Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and Fingerprinting

Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and... Abstract Nowadays, increasingly more individuals turn to supplementation of the diet with herbal medicines and many such products are marketed lately. Thus the problem that this article focuses on is that these products are not subjected to rigorous quality control like synthetic drugs are, which rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do. As a solution, micellar electrokinetic chromatography and high performance liquid chromatography were investigated in order to fingerprint and authenticate herbal medicines. For this purpose, minimal sample pre-treatment was applied to several fruit based herbal medicines, which were compared with the ethanolic extract of the respective fruit. The holistic evaluation of the electropherograms and chromatograms was made by using appropriate chemometric tools, such as principal component analysis (PCA), cluster analysis and a combination of PCA and linear discriminant analysis (PCA-LDA). The results suggest that the developed method was able to successfully discriminate between different herbal medicines, based on their raw material content. Moreover, this simple and efficient methodology might also be used for routine screening and authenticity control of different products and could be implemented in any quality control laboratory. Introduction Fruits and vegetables have always been considered as part of a healthy and balanced diet, which increasingly more people are trying to achieve. But because living nowadays, most often implies a chaotic life, increasingly more individuals turn to supplementation of the diet with herbal medicines. For this reason, hundreds, perhaps thousands of such products are marketed lately, but the problem is that these products are not subjected to rigorous quality control like synthetic drugs are, and this rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do (1). Because of their complex composition, the development of a suitable analytical procedure to separate and evaluate all the constituents of herbal medicines is very difficult, impractical and not to mention, time consuming. Therefore, the global/holistic evaluation of these samples seems more suitable, instead of focusing on individual compounds; and fingerprinting methods fit this challenge by emphasizing and comprehensively characterizing the analyzed sample (2). The fingerprinting method was firstly introduced for the characterization of herbal medicines and extended to other types of vegetal materials. Even more, nowadays it is widely involved in the authenticity and origin control of food, herbs or derived products (3, 4). The Food and Drug Administration (5) and the European Medicines Agency (6) recommend that the chromatographic techniques are the most appropriate for fingerprinting procedures. Thus, in the last years, many methods have been developed for fingerprinting different samples, including thin layer chromatography/high performance thin layer chromatography (TLC/HPTLC) (7–9), high performance liquid chromatography (HPLC) and gas chromatography (GC) (10, 11), highly speed counter current chromatography (HSCCC) (12), capillary zone electrophoresis (CZE) (13–15) and micellar electrokinetic chromatography (MEKC) (16). These techniques are successfully completed by spectroscopic techniques, such as nuclear magnetic resonance or mass spectrometry and representative results, comparable to those obtained by chromatography were also obtained using IR or UV–Vis spectroscopy (17). Although the most often used method for fingerprinting and authentication remains the HPLC, in the last years, capillary electrophoresis (CE) has also been employed in such studies, however with a lower frequency. At a close inspection of the literature, it seems that CE has been used mostly for applications like: DNA fingerprinting of different samples, wine discrimination and determination of polyphenols from olive oil (18). However, the main advantages of this method, like no need for organic solvent, higher resolution than HPLC, the ability of chiral separation, injection of the sample without pre-treatment and small amount of sample used for analysis, made CE a convenient alternative for fingerprinting, discrimination and authentication of herbal medicines. Fingerprinting techniques are not necessarily focused on the identification of all metabolites, but rather on the recognition of patterns, so-called matrix fingerprint (19), which indicates the presence of a compound or of a certain class of compounds, in the given experimental conditions. Being a qualitative analysis this technique permits the accurate authentication and identification of herbal medicines, even if the concentration of the chemically characteristic constituents is not exactly the same for different samples containing the same herb (20). However, the similarities or differences between samples can only be highlighted by using the appropriate chemometric methods applied to the chromatographic, electrophoretic or spectral data, because otherwise the complexity of the data obtained from the analytical methods does not allow the visualization of clustering patterns. The most frequently used methods for this purpose are the multivariate exploratory techniques, like Principal Component Analysis (PCA), Partial Least Squares (PLS), Cluster Analysis (CA), Linear Discriminant Analysis (LDA) and Artificial Neuronal Networks (ANN) (21, 22). In view of the above considerations, the aim of this study was the chemical fingerprinting, authentication and discrimination of herbal medicines based on their herbal content, through analytical methods coupled with advanced chemometrics for the statistical data treatment. Also, the study aimed to compare the results obtained using two different separation techniques, HPLC and MEKC. Experimental Materials and sample preparation All analytical grade solvents (ethanol, methanol, acetonitrile) were purchased from Merck KGaA (Darmstadt, Germany), reagents (sodium hydroxide, disodium tetraborate, sodium dodecyl sulfate) were purchased from Reanal (Hungary) and a number of 19 samples were acquired from local stores. As presented in Table I, Samples 1–6 are based on cranberry extract/fruit, Samples 8–11 are based on bilberry extract/fruit and Samples 13–16 are based on sea-buckthorn extract/fruit, also Samples 17 and 18 contain both sea-buckthorn and bilberry extracts/fruits and Sample 19 contains cranberry and bilberry extracts/fruits. Table I. Information about the Investigated Samples Code  Sample compositiona  Comercial name  Manufacturer  1  Cranberry fruits  Dried cranberry fruit  Dragon Superfoods  2  Cranberry extract  Cranberry powder  Dacia Plant  3  203 mg cranberry extract, excipients  Urinal  Walmark  4  350 mg cranberry fruit, 100 mg cranberry extract, excipients  Cranberry extract  Dacia Plant  5  367 mg cranberry extract, excipients  Uro-Magic  Parapharm  6  300 mg cranberry extract, 145 mg neem extract, excipients  Urisan  Sun Wave Pharma  7  Dried bilberry powder  Dried billberry powder  Natural Health  8  80 mg bilberry extract, excipients  Billberry extract  Rotta Natura  9  25 mg bilberry extract, 100 mg vit. C, 2 mg beta-carotene, lutein, copper, zinc, vit. E, excipients  