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Current application of chemometrics in traditional Chinese herbal medicine research Yipeng Huang a , Zhenwei Wu a , Rihui Su a , Guihua Ruan a,∗ , Fuyou Du a , Gongke Li b,∗∗ a Guangxi Key Laboratory of Electrochemical and Magnetochemical Function Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guangxi 541004, China b School of Chemistry and Chemical Engineering, Sun Yet-sen University, Guangzhou 510275, China

a r t i c l e

i n f o

Article history: Received 7 August 2015 Received in revised form 16 December 2015 Accepted 22 December 2015 Available online xxx Keyword: Traditional Chinese herbal medicine Chemometrics Quality control Pharmacological efficacy Bioactive components

a b s t r a c t Traditional Chinese herbal medicines (TCHMs) are promising approach for the treatment of various diseases which have attracted increasing attention all over the world. Chemometrics in quality control of TCHMs are great useful tools that harnessing mathematics, statistics and other methods to acquire information maximally from the data obtained from various analytical approaches. This feature article focuses on the recent studies which evaluating the pharmacological efficacy and quality of TCHMs by determining, identifying and discriminating the bioactive or marker components in different samples with the help of chemometric techniques. In this work, the application of chemometric techniques in the classification of TCHMs based on their efficacy and usage was introduced. The recent advances of chemometrics applied in the chemical analysis of TCHMs were reviewed in detail. © 2015 Elsevier B.V. All rights reserved.

1. Introduction

1.1. TCHMs classification and quality control challenges

Recently, traditional Chinese herbal medicine (TCHM) has increasingly attracted the attention of both patients and mediciners. However, owing to the multi-component systems with massive unknown components and misunderstanding, complication theories of TCHMs, there left a great challenge in their analyses and quality control, which has severely restricted the widespread application of TCHMs. The data reported on TCHMs was far from sufficient to meet the criteria needed to support their use worldwide. According to the theory of TCHMs, their efficacy has a great extent to the comprehensive effect of multi-components rather than a few bioactive compounds. For that reason, small numbers of markers or pharmacologically active components in herbs and/or herbal mixtures can only give a brief look of TCHMs but insufficient for assessment of herbal medicines. To exploit sufficient methods for multi-components analysis simultaneously, and then enhancing TCHMs product quality control, numerous analytical methods and chomometric tools have been harnessed vigorously for the analysis of TCHM samples and raw complex data handling.

TCHM, one of the major systems of contemporary medicine in practice in China, is an integral part of the Chinese civilization [1]. In the written records, the first herbal book, Shen Nong Ben Cao Jing, was discovered in China and dated from 2700 BC [2]. TCHMs can be classified by many ways. In terms of the medicinal nature, flavors, and actions, those herbs with cold nature, sour, bitter and salty flavors, astringent, descending and sinking actions belong to “yin”, while those with warm and hot nature, acrid, sweet and bland flavors, dispersing, ascending and floating actions pertain to “yang”. Whereas, if the classification was based on their efficacy and usage, TCHMs can be grouped into relieving exterior herbal medicines, heat clearing herbal medicines, tonic medicinal herbs, promoting blood circulation and removing blood stasis herbal medicines, etc. Though great attention has been paid and lots of works have been endeavored, there was still very limited knowledge known about the chemical compositions, pharmacokinetics, and metabolomics of TCHMs, which created a challenge in establishing quality control standards for TCHMs [3]. The current identification of herbal extracts is by measuring the concentration of a small part of markers or active components, which is referred to the “marker approach”. However, this cannot give a complete overview of an herbal product since multiple constituents are usually accountable for its therapeutic actions and effectiveness. Moreover,

∗ Corresponding author. Fax: +86 773 5892796. ∗∗ Corresponding author. Fax: +86 20 84115107. E-mail addresses: [email protected] (G. Ruan), [email protected] (G. Li). http://dx.doi.org/10.1016/j.jchromb.2015.12.050 1570-0232/© 2015 Elsevier B.V. All rights reserved.

