem in voltammetric studies, the moving-average baseline correction was used to tackle this problem. It resulted in a simpler calibration model with reduced factors and better prediction ability. Then, the cross validation was used to choose the optimal latent variables (LVs) of this system. Estimation of the pseudo-rank of the raw experimental data is critical part of PLS procedure and other factor-based methods [36]. Constructing the model with extra number of LV leads to overfitting due to noise introduction. On the other hand, selecting fewer number of LV may cause to discard informative data. Both of them lead to poor prediction power of calibration model [36]. The optimum numbers of five LVs were used to construct the calibration models of (S)-MA and (R)MA, which explained more than 99% of the concentration variance (Tables 2, 3). It is expected that two factors contribute into the model due to the presence of two enantiomers in real system. However, the extra number of factors may be due to the other sources of variance such as, interaction among enantiomers, background contribution, potential shift, and variation of the shape of voltammograms as a function of concentration [37]. PLS methods have the advantages of full spectrum analysis, in the cases that there are many wavelengths or potentials. However, it has been recognized that the irrelevant variables, which do not contribute to model formulation, may spoil the prediction ability of the established calibration model [38]. Variable selection using genetic algorithm is one way to filter the response matrix and delete variables that do not match the calibration model. Finally, PLS-1 and PLS-2 methods were applied to the original data and GA selected potentials matrices. The better predictive ability of the PLS model with selected potentials compared to the full voltammogram PLS model, confirms the improvement in the regression performance by combined GA-PLS algorithm (Table 4). Furthermore, it was found that GA-PLS-1 and GA-PLS-2 methods performed about equally for (S)-MA and (R)-MA. Also, it should be noted that the full voltammogram-PLS methods gave a slightly better result for (S)-MA than (R)-MA, attributing to the nonlinear behavior of the system.

Table 2 Statistical parameters for (S)-MA enantiomer dataset using PLS-1, PLS-2, GA-PLS-1 and GA-PLS-2 methods. Method

Full potential PLS-1

Full potential PLS-2

GA-PLS1

GA-PLS-2

Number of potentials Number of factors R2 for calibration set RMSE for calibration set Recovery for calibration set R2 for validation set RMSE for validation set Recovery for validation set

185 2 0.9466 1.24 100 0.68 2.43 118.21

185 7 0.9993 0.14 100 0.75 3.05 119.04

5 5 0.9974 0.27 100 0.97 0.77 90.2

5 5 0.9974 0.27 100 0.96 0.77 91.2

Table 3 Statistical parameters for (R)-MA enantiomer dataset using PLS-1, PLS-2, GA-PLS-1 and GA-PLS-2 methods. Method

Full potential PLS-1

Full potential PLS-2

GA-PLS-1

GA-PLS-2

Number of potentials Number of factors R2 for calibration set RMSE for calibration set Recovery for calibration set R2 for validation set RMSE for validation set Recovery for validation set

185 6 0.9988 0.17

185 7 0.9966 0.30

5 5 0.9889 0.53

5 5 0.9872 0.57

100

100

100

100

0.75 4.9

0.61 5.6

0.96 1.2

0.96 1.2

84.37

83.75

105.15

104.25

on OPPy-DEX/GR-modified surface, the simultaneous determination of these stereoisomers in their mixtures was not possible by the conventional voltammetric method. To overcome the problem of highly overlapping signals of MA enantiomers, the multivariate regression methods of PLS-1 and PLS-2 were applied to the data matrices of full voltammograms and GA-selected data. The first step to improve the prediction ability and optimize the performance of the chemometrics calibration model is a suitable experimental design which spans the entire sample space. For this aim, four-level orthogonal array, denoted by OA16 (44), was used as a design method to guarantee that there is no correlation between the concentrations of MA enantiomers (Table 1). The correlation between the different calibration samples has to be avoided because colinear components in the training set data will tend to cause under-fitting in the PLS models [35]. A calibration set was prepared according to Table 1, which includes the concentration range of 1.0–25.0 mM. The DP voltammograms for each of the mixture samples were recorded between + 800 and + 1700 mV to produce a data matrix with 16 rows and 185 columns (Fig. 7). Another set of samples consisting of 5 synthetic mixtures was then used to evaluate the prediction ability of the calibration models. The composition of the test set was within the ranges of

4. Conclusions Current achievements showed that the electrochemical enantioselective sensor based on DEX as chiral selector and nanocomposite of overoxidized polypyrrole and graphene, associated with chemometrics methods, can be used for resolving the highly overlapping signals of MA enantiomers. By comparison of the current electrochemical sensor, with those previously reported for selective detection of MA enantiomers, fast response time, reusability, stability and reagent-less nature of the sensor are the advantages of this chiral sensor. Based on the correlation between the observed chiral selectivity in the

Table 4 Actual concentration and found concentration of test set. Sample no.

1 2 3 4 5

Actual concentration (mM)

PLS-1

PLS-2

(S)-MA

(R)-MA

(S)-MA

(R)-MA

(S)-MA

(R)-MA

(S)-MA

(R)-MA

(S)-MA

(R)-MA

10 8 5 4 3

20 3 10 5 9

13.41 6.32 6.59 4.33 4.79

10.78 5.61 − − 10.62

15.11 6.75 − − 3.91

10.75 5.44 − − 10.55

8.24 7.36 5.16 3.06 3.23

20.64 4.52 9.02 4.39 10.83

8.79 7.25 5.13 3.02 3.16

19.96 4.65 9.03 4.44 10.91

89

GA-PLS-1

GA-PLS-2

Journal of Electroanalytical Chemistry 805 (2017) 83–90

M. Borazjani et al.

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