Vizual Forte  Cosmopharm  10  70 mg bilberry fruit, 10 mg beta-carotene, 45 mg vit. E, excipients  Vizual  Alevia  11  190 mg bilberry fruit, excipients  Dried billberry powder  Pro Natura  12  Dried sea-buckthorn powder  Dried sea-buckthorn powder  Natural Health  13  42.5 mg sea-buckthorn fruit, rosehip extract, vit. C, lemon essential oil, excipients  Fortifier  Fares  14  150 mg sea-buckthorn extract, 350 mg spirulina biomass, excipients  Sea-buckthorn extract  Dacia Plant  15  170 mg sea-buckthorn fruit, 100 mg spirulina biomass, excipients  Spirofort  Parapharm  16  354 mg sea-buckthorn extract, 500 mg spirulina biomass, excipients  Sea-buckthorn extract  Hofigal  17  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 150 mg goji extract, 150 mg rosehip fruit, vit. C, excipients  Billberry & Sea-buckthorn extract  Fares  18  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 80 mg gymnema extract, 80 mg fenugreek extract, 20 mg green tea extract, 20 mg ginkgo biloba extract, excipients  Diabevital B  Parapharm  19  147 mg cranberry fruit, bilberry fruit, olive leaf extract, poplar buds, nasturtium, cloves, juniper fruits, excipients  Diurosept U68  Fares  Code  Sample compositiona  Comercial name  Manufacturer  1  Cranberry fruits  Dried cranberry fruit  Dragon Superfoods  2  Cranberry extract  Cranberry powder  Dacia Plant  3  203 mg cranberry extract, excipients  Urinal  Walmark  4  350 mg cranberry fruit, 100 mg cranberry extract, excipients  Cranberry extract  Dacia Plant  5  367 mg cranberry extract, excipients  Uro-Magic  Parapharm  6  300 mg cranberry extract, 145 mg neem extract, excipients  Urisan  Sun Wave Pharma  7  Dried bilberry powder  Dried billberry powder  Natural Health  8  80 mg bilberry extract, excipients  Billberry extract  Rotta Natura  9  25 mg bilberry extract, 100 mg vit. C, 2 mg beta-carotene, lutein, copper, zinc, vit. E, excipients  Vizual Forte  Cosmopharm  10  70 mg bilberry fruit, 10 mg beta-carotene, 45 mg vit. E, excipients  Vizual  Alevia  11  190 mg bilberry fruit, excipients  Dried billberry powder  Pro Natura  12  Dried sea-buckthorn powder  Dried sea-buckthorn powder  Natural Health  13  42.5 mg sea-buckthorn fruit, rosehip extract, vit. C, lemon essential oil, excipients  Fortifier  Fares  14  150 mg sea-buckthorn extract, 350 mg spirulina biomass, excipients  Sea-buckthorn extract  Dacia Plant  15  170 mg sea-buckthorn fruit, 100 mg spirulina biomass, excipients  Spirofort  Parapharm  16  354 mg sea-buckthorn extract, 500 mg spirulina biomass, excipients  Sea-buckthorn extract  Hofigal  17  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 150 mg goji extract, 150 mg rosehip fruit, vit. C, excipients  Billberry & Sea-buckthorn extract  Fares  18  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 80 mg gymnema extract, 80 mg fenugreek extract, 20 mg green tea extract, 20 mg ginkgo biloba extract, excipients  Diabevital B  Parapharm  19  147 mg cranberry fruit, bilberry fruit, olive leaf extract, poplar buds, nasturtium, cloves, juniper fruits, excipients  Diurosept U68  Fares  aQuantities are given per tablet/capsule. The bold text refers to the fruit extract that the herbal medicine was made from. The investigated samples, dried fruits and herbal medicines, were purchased in powdered form and tablets/capsules, respectively, and they were used directly for extraction. The extraction solvent was selected to have the ability to extract as many bioactive compounds as possible from the investigated samples and the extraction method was the ultrasound-assisted one. Thus, 1 g of the powdered fruit and different quantities of the herbal medicines (the used amount was in accordance with the manufacturer’s specifications for the concentration of fruit that was used to prepare the tablets/capsules) were moistened with 10 mL ethanol for 10 min, then extracted for 30 min using an Elma TI-H-5 ultrasonic bath (Elma- Hans Schmidbauer, Singen). Each extraction was performed in duplicate. Extracts were separated from the vegetable material/excipients by filtration through 0.45 μm micro-filters. The extracts thus obtained were used for the electrophoretic analysis and for the HPLC analysis the extracts needed dilution by 10-fold. All the solutions were stored at 4°C until final analysis. Electrophoretic separation analysis The electrophoretic measurements were carried out using the micellar electrokinetic chromatographic method (MEKC). All the experiments were conducted using an Agilent G1600 CE Instrument fitted with a diode array detector (DAD) for UV–Vis detection and a thermostating compartment for temperature-control of the capillary. Separations were carried out using a polyimide-coated fused-silica capillary of 68.5 cm (effective length was 60 cm) and 50 μm id (Polymicro Technology, Phoenix, USA). The samples solutions were introduced at the anodic end of the capillary by hydrodynamic sample injection (50 mbar, 2 s) and the applied voltage was +25 kV. The temperature of the capillary holder was kept constant at 25°C. The detection was carried out by on column diode array photometric measurement at 200 nm. A new capillary was washed for 30 min with 1 M NaOH and before each injection the capillary was preconditioned with 1 M NaOH for 3 min and with background electrolyte (BGE) for 6 min. The BGE consisted of 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3). After daily use the capillary was flushed with 1 M NaOH (10 min), 100 mM SDS (10 min) and distilled water (5 min) to remove all components which tend to adsorb onto the capillary wall. The electropherograms were recorded and processed by ChemStation 7.01 software version (Agilent). HPLC separation analysis The chromatographic measurements were performed on an Agilent 1100 Series LC system consisting of a vacuum degassing unit, a binary high pressure pump, a standard automatic sample injector, a column thermostat and a DAD. In order to obtain the fingerprints of the investigated samples, reversed phase HPLC analysis was employed for the separation of bioactive compounds and data acquisition and processing was done by Agilent Chemstation software. The experiments were carried out using an HPLC Zorbax, Eclipse XDB-C18 column (4.6 mm × 100 mm, 3.5 μm—particle size) and the elution was made using a mixture of two mobile phases A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: first 13 min 5–45% B, next 2 min of linear rise up to 66% B, followed by 8 min linear rise up to 95% B, next 5 min of isocratic elution with 95% B, followed by 2 min of linear rise to 100% B. An additional 10 min of post-run with 5% B was employed between samples to equilibrate the column. The injection volume was 15 μL of sample, the detection was performed at 280 nm and the flow rate was 1 mL/min. Data analysis After chromatographic and electrophoretic separations, the obtained chromatograms and electropherograms were digitized and saved as CSV files for further chemometric analysis. Thus, two matrices were obtained, each having as dimensions: 19 samples × 900 variables (absorbance units, at 280 nm) in case of the HPLC analysis and 19 samples × 854 variables (absorbance units, at 200 nm) in case of the MEKC analysis, respectively. These matrices were further used for all chemometric analyses (PCA, CA and Discriminant Analysis), which were