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these multiple constituents may work synergistically [4]. Fingerprint analysis, which is the most frequently used method for quality control of TCHMs, focuses on product comparability by the similarity of the chromatographic profiles of different batches of the TCHMs. Using this method, a particular herbal preparation with complex components could be identified and distinguished from other closely related species [5]. Fingerprinting data can be obtain by numerous methods such as chromatographic [6–8] or spectroscopic measurements generally near infrared (NIR) [9], ultraviolet–visible (UV–vis) [10], fluorescence spectroscopy [11], nuclear magnetic resonance (NMR) [12], mass spectroscopy (MS), and hyphenated techniques [13]. Among these multifarious fingerprint techniques, only chromatographic fingerprinting has been accepted internationally as the strategy for the assessment of TCHMs in that it has the potentiality to determine authenticity, efficacy and tremendous consistency of TCHMs [14]. Consequently, it is not difficult to find that fingerprint analysis is often used the data acquired from gas chromatography (GC), high performance liquid chromatography (HPLC) or ultra-performance liquid chromatography (UPLC). And it is very useful to apply the combination of the analytical techniques with chemometric methods for fingerprint analysis since this approach can provide complementary information from TCHMs for quality assurance and authentication purposes. 1.2. Chemometric techniques Since complicated data from the results of multi-component analysis is exceedingly difficult to evaluate, it is imminent to exploit data processing techniques for mining more useful chemical information from original information-rich data. To meet this, chemometrics with mathematical and statistical techniques to retrieve more information from the data are widely used. With the advancement of computer technology and the development of analytical approaches, chemometrics has developed vigorously and chemometrics methods have become a leading tool among the scientific communities towards faster analysis results and shorter product development time. In herbal drug standardization, various chemometric techniques were commonly used and provided an alternative way for analyzing complex samples [15]. Here, we only introduce exploratory data analysis, unsupervised pattern recognition, and supervised pattern recognition techniques. Exploratory data analysis and unsupervised pattern recognition are commonly used to simplify and gain better knowledge of data sets. The challenge is to remove the redundancy and noise while retaining the meaningful information [16]. Several tools such as principal component analysis (PCA), factor analysis (FA) and projection pursuit (PP) for exploring the data are available [17]. These tools are variable reduction techniques defining a number of latent variables by making linear combinations of the original variables following a given criterion. But actually, compared with PCA, FA and PP were rarely applied. PCA is a technique that allows their visualization retaining as much as possible the information present in the original data by the reduction of the data dimensionality. Unsupervised pattern recognition differs from exploratory data analysis because the aim of the methods is to detect similarities, whereas exploratory data analysis is no particular prejudice as to whether or how many groups will be found [17]. The main unsupervised pattern recognition techniques include similarity analysis (SA) and clustering analysis (CA). In SA, the correlation coefficient and the congruence coefficient were adopted to assess the consistency. SA is the most commonly used standard for evaluation of similarity of the obtained data [18]. CA, comprising fuzzy clustering (FC) and hierarchical clustering analysis (HCA) [19], can be used for preliminary evaluation of the information contents in the data matrices. In CA, samples are grouped based on similarities

without considering the information about the class membership [20]. Supervised pattern recognition techniques use the information about the class membership of the samples to a certain group in order to classify new unknown samples in one of the known classes on the basis of its pattern of measurements [16]. Supervised pattern recognition techniques can be distinguished into discriminating techniques and class-modeling methods. Discriminating techniques such as partial least squares discriminant analysis (PLSDA), linear discriminant analysis (LDA), k-nearest neighbors (KNN), artificial neural networks (ANN), canonical correlation analysis (CCA), least squares-support vector machine (LS-SVM), etc. are utilized to build models based on all the categories concerned in the discrimination, whereas disjoint class-modeling methods create a separate model for each category. LDA is based on the determination of linear discriminant functions, which maximize the ratio of between-class variance and minimize the ratio of within-class variance. PLS-DA aims to find the variables and directions in the multivariate space which discriminate the established classes in the calibration set. SVM is relatively new pattern recognition with advantages of good predictive capability and it is applicable to cope with both classification and regression problems. One of the drawbacks of discriminating techniques is that samples are always grouped into one of the given categories even though they do not belong to any of them. Class-modeling methods, generally soft independent modeling of class analogy (SIMCA), consider those objects that fit the model for a category as part of the model, and classify as non-members those that do not. Owing to the theories of “yin” and “yang” are hard to understand the relationship between the classification and efficacy of TCHMs, in this review, we classified TCHMs based on their efficacy and usage, and paid attention to the recent studies which evaluating the efficacy, ascertaining and determining bioactive components, or controlling the quality of TCHMs with the help of chemometric techniques. The applications of chemometric techniques in analyses of TCHMs were expatiated by giving examples of each type of TCHMs. To better understand this review, the classification of TCHMs and chemometric techniques, and their connections are illustrated in Fig. 1.