carried out using the Statistica 8.1 software (StatSoft, Tulsa, USA). The digitized chromatograms/electropherograms were used for the chemometric analysis without any data pre-treatment. The average of two measurements was used for the chemometric analysis; and the problem concerning the difference in migration time for duplicate samples was solved by adjustment/normalization of the chromatogram/electropherogram using the Chemstation software. Also by using the Statistica software, PCA was carried out via the correlation matrix, in which case the data are automatically standardized. Results Considering the investigated samples, herbal medicines based on cranberry, bilberry and sea-buckthorn extract/fruit, their therapeutic use is derived mainly from their high content of polyphenols. Consequently the separation techniques used in this study were selected and optimized accordingly. Optimization of HPLC method To achieve efficient resolution and as many signals as possible, mixtures of solvent A—water or acidified water (0.1% formic acid) and solvent B—methanol or methanol:acetonitrile (1:1) as mobile phase were tested. Finally, solvent A—acidified water (0.1% formic acid) and solvent B—methanol:acetonitrile (1:1) were selected as the best elution system, as the acidified water gave better peak shapes and the mixture of methanol and acetonitrile had better elution power. Also, isocratic and several gradient conditions, selected from literature and adapted, were tested to optimize the HPLC separation. As the sample’s components were barely separated under isocratic conditions, gradient elution was used instead and the best results were obtained using an adapted version of our previous work (10), method which is presented in detail in “HPLC separation” chapter. Optimization of MEKC method On the other hand, the MEKC method is generally applicable for the separation of neutral components and this technique also needed some optimization. The bioactive compounds from the investigated samples have in their structure aromatic rings and differ in their pattern of hydroxylation, methylation and glycosylation. Accordingly they could be ionizable or neutral. The interaction between the polyphenols and the negatively charged micelles of the buffer depends on the charge value and the hydrophobicity of the compounds. The MEKC has a resolving effect on the neutral polyphenols, while the charged ones have small interactions with the micelles. Also, as the polyphenols strongly interact with the micelles due to the hydrophobic properties therefore the resolution may be varied by modifying the micellar phase. The addition of organic solvents to the BGE containing surfactant (SDS) is commonly used modifier in order to improve the selectivity (23). At pH 9.3 in borate buffer polyphenols are negatively charged due to the dissociation of phenolic groups (pK ≈ 9), thus they migrate according to their charge-to-size ratio. The 50 mM concentration of SDS resolves the neutral components. The borate has a complexation effect on the glycosides enhancing selectivity. The post-conditioning procedure was also optimized, based on our previous work (24, 25), thus the capillary was flushed with 100 mM of SDS for 10 min, after daily use in order to remove adsorbed components from the capillary wall. Selection of optimum detection wavelength As for the detection of the bioactive compounds, DAD detection was considered to be a good choice, as the structure of these compounds allows them to have strong UV absorbance at different wavelengths. Therefore, in both MEKC and HPLC analysis, different UV wavelengths were tested: 200, 214, 250, 280, and 365 nm and 360 and 280, respectively. Thus, the best results were obtained using 200 nm for the MEKC analysis and 280 nm for the HPLC analysis, examples of electropherogram and chromatogram are presented in Figure 1a and b. Figure 1. View largeDownload slide (a) MEKC polyphenolic profile of Sample 12 registered at 200 nm, using 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3) as BGE and (b) RP-HPLC polyphenolic profile of Sample 12 registered at 280 nm, using a mixture of A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: 0–13 min, 5–45% B; 13–15 min, 45–66% B; 15–23 min, 66–95% B; 23–28 min, 95% B; 28–30 min, 95–100% B. Figure 1. View largeDownload slide (a) MEKC polyphenolic profile of Sample 12 registered at 200 nm, using 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3) as BGE and (b) RP-HPLC polyphenolic profile of Sample 12 registered at 280 nm, using a mixture of A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: 0–13 min, 5–45% B; 13–15 min, 45–66% B; 15–23 min, 66–95% B; 23–28 min, 95% B; 28–30 min, 95–100% B. Chemometric analysis Furthermore, in order to obtain the authentication of herbal medicines several chemometric approaches were tested, considering the sample’s polyphenolic profiles (chromatograms and electropherograms, respectively) as analytical information. By a careful examination of the obtained chromatograms and electropherograms, a visual differentiation among different samples could be done, but the process would be subjective and also small differences between related samples might be missed. Consequently, a chememometric approach for the data evaluation is more suited and in this study CA, PCA and LDA were used. Among the chemometric methods, PCA is the most preferred because it is considered to reduce dimensionality of the original dataset by explaining the correlation amongst a large number of variables in terms of a smaller number of underlying PCs without losing much information. The PCs are a very useful tool for examining the relationships between objects, looking for groups and trends but is highly sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. Another unsupervised clustering procedure is CA with its hierarchical and non-hierarchical approaches generally used to sort samples into groups. The Ward’s method as the amalgamation rule and the squared Euclidean distance as metric are usually used to establish clusters or distributed as dendrograms in case of different classes of compounds. In the multivariate analysis of data, PCA and CA methods are completed by LDA which is a supervised classification technique based on the linear discriminant functions, which maximizes the ratio of between-class variance and minimizes the ratio of within-class variance (26). Usually the Euclidean distance is used in the LDA algorithms to select directions which accomplish maximum separation among the given classes and stepwise algorithm to extract the most important variables (8). Furthermore, the combination of the PCA and LDA may offer some remarkable information for classification and discrimination of the considered samples (8, 10). Discussion The dendrograms (Figure 2a and b) obtained by applying the CA on the digitized electropherograms and chromatograms, offer some information about the similarities/dissimilarities observed between the analyzed samples. The Ward’s method used for cluster building is regarded as being one of the most efficient rules of amalgamation, because it uses an analysis of variance approach to evaluate the distance between clusters. The procedure is more efficient when the distance between clusters is computed by squared Euclidean method, which is not affected by the addition of new objects to the analysis or by outliers. As it can be observed in both cases Sample 9 was the most differentiated sample from the others, forming a group of its own, and this can be attributed to the fact that this sample contains the least amount of extract/tablet (Table I). Also, the layout of the samples in the dendrograms (from right to left) was made in increasing order of the distances between them, suggesting once again that Sample 9 is the most different of all. On the other hand, generally good clustering was obtained for samples containing cranberry and sea-buckthorn, regardless of the analytical technique that was used for the separation. However, better classification of the mixed samples was obtained using the electrophoretic separation. Thus, Sample 19, which contains both cranberry and bilberry extract/fruit, was grouped with Sample 6, which is based on cranberry extract; Sample 18, which contains both bilberry and sea-buckthorn fruits, was closely placed to Sample 8, which is based on bilberry extract. In case of Sample 17 (contains both bilberry and sea-buckthorn fruits) the classification using electrophoretic data was quite different than expected, but on the contrary, when the chromatographic data were used, this sample was classified in a group with Sample 12, which is based on sea-buckthorn fruits. Figure 2. View largeDownload slide Dendograms obtained by applying CA using Ward’s method as the amalgamation rule and the squared Euclidean distance as metric, on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 2. View largeDownload slide Dendograms obtained by applying CA using Ward’s method as the amalgamation rule and the squared Euclidean distance as metric, on data matrices of digitized: (a) electropherograms and (b) chromatograms. Further, PCA was used to reduce the dimensionality of the original dataset by explaining the correlation among a large number of variables on the basis of a smaller number of principal components (PCs) without much loss of information. By applying PCA on the digitized chromatograms and electropherograms, the first 18 PCs explain the total variance (100%) of the data. The projection of PC1 vs. PC2 is presented in Figure 3a, for the electrophoretic data and Figure 3b, for the chromatographic data, respectively. As it is presented, the first two PCs obtained from the chromatographic data account for more than 84% of the variance, while the two PCs corresponding to electrophoretic data account for approximately 56% of the variance, respectively. The PCA results, although more illustrative, are in good agreement with those obtained with CA. Thus it can be observed, that Sample 9 was separated from the larger group, but also Sample 18 had the same tendency and all the other samples were clustered, more or less in the same group. These two samples have clearly different composition compared to the other samples, Sample 9 by having the least ingredients/tablet and Sample 18 by having the most ingredients/tablet. Figure 3. View largeDownload slide 2D projections of PC1 vs. PC2 obtained by applying PCA on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 3. View largeDownload slide 2D projections of PC1 vs. PC2 obtained by applying PCA on data matrices of digitized: (a) electropherograms and (b) chromatograms. The low discrimination obtained in both cases, HPLC and MEKC, can be attributed to the fact that most of the samples are mixtures of different ingredients (extracts/fruits) which contain large amounts of flavonoids and polyphenolic compounds and they cannot be discriminated by any of the multivariate classical methods (CA and PCA). Regarding PCA, it is well documented that in many cases, more than two or three significant PCs are necessary to adequately characterize the data. In these cases there are more possible graphs and, as a direct consequence, the information retained in a larger number of PCs is dissipated. However, this situation can be proficiently resolved by using a combination of PCA with LDA which could lead to a more efficient discrimination of the investigated samples, according to our previous work (8, 10) and other relevant applications (26, 27). In this way, the variance covariance matrix of the new variables becomes a diagonal matrix, because the scores are orthogonal and the number of PCs is less than or equal to the number of samples. LDA is a supervised classification technique based on linear discriminant functions which maximize between-class variance and minimize within-class variance. The Euclidean distance was used in the LDA algorithms to classify unknown samples and the stepwise algorithm was used to extract the most important variables. The results obtained by applying LDA to the scores corresponding to the first 15 PCs indicate a total separation of samples (100%) within four groups, in good agreement with the nature of the raw material used for their preparation and independently of the separation technique. The Root1–Root2 score plots (Figure 4a and b) illustrate well differentiated groups of samples based on cranberries, bilberries, sea-buckthorn and mixtures, without any overlapping. Although the concentration of fruit/extract was very different in each sample, the proposed combination of PCA-LDA was able to successfully classify the samples according to the nature of their raw material. Also, the results indicate that regardless of the separation technique, the classification of samples was made along the Root1 axis for the mixtures (Samples 17, 18 and 19) and mostly along the Root2 axis for the other samples. Figure 4. View largeDownload slide Plot of Root1 vs. Root2 scores obtained by applying PCA-LDA methodology on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 4. View largeDownload slide Plot of Root1 vs. Root2 scores obtained by applying PCA-LDA methodology on data matrices of digitized: (a) electropherograms and (b) chromatograms. Conclusions Fingerprinting, discrimination and authentication of dietary supplements were achieved using HPLC and MEKC combined with chemometric evaluation of data. The results suggested that the combination of PCA with LDA leads to more powerful classification and discrimination of samples, according to their raw material composition. Also, because the results did not show significant differences by using the two separation techniques, it is suited to use either of them for similar experiments, with respect to their advantages/disadvantages. However, the results indicated that the reproducibility of HPLC is better. Furthermore, the simple and efficient methodology developed in this paper might also be used for routine screening and authenticity control of different products (herbal medicines, drugs, food, etc.) and could be implemented in any quality control laboratory. Funding M.A. is grateful for funding from National Research, Development and Innovation Office, Hungary (grant number: NKFI K111932). 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Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Chromatographic Science Oxford University Press

Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and Fingerprinting

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© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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0021-9665