2. Application of chemometric techniques in TCHMs analyses 2.1. In relieving exterior herbal medicines Relieving exterior herbal medicines are used for the treatment of exterior syndrome, which can be classified into relieving exterior syndrome with pungent and warm natured drugs and resolving exterior with pungent and cool natured drugs. Antipyretic action, which is attributed to the affect of central nervous system by volatile oils in these herbs, is one of the most remarkable characteristics of this kind of herbs. The most well known bioactive components in relieving exterior herbs are monoterpenes, sesquiterpenes and phenylpropanoid compounds [21–23]. In Liu et al. [24], chemometrics methods combined with HPLC fingerprint were utilized for the species classification and quality assessment of Radix bupleuri. Saikosaponins (a, c, d) were used as the markers in the HPLC fingerprints. HCA, PCA, and SA were performed on the HPLC fingerprints and they were capable to assort samples objectively and successfully in accordance with their species. PLS-DA loading plots gave the specific peaks which enabled to find the main chemical markers that have the most influence on the separation among different species. This study confirmed the potential of the HPLC fingerprint in combination with chemometric methods for quality assessment of TCHMs.

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Fig. 1. Schematic illustration of the classification of TCHMs and chemometric techniques, and the connection between the most frequently used chemometric techniques and each category of TCHMs.

Fan et al. [25]. developed a rapid and efficient method of rapid resolution liquid chromatography (RRLC) combined with PCA and HCA for discriminating sulphur-fumigated Angelica dahurica and controlling its quality. Using the peak areas of all 23 common peaks as input data, HCA was employed to evaluate the resemblance and difference of the samples and finally the sun-dried and sulphur-fumigated samples were divided into two main clusters. To observe the distribution of the 40 samples and find the differences between sun-dried and sulphur-fumigated A. dahurica, PCA was applied to classify the RRLC chromatographic data. Quantitative analysis results of imperatorin in 40 A. dahurica samples showed that all samples of sun-drying and 13 sulphur-fumigated samples met the Chinese Pharmacopoeia requirements, which illustrated that it was inappropriate to control the quality of A. dahurica by using only imperatorin as a marker. However, loadings plot of PCA indicated that oxypeucedanin, imperatorin and one unknown compound were the chemical markers for discrimination of sun-dried and sulphur-fumigated A. dahurica. PCA result was in accordance with that obtained above in the HCA, which indicated that the HCA and PCA methods could be combined to provide more reliable information for the quality evaluation of TCHMs. Moreover, the sun-dried and sulphur-fumigated A. dahurica could be clearly separated by HCA and PCA, and the essential markers such as oxypeucedanin and imperatorin accountable for such differences were ultimately screened out and quantified. A method was developed for the simultaneous quantitative analysis of six major compounds, including nodakenin, oxypeucedanin, bisabolangelone, notopterol, imperatorin, and isoimperatorin, in Notopterygii Rhizoma et Radix and Osterici Radix (OR) by HPLC and discrimination of their origins from chemical fingerprint analysis by PCA and HCA [26]. From the results of the HCA and PCA analyses, the relationship between OR and Notopterygium incisum Ting et H.T. Chang (NI, a species of N. Rhizoma et Radix) was confirmed, which might be the reason why OR could be used to replace NI. In addition, the obvious differences between NI and Notopterygium forbesii Boiss (another species of N. Rhizoma et Radix) were also determined. The HCA and 3D projection plot of PCA can visually show the relationship among the medicinal materials based on the chemical constituents. One more example is that Yudthavorasit et al. [27]. established a method of characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger according to geographical origin using HPLC-diode array detector (DAD) combined

with chemometrics. By employing HCA and PCA with HPLC, the pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were identified and quantified, and their HPLC profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. As the above review and Table 1 shows, current studies on herbs for relieving exterior syndrome were focus on analysis the bioactive components in their volatile oils. Since volatile oil is the main constituent that deciding to the efficacy of this kind of TCHMs, it is significant to construct various powerful evaluated systems for the quality control of volatile oil. To meet that, PCA coupled with HCA were inclined to perform on the quality control of this kind of herbal medicines. These two methods have play an important role and have exhibited their great potential in combined with multifarious analytical approaches to discriminate herbs from different geographical origins, productive processes, and other factors that may affect the amount or quality of volatile oil. Moreover, HCA and PCA could be used to approve each other in order to guarantee the quality evaluation of the TCHMs. However, sometimes the assumptions made by these two chemometric techniques are still not enough, for this purpose, other techniques, such as SA or supervised pattern recognition methods [24,28], can be further introduced to implement the comprehensive establishment of TCHMs fingerprints for quality evaluation. 2.2. In heat clearing herbal medicines The herbs that the main function is resolving interior heat are call heat clearing herbal medicines. According to the difference of pharmacological effect and the application, heat clearing herbal medicines can be classified into five small groups, i.e. heatclearing and detoxifying drugs, heat-clearing and damp-drying drugs, heat-clearing and blood-cooling drugs, deficient-clearing drugs and heat-clearing and fire-purging drugs. To the best of our knowledge, the majority of researches were aiming at the former three categories, which would be exampled herein. In Cui’s group, PCA, SA and HCA were used to study the factors affecting the quality of Andrographis paniculata Nees [29], a TCHM used for fever, dysentery, inflammation sore throat, snakebite, antimicrobial and antimalarial [30–33]. Microwaveassisted extraction was utilized with HPLC-DAD for fingerprint

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Table 1 Reports based on the most frequently used chemometric techniques in the analyses of various types of TCHMs. Type of TCHMs

Herbs’ name

Chemometric tools

Analysed components

Objective

Analytical approach

Reference

Relieving exterior herbal medicines

Notopterygii Rhizoma et Radix

HCA, PCA

a,b

GC–MS, HPLC

[76]

Asari Radix et Rhizoma Perilla frutescens

HCA, PCA HCA, PCA

b a

GC–MS, E-nose GC–MS

[77] [78]

Schizonepeta tenuifolia Briq Rhizoma Ligustici

HCA, PCA

39 volatile and 4 non-volatile compounds 54 volatile constituents 119 compounds in essential oils Essential oils in the crude extract Ferulic acid

a

GC–MS

[79]

Coptidis rhizoma Radix Isatidis

SA, HCA, PCA SA, HCA, PCA

Pulsatilla chinensis (Bunge) Regel Rhizoma Smilacis Glabrae Artemisia selengensis Turcz

Heat clearing herbal medicines

Benefiting qi herbal drugs

Benefiting yin/yang herbal drugs

Bloodactivating and stasiseliminating herbal medicines Expectorant herbs

Acesodyne herbs

Restringent herbs Qi-activating herbs Aromatic damp resolving drug

HCA, PCA

b,c

HPLC-DAD

[80]

a, c a, c, e

HPLC- ELSD UPLC-PDA

[81] [82]

SA, HCA, PCA

5 alkaloids 8 bioactive constituents 5 marker compounds

a

HPLC- ELSD

[83]

SA, HCA, PCA

6 bioactive flavonoids

a

HPLC-MS

[84]

SA, HCA, PCA

Rutin

b

HPLC-PDA

[85]

Ginseng Codonopsis Radix

PCA, PLS-DA PCA, PLS-DA

b d

1

H NMR UPLC q-TOF MS/MS

[86] [87]

Astragalus membranaceus

HCA, PCA, PLS-DA

a

UPLC-MS

[88]

Dioscorea opposita

PCA, PLS, LS-SVM

The major metabolites 57 changed components Flavonoids, triterpenoid, and saponins Total sugar, polysaccharides, and flavonoids

a

NIR, mid-IR

[89]

Radix Angelica sinensis

HCA, PCA

a

GC-MS

[90]

Polygonum multiflorum

HCA, PCA

a

HPLC

[91]

Ophiopogonis Radix

HCA, PCA

a

HPLC-UV-ELSD

[92]

Fructus Ligustri Lucidi

HCA, PCA

a

HPLC

[93]

Morinda officinalis

HCA, PCA

a

HPLC

[94]

Cistanche deserticola

HCA, PCA

Herba leonuri

PCA, LS-SVM

Peach kernel

PCA, PLSR

Saffron Saffron

PCA, MLRA PCA, OPLS-DA

Radix Aconiti Lateralis Preparata

HCA, PCA, CCA

Semen Armeniacae Amarae Fritillariae thunbergii bulbus

SA, HCA, PCA SA, HCA, PCA

51 volatile organic compounds 8 hydrophilic compounds Homoisoflavonoids and steroidal saponins 3 glycosides and 3 triterpene acids 4 anthraquinone compounds 8 bioactive chemicals

d

HPLC-DAD/MS

[95]

Alkaloids and flavonoids Linoleic and oleinic acids Major apocarotenoids Picrocrocin and glycosyl esters of crocetin

a

HPLC

[96]

a,c

DR-NIR

[97]

e e

HPLC-DAD 1H NMR

[98] [99]