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1945-239X
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10.1093/chromsci/bmx080
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Abstract

Abstract Nowadays, increasingly more individuals turn to supplementation of the diet with herbal medicines and many such products are marketed lately. Thus the problem that this article focuses on is that these products are not subjected to rigorous quality control like synthetic drugs are, which rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do. As a solution, micellar electrokinetic chromatography and high performance liquid chromatography were investigated in order to fingerprint and authenticate herbal medicines. For this purpose, minimal sample pre-treatment was applied to several fruit based herbal medicines, which were compared with the ethanolic extract of the respective fruit. The holistic evaluation of the electropherograms and chromatograms was made by using appropriate chemometric tools, such as principal component analysis (PCA), cluster analysis and a combination of PCA and linear discriminant analysis (PCA-LDA). The results suggest that the developed method was able to successfully discriminate between different herbal medicines, based on their raw material content. Moreover, this simple and efficient methodology might also be used for routine screening and authenticity control of different products and could be implemented in any quality control laboratory. Introduction Fruits and vegetables have always been considered as part of a healthy and balanced diet, which increasingly more people are trying to achieve. But because living nowadays, most often implies a chaotic life, increasingly more individuals turn to supplementation of the diet with herbal medicines. For this reason, hundreds, perhaps thousands of such products are marketed lately, but the problem is that these products are not subjected to rigorous quality control like synthetic drugs are, and this rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do (1). Because of their complex composition, the development of a suitable analytical procedure to separate and evaluate all the constituents of herbal medicines is very difficult, impractical and not to mention, time consuming. Therefore, the global/holistic evaluation of these samples seems more suitable, instead of focusing on individual compounds; and fingerprinting methods fit this challenge by emphasizing and comprehensively characterizing the analyzed sample (2). The fingerprinting method was firstly introduced for the characterization of herbal medicines and extended to other types of vegetal materials. Even more, nowadays it is widely involved in the authenticity and origin control of food, herbs or derived products (3, 4). The Food and Drug Administration (5) and the European Medicines Agency (6) recommend that the chromatographic techniques are the most appropriate for fingerprinting procedures. Thus, in the last years, many methods have been developed for fingerprinting different samples, including thin layer chromatography/high performance thin layer chromatography (TLC/HPTLC) (7–9), high performance liquid chromatography (HPLC) and gas chromatography (GC) (10, 11), highly speed counter current chromatography (HSCCC) (12), capillary zone electrophoresis (CZE) (13–15) and micellar electrokinetic chromatography (MEKC) (16). These techniques are successfully completed by spectroscopic techniques, such as nuclear magnetic resonance or mass spectrometry and representative results, comparable to those obtained by chromatography were also obtained using IR or UV–Vis spectroscopy (17). Although the most often used method for fingerprinting and authentication remains the HPLC, in the last years, capillary electrophoresis (CE) has also been employed in such studies, however with a lower frequency. At a close inspection of the literature, it seems that CE has been used mostly for applications like: DNA fingerprinting of different samples, wine discrimination and determination of polyphenols from olive oil (18). However, the main advantages of this method, like no need for organic solvent, higher resolution than HPLC, the ability of chiral separation, injection of the sample without pre-treatment and small amount of sample used for analysis, made CE a convenient alternative for fingerprinting, discrimination and authentication of herbal medicines. Fingerprinting techniques are not necessarily focused on the identification of all metabolites, but rather on the recognition of patterns, so-called matrix fingerprint (19), which indicates the presence of a compound or of a certain class of compounds, in the given experimental conditions. Being a qualitative analysis this technique permits the accurate authentication and identification of herbal medicines, even if the concentration of the chemically characteristic constituents is not exactly the same for different samples containing the same herb (20). However, the similarities or differences between samples can only be highlighted by using the appropriate chemometric methods applied to the chromatographic, electrophoretic or spectral data, because otherwise the complexity of the data obtained from the analytical methods does not allow the visualization of clustering patterns. The most frequently used methods for this purpose are the multivariate exploratory techniques, like Principal Component Analysis (PCA), Partial Least Squares (PLS), Cluster Analysis (CA), Linear Discriminant Analysis (LDA) and Artificial Neuronal Networks (ANN) (21, 22). In view of the above considerations, the aim of this study was the chemical fingerprinting, authentication and discrimination of herbal medicines based on their herbal content, through analytical methods coupled with advanced chemometrics for the statistical data treatment. Also, the study aimed to compare the results obtained using two different separation techniques, HPLC and MEKC. Experimental Materials and sample preparation All analytical grade solvents (ethanol, methanol, acetonitrile) were purchased from Merck KGaA (Darmstadt, Germany), reagents (sodium hydroxide, disodium tetraborate, sodium dodecyl sulfate) were purchased from Reanal (Hungary) and a number of 19 samples were acquired from local stores. As presented in Table I, Samples 1–6 are based on cranberry extract/fruit, Samples 8–11 are based on bilberry extract/fruit and Samples 13–16 are based on sea-buckthorn extract/fruit, also Samples 17 and 18 contain both sea-buckthorn and bilberry extracts/fruits and Sample 19 contains cranberry and bilberry extracts/fruits. Table I. Information about the Investigated Samples Code  Sample compositiona  Comercial name  Manufacturer  1  Cranberry fruits  Dried cranberry fruit  Dragon Superfoods  2  Cranberry extract  Cranberry powder  Dacia Plant  3  203 mg cranberry extract, excipients  Urinal  Walmark  4  350 mg cranberry fruit, 100 mg cranberry extract, excipients  Cranberry extract  Dacia Plant  5  367 mg cranberry extract, excipients  Uro-Magic  Parapharm  6  300 mg cranberry extract, 145 mg neem extract, excipients  Urisan  Sun Wave Pharma  7  Dried bilberry powder  Dried billberry powder  Natural Health  8  80 mg bilberry extract, excipients  Billberry extract  Rotta Natura  9  