Aconitine, hypaconitine, and 7 unknown compounds Amygdalin and another four components Major alkaloids

a

UPLC

[100]

a

HPLC

[101]

d

UPLC-ELSD

[102]

c, f

UPLC-ESI/MSn

[103]

f

Microcalorimetry

[104]

Radix Aconiti

SA, HCA, PCA

Radix Aconitum Kusnezoffii, Radix Aconitum carmichaeli Alpinia oxyphylla Cortex magnoliae officinalis Atractylis chinensis DC

SA, PCA, MANOVA

Monoester/diester diterpenoid aconitines –

SA, HCA, PCA HCA, PCA, PLS-DA

Volatile oils Honokiol and magnolol

a a

GC-FID, GC-MS 1 H NMR, HPLC

[105] [106]

PCA, KNN, LDA

6 unknown molecular and 7 metal ion

a

HPLC, ICP-AES

[107]

a: distinguishing geographic origins; b: differentiating similar species; c: quantitative analysis; d: evaluating the processing methods; f: assessing toxicity. HCA: hierarchical clustering analysis PCA: principal component analysis; SA: similarity analysis; PLS-DA: partial least squares discriminant analysis; LS-SVM: least-square support vector machine; PLSR: partial least squares regression; MLRA: Multiple Linear Regression Analysis; OPLS-DA: orthogonal projections to latent structures-discriminant analysis; CCA: canonical correlation analysis; MANOVA: multivariate analysis of variance; KNN: k-nearest neighbors; LDA: Linear discriminant analysis. GC: gas chromatography; MS: mass spectroscopy; HPLC: high performance liquid chromatography; E-nose: Electronic nose; UPLC: ultra-performance liquid chromatography; 1 H NMR: Hydrogen nuclear magnetic resonance; UHPLC q-TOF MS/MS: ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry; NIR: near infrared; mid-IR: mid-infrared; UV: ultraviolet; DR-NIR: diffuse reflectance near-infrared spectroscopy; ESI: electrospray ionization; ICP-AES: inductively coupled plasma atomic emission spectroscopy. DAD: diode array detector; ELSD: evaporative light scattering detection; PDA: photo diode array; FID: flame ionization detection.

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establishment of A. paniculata Nees to identify the qualified samples from the unqualified ones. The coalescent of chemometric tools and HPLC-DAD enhanced fingerprint has the advantages of efficiency, accuracy and more informative in comparison with common fingerprint. PCA was used for decomposition of two-dimensional matrices. HCA that based on peak characteristics from the average fingerprint profiles among five batches of samples was applied to cluster samples from different sources, and the Euclidean distance was selected as measurement for HCA. To control the quality of Rhizoma Coptidis, a method based on UPLC-PAD was developed for quantitative analysis of five active alkaloids (berberine, coptisine, palmatine, jateorrhizine and epiberberine) and chemical fingerprint analysis. In order to compare the UPLC fingerprints between Rhizoma Coptidis from different origins, SA, HCA, and PCA were applied to identify biomarkers and classify the Rhizoma Coptidis samples according to their cultivated origins [34]. According to SA, the correlation coefficient between each chromatogram of Rhizoma Coptidis samples and the simulative mean chromatogram was different indicating that the samples shared different correlation coefficients of similarities and had different internal quality. The correlation coefficients of the samples from the same source were similar illustrating that the entire chromatograms of these samples were generally consistent and stable. In order to assess the resemblance and differences of these samples, a hierarchical agglomerative clustering analysis of Rhizoma Coptidis samples was performed based on the relative peak areas of all the common chromatographic peaks. The samples could be classified into three quality clusters if an appropriate distance level was chosen. PCA was employed utilizing the relative peak areas of common peaks as input data instead of the full spectrum of fingerprints without any preprocessing to evaluate the discrimination ability of these common components. The PCA results were correspondence with the SA and same to HCA. In addition, five characteristic components that have the most influence on separation among different samples were found out with the help of PCA loadings plots. On this status, PCA could be used as an effective tool to find main chemical markers in quality control of Rhizoma Coptidis. A novel approach for fingerprint analysis of Chrysanthemum morifolium Ramat was developed by combining chemometrics methods, namely SA, HCA, and PCA, with UPLC [8]. The similarity of 20 batches of C. morifolium samples from various sources was reliably evaluated by calculating the correlative coefficient (r) of original data. All the samples show high similarity in retention time while their peak abundances are different. It is considered that two samples are much different when r < 0.8, while it has high similarity with r > 0.9, and it has less similarity with 0.8 < r < 0.9 [18]. To further assess the quality characteristics including the resemblance and differences of these samples, HCA was performed based on the relative peak areas of all the 25 common chromatographic peaks. After determining an appropriate distance method, the samples were classified into three clusters. The shorter distance between two samples demonstrated their higher similarity in quality. The same values of relative peak area of all common peaks used to HCA were also applied to PCA. On the basis of eigenvalues >1, the first two principal components PC1 and PC2 are often used to provide a convenient visual aid for identifying inhomogeneity in the data set [35]. The result of PCA showed that all the samples could be easily classified into two groups, and the classification by PCA was similar to the results of SA and HCA, they basically consistent with each other. Yang et al. established a method by gathering SA, PCA and HCA with UPLC-PDA (photo diode array) fingerprints for simultaneous quantification of three major classes of constitutions including iridoid glycosides, crocins and organic acids of fructus Gardeniae [36]. In the fingerprint, although similarity indices of all samples were very close, a few differences could be observed when some regions

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were magnified. In this case, PCA and HCA were applied to search the structures in the multivariate data set. Compounds of close similar structures shared a positive correlation means that determination of minority characteristic components, such as geniposide, chlorogenic acid and crocin I, could reflect general profile of fructus Gardeniae. It is apparent to find that PCA, and unsupervised pattern recognition chemometrics of SA and HCA were frequently employed to combine with the analytical approaches such as HPLC/UPLC for the differentiation and quantification of heat clearing herbal medicines from different origins. The combination of these three chemometrics techniques can consistent with each other. This can enhance the certainty of the quality evaluation of this kind of herbal medicines. Generally, SA, HCA, and PCA are used in chronological order and the results of these three techniques are increasing convincing. If SA was failed to recognize the difference or similarity of the samples among various origins, HCA and PCA can be further employed and successful discriminate them out. 2.3. In tonic herbal medicines The tonic medicinal herbs are used to replenish physical function, improve the disease resistant ability and eliminate drug weakness syndrome with taking care of yin or yang or reinforce qi of body. Moreover, this kind of TCHMs have been utilized for the treatment of various diseases including nerve, endocrine, metabolism, immune, cardiovascular, digestive, respiratory, blood, anti-aging. Especially, it has remarkable therapeutical effect on the regulation of immune function and improvement of body organism adaptability. 2.3.1. In benefiting qi herbal drugs Ginseng is a well-known TCHM, and it offers several types of therapeutic benefits including anti-stress, health promotion, maintenance and enhancement of the central and immune systems, prevention of certain chronic diseases and aging deterrence properties [37,38]. Among its’ bioactive elements, ginsenosides are known as the major bioactive ingredients for their therapeutic effects and considered as quality control markers of ginseng [39]. Electronic nose (E-nose) coupled with chemometrics, comprising PCA, discriminant factorial analysis (DFA) and SIMCA, were developed to rapidly and nondestructively discriminate between Chinese red ginseng and Korean ginseng [40]. The results indicated that Chinese red ginseng and Korean ginseng were successfully discriminated using the electronic nose coupled with PCA, DFA and SIMCA. PCA provides qualitative information for E-nose pattern recognition files and it is objective and intuitive. DFA can be used to build a discriminant model, i.e., to determine to which class a new sample belongs. SIMCA is a statistical method for supervised classification of data, which provides good or bad, qualified or unqualified results thus identifying whether each sample belongs to the class or not according to the established model. Another E-nose combined with chemometrics method has been used for the qualitative and quantitative analysis of aroma (comprising terpenes, alcohols, aromatics and ester) and characteristics of ginseng at different ages [41]. PCA and DFA performed well in determining the different ages of ginseng samples. The PLS loading plot of gas sensors and aroma ingredients indicated that particular sensors were closely related to terpenes, and 7 constituent of terpenes could be accurately explained and predicted by using gas sensors in PLS models as regards to quantitative analyze. In predicting ginseng age, E-nose data was found to predict more accurately than GC–MS data, but the combination of GC–MS can help explain the hidden correlation between sensors and fragrance ingredients from two different viewpoints.