25 mg bilberry extract, 100 mg vit. C, 2 mg beta-carotene, lutein, copper, zinc, vit. E, excipients  Vizual Forte  Cosmopharm  10  70 mg bilberry fruit, 10 mg beta-carotene, 45 mg vit. E, excipients  Vizual  Alevia  11  190 mg bilberry fruit, excipients  Dried billberry powder  Pro Natura  12  Dried sea-buckthorn powder  Dried sea-buckthorn powder  Natural Health  13  42.5 mg sea-buckthorn fruit, rosehip extract, vit. C, lemon essential oil, excipients  Fortifier  Fares  14  150 mg sea-buckthorn extract, 350 mg spirulina biomass, excipients  Sea-buckthorn extract  Dacia Plant  15  170 mg sea-buckthorn fruit, 100 mg spirulina biomass, excipients  Spirofort  Parapharm  16  354 mg sea-buckthorn extract, 500 mg spirulina biomass, excipients  Sea-buckthorn extract  Hofigal  17  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 150 mg goji extract, 150 mg rosehip fruit, vit. C, excipients  Billberry & Sea-buckthorn extract  Fares  18  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 80 mg gymnema extract, 80 mg fenugreek extract, 20 mg green tea extract, 20 mg ginkgo biloba extract, excipients  Diabevital B  Parapharm  19  147 mg cranberry fruit, bilberry fruit, olive leaf extract, poplar buds, nasturtium, cloves, juniper fruits, excipients  Diurosept U68  Fares  Code  Sample compositiona  Comercial name  Manufacturer  1  Cranberry fruits  Dried cranberry fruit  Dragon Superfoods  2  Cranberry extract  Cranberry powder  Dacia Plant  3  203 mg cranberry extract, excipients  Urinal  Walmark  4  350 mg cranberry fruit, 100 mg cranberry extract, excipients  Cranberry extract  Dacia Plant  5  367 mg cranberry extract, excipients  Uro-Magic  Parapharm  6  300 mg cranberry extract, 145 mg neem extract, excipients  Urisan  Sun Wave Pharma  7  Dried bilberry powder  Dried billberry powder  Natural Health  8  80 mg bilberry extract, excipients  Billberry extract  Rotta Natura  9  25 mg bilberry extract, 100 mg vit. C, 2 mg beta-carotene, lutein, copper, zinc, vit. E, excipients  Vizual Forte  Cosmopharm  10  70 mg bilberry fruit, 10 mg beta-carotene, 45 mg vit. E, excipients  Vizual  Alevia  11  190 mg bilberry fruit, excipients  Dried billberry powder  Pro Natura  12  Dried sea-buckthorn powder  Dried sea-buckthorn powder  Natural Health  13  42.5 mg sea-buckthorn fruit, rosehip extract, vit. C, lemon essential oil, excipients  Fortifier  Fares  14  150 mg sea-buckthorn extract, 350 mg spirulina biomass, excipients  Sea-buckthorn extract  Dacia Plant  15  170 mg sea-buckthorn fruit, 100 mg spirulina biomass, excipients  Spirofort  Parapharm  16  354 mg sea-buckthorn extract, 500 mg spirulina biomass, excipients  Sea-buckthorn extract  Hofigal  17  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 150 mg goji extract, 150 mg rosehip fruit, vit. C, excipients  Billberry & Sea-buckthorn extract  Fares  18  100 mg bilberry fruit, 100 mg sea-buckthorn fruit, 80 mg gymnema extract, 80 mg fenugreek extract, 20 mg green tea extract, 20 mg ginkgo biloba extract, excipients  Diabevital B  Parapharm  19  147 mg cranberry fruit, bilberry fruit, olive leaf extract, poplar buds, nasturtium, cloves, juniper fruits, excipients  Diurosept U68  Fares  aQuantities are given per tablet/capsule. The bold text refers to the fruit extract that the herbal medicine was made from. The investigated samples, dried fruits and herbal medicines, were purchased in powdered form and tablets/capsules, respectively, and they were used directly for extraction. The extraction solvent was selected to have the ability to extract as many bioactive compounds as possible from the investigated samples and the extraction method was the ultrasound-assisted one. Thus, 1 g of the powdered fruit and different quantities of the herbal medicines (the used amount was in accordance with the manufacturer’s specifications for the concentration of fruit that was used to prepare the tablets/capsules) were moistened with 10 mL ethanol for 10 min, then extracted for 30 min using an Elma TI-H-5 ultrasonic bath (Elma- Hans Schmidbauer, Singen). Each extraction was performed in duplicate. Extracts were separated from the vegetable material/excipients by filtration through 0.45 μm micro-filters. The extracts thus obtained were used for the electrophoretic analysis and for the HPLC analysis the extracts needed dilution by 10-fold. All the solutions were stored at 4°C until final analysis. Electrophoretic separation analysis The electrophoretic measurements were carried out using the micellar electrokinetic chromatographic method (MEKC). All the experiments were conducted using an Agilent G1600 CE Instrument fitted with a diode array detector (DAD) for UV–Vis detection and a thermostating compartment for temperature-control of the capillary. Separations were carried out using a polyimide-coated fused-silica capillary of 68.5 cm (effective length was 60 cm) and 50 μm id (Polymicro Technology, Phoenix, USA). The samples solutions were introduced at the anodic end of the capillary by hydrodynamic sample injection (50 mbar, 2 s) and the applied voltage was +25 kV. The temperature of the capillary holder was kept constant at 25°C. The detection was carried out by on column diode array photometric measurement at 200 nm. A new capillary was washed for 30 min with 1 M NaOH and before each injection the capillary was preconditioned with 1 M NaOH for 3 min and with background electrolyte (BGE) for 6 min. The BGE consisted of 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3). After daily use the capillary was flushed with 1 M NaOH (10 min), 100 mM SDS (10 min) and distilled water (5 min) to remove all components which tend to adsorb onto the capillary wall. The electropherograms were recorded and processed by ChemStation 7.01 software version (Agilent). HPLC separation analysis The chromatographic measurements were performed on an Agilent 1100 Series LC system consisting of a vacuum degassing unit, a binary high pressure pump, a standard automatic sample injector, a column thermostat and a DAD. In order to obtain the fingerprints of the investigated samples, reversed phase HPLC analysis was employed for the separation of bioactive compounds and data acquisition and processing was done by Agilent Chemstation software. The experiments were carried out using an HPLC Zorbax, Eclipse XDB-C18 column (4.6 mm × 100 mm, 3.5 μm—particle size) and the elution was made using a mixture of two mobile phases A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: first 13 min 5–45% B, next 2 min of linear rise up to 66% B, followed by 8 min linear rise up to 95% B, next 5 min of isocratic elution with 95% B, followed by 2 min of linear rise to 100% B. An additional 10 min of post-run with 5% B was employed between samples to equilibrate the column. The injection volume was 15 μL of sample, the detection was performed at 280 nm and the flow rate was 1 mL/min. Data analysis After chromatographic and electrophoretic separations, the obtained chromatograms and electropherograms were digitized and saved as CSV files for further chemometric analysis. Thus, two matrices were obtained, each having as dimensions: 19 samples × 900 variables (absorbance units, at 280 nm) in case of the HPLC analysis and 19 samples × 854 variables (absorbance units, at 200 nm) in case of the MEKC analysis, respectively. These matrices were