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Codonopsis Radix has been used as one of the herbal ingredients in prescriptions for replenishing energy deficiency, strengthening immune system, lowering blood pressure, and improving appetite for thousands of years in China [42], and it is often served as a tonic equivalent to ginseng [43]. In the report of Li et al. [44], NIR diffuse reflection spectroscopy coupled with random forests (RF) and knearest neighbor (KNN) methods were proposed and used for the discrimination of the geographical origin of Codonopsis pilosula. RF is a useful classification algorithm and it is a classifier ensembling classification trees. The forest chooses the classification having the most votes. RF is very resistant to overfitting and usually performs well in problems with a low samples/features ratio, like spectrometric data. RF shows an excellent ability to discriminate C. pilosula with correct classifications of up to 97% for training set and 94% for test set. KNN method possesses the advantage of mathematical simplicity, which does not prevent it from achieving classification results as good as (or even better than) other more complex pattern recognition techniques. Nearest neighbor methods are based on the determination of the distances between an unknown object and each of the objects of the training set. The lowest distance is selected for the assignment of the class membership. In KNN, the knearest objects to the unknown sample are selected and a majority rule is applied: the unknown is classified in the group to which the majority of the k objects belong. The choice of k is optimized by calculating the prediction ability with different k values. The spectral bands selected were demonstrated to be sufficient enough to discriminate samples. And the efficiency and accuracy of KNN could be enhanced by the use of the most relevant spectral bands. The same accuracy to RF may reveal that the feature variables were practical and could be applied to other common algorithms. Another example is from the study of Chen et al., who have strategically established an HPLC coupled with PCA and orthogonal signal correction partial least squares discriminate analysis (OSC-PLS-DA) method for determination of lobetyolin for the assessment of the quality of Codonopsis Radix [45]. To obtain a comprehensive view of the distinction, an unsupervised PCA method was employed to analyze the 52 Codonopsis Radix samples. In order to eliminate the effect of the inter-subject variability among the samples, the technique of OSC-PLS-DA was applied and a better classification result was obtained, the good and bad samples were located in different regions. DA method was aimed at classifying and predicting the group Codonopsis Radix samples based on observed characteristics of each sample. The construction of DA function was carried out considering the common peaks of each sample. This procedure generated the discriminant function based on the linear combinations of the predictor variables that provided the best discrimination between the two groups. 2.3.2. In benefiting yin/yang herbal drugs Angelica sinensis is known as tonic, hemopoetic, spasmolytic, analgesic and anti-inflammatary activities [46]. It is often used for the treatment of menstrual disorders, amenorrhea, dysmenorrheal, anemia, constipation, carbuncles, boils and sores [47]. Lou et al. [48] have successfully applied SA, HCA, and PCA for the discrimination of sun-dried and sulfur-fumigated Angelicae Sinensis Radix and simultaneous quantitative determination of marker compounds (ferulic acid, senkyunolide A, and Z-ligustilide). Clustering analysis, using average linkage (between groups) and square euclidean distance, was performed to differentiate and classify the samples. By HCA, samples could be divided into two main groups, namely, the sundried samples were in cluster one and the sulfur-fumigated samples were in another cluster. This indicated that the two processing methods result in significant differences in quality, and illustrated that the sulfur-fumigation processing of A. Sinensis Radix significantly alters chemical components than the sun-drying processing. To analyze further differences in A. Sinensis Radix before and after