further used for all chemometric analyses (PCA, CA and Discriminant Analysis), which were carried out using the Statistica 8.1 software (StatSoft, Tulsa, USA). The digitized chromatograms/electropherograms were used for the chemometric analysis without any data pre-treatment. The average of two measurements was used for the chemometric analysis; and the problem concerning the difference in migration time for duplicate samples was solved by adjustment/normalization of the chromatogram/electropherogram using the Chemstation software. Also by using the Statistica software, PCA was carried out via the correlation matrix, in which case the data are automatically standardized. Results Considering the investigated samples, herbal medicines based on cranberry, bilberry and sea-buckthorn extract/fruit, their therapeutic use is derived mainly from their high content of polyphenols. Consequently the separation techniques used in this study were selected and optimized accordingly. Optimization of HPLC method To achieve efficient resolution and as many signals as possible, mixtures of solvent A—water or acidified water (0.1% formic acid) and solvent B—methanol or methanol:acetonitrile (1:1) as mobile phase were tested. Finally, solvent A—acidified water (0.1% formic acid) and solvent B—methanol:acetonitrile (1:1) were selected as the best elution system, as the acidified water gave better peak shapes and the mixture of methanol and acetonitrile had better elution power. Also, isocratic and several gradient conditions, selected from literature and adapted, were tested to optimize the HPLC separation. As the sample’s components were barely separated under isocratic conditions, gradient elution was used instead and the best results were obtained using an adapted version of our previous work (10), method which is presented in detail in “HPLC separation” chapter. Optimization of MEKC method On the other hand, the MEKC method is generally applicable for the separation of neutral components and this technique also needed some optimization. The bioactive compounds from the investigated samples have in their structure aromatic rings and differ in their pattern of hydroxylation, methylation and glycosylation. Accordingly they could be ionizable or neutral. The interaction between the polyphenols and the negatively charged micelles of the buffer depends on the charge value and the hydrophobicity of the compounds. The MEKC has a resolving effect on the neutral polyphenols, while the charged ones have small interactions with the micelles. Also, as the polyphenols strongly interact with the micelles due to the hydrophobic properties therefore the resolution may be varied by modifying the micellar phase. The addition of organic solvents to the BGE containing surfactant (SDS) is commonly used modifier in order to improve the selectivity (23). At pH 9.3 in borate buffer polyphenols are negatively charged due to the dissociation of phenolic groups (pK ≈ 9), thus they migrate according to their charge-to-size ratio. The 50 mM concentration of SDS resolves the neutral components. The borate has a complexation effect on the glycosides enhancing selectivity. The post-conditioning procedure was also optimized, based on our previous work (24, 25), thus the capillary was flushed with 100 mM of SDS for 10 min, after daily use in order to remove adsorbed components from the capillary wall. Selection of optimum detection wavelength As for the detection of the bioactive compounds, DAD detection was considered to be a good choice, as the structure of these compounds allows them to have strong UV absorbance at different wavelengths. Therefore, in both MEKC and HPLC analysis, different UV wavelengths were tested: 200, 214, 250, 280, and 365 nm and 360 and 280, respectively. Thus, the best results were obtained using 200 nm for the MEKC analysis and 280 nm for the HPLC analysis, examples of electropherogram and chromatogram are presented in Figure 1a and b. Figure 1. View largeDownload slide (a) MEKC polyphenolic profile of Sample 12 registered at 200 nm, using 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3) as BGE and (b) RP-HPLC polyphenolic profile of Sample 12 registered at 280 nm, using a mixture of A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: 0–13 min, 5–45% B; 13–15 min, 45–66% B; 15–23 min, 66–95% B; 23–28 min, 95% B; 28–30 min, 95–100% B. Figure 1. View largeDownload slide (a) MEKC polyphenolic profile of Sample 12 registered at 200 nm, using 50 mM disodium tetraborate and 50 mM sodium dodecyl sulfate (pH = 9.3) as BGE and (b) RP-HPLC polyphenolic profile of Sample 12 registered at 280 nm, using a mixture of A (H2O with 0.1% formic acid) and B (Metanol:Acetonitrile, 1:1 v/v), with a 30 min gradient profile as follows: 0–13 min, 5–45% B; 13–15 min, 45–66% B; 15–23 min, 66–95% B; 23–28 min, 95% B; 28–30 min, 95–100% B. Chemometric analysis Furthermore, in order to obtain the authentication of herbal medicines several chemometric approaches were tested, considering the sample’s polyphenolic profiles (chromatograms and electropherograms, respectively) as analytical information. By a careful examination of the obtained chromatograms and electropherograms, a visual differentiation among different samples could be done, but the process would be subjective and also small differences between related samples might be missed. Consequently, a chememometric approach for the data evaluation is more suited and in this study CA, PCA and LDA were used. Among the chemometric methods, PCA is the most preferred because it is considered to reduce dimensionality of the original dataset by explaining the correlation amongst a large number of variables in terms of a smaller number of underlying PCs without losing much information. The PCs are a very useful tool for examining the relationships between objects, looking for groups and trends but is highly sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. Another unsupervised clustering procedure is CA with its hierarchical and non-hierarchical approaches generally used to sort samples into groups. The Ward’s method as the amalgamation rule and the squared Euclidean distance as metric are usually used to establish clusters or distributed as dendrograms in case of different classes of compounds. In the multivariate analysis of data, PCA and CA methods are completed by LDA which is a supervised classification technique based on the linear discriminant functions, which maximizes the ratio of between-class variance and minimizes the ratio of within-class variance (26). Usually the Euclidean distance is used in the LDA algorithms to select directions which accomplish maximum separation among the given classes and stepwise algorithm to extract the most important variables (8). Furthermore, the combination of the PCA and LDA may offer some remarkable information for classification and discrimination of the considered samples (8, 10). Discussion The dendrograms (Figure 2a and b) obtained by applying the CA on the digitized electropherograms and chromatograms, offer some information about the similarities/dissimilarities observed between the analyzed samples. The Ward’s method used for cluster building is regarded as being one of the most efficient rules of amalgamation, because it uses an analysis of variance approach to evaluate the distance between clusters. The procedure is more efficient when the distance between clusters is computed by squared Euclidean method, which is not affected by the addition of new objects to the analysis or by outliers. As it can be observed in both cases Sample 9 was the most differentiated sample from the others, forming a group of its own, and this can be attributed to the fact that this sample contains the least amount of extract/tablet (Table I). Also, the layout of the samples in the dendrograms (from right to left) was made in increasing order of the distances between them, suggesting once again that Sample 9 is the most different of all. On the other hand, generally good clustering was obtained for samples containing cranberry and sea-buckthorn, regardless of the analytical technique that was used for the separation. However, better classification of the mixed samples was obtained using the electrophoretic separation. Thus, Sample 19, which contains both cranberry and bilberry extract/fruit, was grouped with Sample 6, which is based on cranberry extract; Sample 18, which contains both bilberry and sea-buckthorn fruits, was closely placed to Sample 8, which is based on bilberry extract. In case of Sample 17 (contains both bilberry and sea-buckthorn fruits) the classification using electrophoretic data was quite different than expected, but on the contrary, when the chromatographic data were used, this sample was classified in a group with Sample 12, which is based on sea-buckthorn fruits. Figure 2. View largeDownload slide Dendograms obtained by applying CA using Ward’s method as the amalgamation rule and the squared Euclidean distance as metric, on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 2. View largeDownload slide Dendograms obtained by applying CA using Ward’s method as the amalgamation rule and the squared Euclidean distance as metric, on data matrices of digitized: (a) electropherograms and (b) chromatograms. Further, PCA was used to reduce the dimensionality of the original dataset by explaining the correlation among a large number of variables on the basis of a smaller number of principal components (PCs) without much loss of information. By applying PCA on the digitized chromatograms and electropherograms, the first 18 PCs explain the total variance (100%) of the data. The projection of PC1 vs. PC2 is presented in Figure 3a, for the electrophoretic data and Figure 3b, for the chromatographic data, respectively. As it is presented, the first two PCs obtained from the chromatographic data account for more than 84% of the variance, while the two PCs corresponding to electrophoretic data account for approximately 56% of the variance, respectively. The PCA results, although more illustrative, are in good agreement with those obtained with CA. Thus it can be observed, that Sample 9 was separated from the larger group, but also Sample 18 had the same tendency and all the other samples were clustered, more or less in the same group. These two samples have clearly different composition compared to the other samples, Sample 9 by having the least ingredients/tablet and Sample 18 by having the most ingredients/tablet. Figure 3. View largeDownload slide 2D projections of PC1 vs. PC2 obtained by applying PCA on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 3. View largeDownload slide 2D projections of PC1 vs. PC2 obtained by applying PCA on data matrices of digitized: (a) electropherograms and (b) chromatograms. The low discrimination obtained in both cases, HPLC and MEKC, can be attributed to the fact that most of the samples are mixtures of different ingredients (extracts/fruits) which contain large amounts of flavonoids and polyphenolic compounds and they cannot be discriminated by any of the multivariate classical methods (CA and PCA). Regarding PCA, it is well documented that in many cases, more than two or three significant PCs are necessary to adequately characterize the data. In these cases there are more possible graphs and, as a direct consequence, the information retained in a larger number of PCs is dissipated. However, this situation can be proficiently resolved by using a combination of PCA with LDA which could lead to a more efficient discrimination of the investigated samples, according to our previous work (8, 10) and other relevant applications (26, 27). In this way, the variance covariance matrix of the new variables becomes a diagonal matrix, because the scores are orthogonal and the number of PCs is less than or equal to the number of samples. LDA is a supervised classification technique based on linear discriminant functions which maximize between-class variance and minimize within-class variance. The Euclidean distance was used in the LDA algorithms to classify unknown samples and the stepwise algorithm was used to extract the most important variables. The results obtained by applying LDA to the scores corresponding to the first 15 PCs indicate a total separation of samples (100%) within four groups, in good agreement with the nature of the raw material used for their preparation and independently of the separation technique. The Root1–Root2 score plots (Figure 4a and b) illustrate well differentiated groups of samples based on cranberries, bilberries, sea-buckthorn and mixtures, without any overlapping. Although the concentration of fruit/extract was very different in each sample, the proposed combination of PCA-LDA was able to successfully classify the samples according to the nature of their raw material. Also, the results indicate that regardless of the separation technique, the classification of samples was made along the Root1 axis for the mixtures (Samples 17, 18 and 19) and mostly along the Root2 axis for the other samples. Figure 4. View largeDownload slide Plot of Root1 vs. Root2 scores obtained by applying PCA-LDA methodology on data matrices of digitized: (a) electropherograms and (b) chromatograms. Figure 4. View largeDownload slide Plot of Root1 vs. Root2 scores obtained by applying PCA-LDA methodology on data matrices of digitized: (a) electropherograms and (b) chromatograms. Conclusions Fingerprinting, discrimination and authentication of dietary supplements were achieved using HPLC and MEKC combined with chemometric evaluation of data. The results suggested that the combination of PCA with LDA leads to more powerful classification and discrimination of samples, according to their raw material composition. Also, because the results did not show significant differences by using the two separation techniques, it is suited to use either of them for similar experiments, with respect to their advantages/disadvantages. However, the results indicated that the reproducibility of HPLC is better. Furthermore, the simple and efficient methodology developed in this paper might also be used for routine screening and authenticity control of different products (herbal medicines, drugs, food, etc.) and could be implemented in any quality control laboratory. Funding M.A. is grateful for funding from National Research, Development and Innovation Office, Hungary (grant number: NKFI K111932). 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Journal of Chromatographic ScienceOxford University Press

Published: Jan 1, 2018

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