sulfur-fumigation, PCA was used to independently discriminate each chemical component. Similar to HCA, sun-dried samples were categorized differently from sulfur-fumigated samples, which further illustrated that alterations in chemical components occured as a result of different post-harvest processing methods. Dendrobium species (Orchidaceae), locally known as ‘Shihu’ or‘Huangcao’. Research focused on its constituents and pharmacological activity has shown that there are several active constituents, such as alkaloids, stilbenoids, glycosides and polysaccharides [49,50]. PCA and HCA were performed for qualitative control of Dendrobium in both researches of Wang et al. [51] and Wei et al. [52]. In the work of Wang, et al., PCA and HCA were used to the discrimination of the Dendrobium candidum Wall. ex Lindl. samples from different growing places and species on the basis of pyrolysis-GC (Py-GC). The results proved the chemometric combined with PyGC fingerprint approach is a simple, rapid and selective method for construction of Py-GC fingerprint for D. candidum Wall. ex Lindl. Raw material without using any tedious pretreatments, which is suitable for the quality control of the raw materials of herbal medicine. The goal of the present work of Wei et al. was to investigate the use of PCA and HCA coupled to NIR spectroscopy to develop a rapid and non-invasive analytical procedure for the discrimination of Dendrobium officinale from non-Dendrobium officinale species. A separation of two groups in the three dimensional space was represented by PC1, PC2, and PC3. The excellent classification in the three dimensional space may be explained by the chemical background of sample and the PCA, which demonstrated that NIR spectroscopic data can cluster Dendrobium officinale from nonDendrobium officinale species by using PCA. HCA separates samples into clusters that exhibit a high degree of both intracluster similarity and intercluster dissimilarity. According to the different species and geographical origins existing in those samples, the samples can be separated into different clusters, therefore, cluster analysis reflected the chemical relationship among all the samples and it also approximately confirmed the results of the PCA. Eucommia Bark is well known for its low toxicity and multiple pharmacological activities [53,54]. It has wide ranging apparent effectiveness of the herbal medicine which attributed to many compounds present in the Eucommia Bark including lignin glycosides, phenols and flavonoids [55]. Multivariate analysis pattern recognition methods such as PCA and HCA have been applied to extract information from the data of chromatographic fingerprints and LC–MS for the analyses of any similarities or differences of the Eucommia Bark samples derived from the different provinces [55]. PCA indicated that samples from the Sichuan, Hubei, Shanxi and Anhui clustered together. The other objects from the four provinces, Guizhou, Jiangxi, Gansu and Henan were discriminated and widely scattered on the biplot in four province clusters. Thus, such results suggested that the composition of the Eucommia Bark samples was dependent on their geographic location and environment. The seven marker compound loading vectors grouped into three sets: (1) three closely correlating substituted resinol compounds and chlorogenic acid; (2) the fourth resinol compound identified by the OCH3 substituent in the R4 position, and an unknown compound; and (3) the geniposidic acid, which was independent of the set 1 variables, and which negatively correlated with the set 2 ones above. These observations from the PCA biplot were supported by HCA, and indicated that Eucommia Bark preparations might be successfully compared with the use of the HPLC responses from the seven marker compounds and chemometric methods such as PCA and the complementary HCA. Cistanches Herba, a superior tonic with an honor of “Desert Ginseng”, has been utilized as a crude drug with claimed health benefits in China for thousands of years. The pharmaceutical values of C. Herba lie in the treatment of kidney deficiency, impotence, female infertility, morbid leukorrhea, profuse metrorrhagia, and senile

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constipation [56]. To develop and validate an analytical method for quality control of C. Herba using multiple markers, PCA and HCA were used to compare the differences among C. Herba from the data of HPLC-DAD-MS. The PCA loadings plot of the scores indicated that peak 1 (echinacoside), peak 3 (acteoside), and peak 5 (isoacteoside) have stronger influences on the discrimination of the samples from different species than other components. Therefore, the quality of C. Herba samples can be determined quantitatively using echinacoside, acteoside, and isoacteoside as the markers [57]. As we found, tonic medicinal herbs are the most frequent choice among all kind of TCHMs for the researches. For benefiting qi herbal drugs, PCA and supervised pattern recognition techniques, such as ANN, KNN, LDA, SIMCA, and PLS-DA, have been applied to couple with the analytical approaches for the discrimination and analysis of the herbal samples. The combination of PCA and supervised pattern recognition chemometric tools can make use of both the advantages and characteristics of these two tools. For example, PCA is objective and intuitive, while SIMCA can identify whether each sample belongs to the class or not according to the established model. For benefiting yin/yang herbal drugs, interestingly, PCA and HCA are preferred to the researchers. The frequent harness of PCA and HCA has demonstrate the powerful and superiority of these two techniques for the differentiation and quantitative analysis of the bioactive components, mainly saponins to benefiting yin/yang herbal drugs, which are likely affected in their content owing to the geographical factor, cultivation pattern, processing method, etc.

2.4. In blood-activating and stasis-eliminating herbal medicines When blood stasis is failed to dissipate in the body, the stagnation will create new risk factors in pathological symptom because of cold, burning blood, wet phlegmy and traumatic injury. Herbs for blood-activating and stasis-eliminating have been used for the treatment of various symptoms: pain, numbness, internal lesions, traumatic hematoma, ecchymosis of skin or tongue [58,59]. Rhizoma Curcumae is a popular type of traditional Chinese medicine whose essential oils are widely used in the treatment of cancer and tumor in China [60,61]. The main components of essential oils have anti-cancer properties with the bioactive terpenoids in Rhizoma Curcumae [61]. An analytical method was established for complex Rhizoma Curcumae samples and the chemometrics methods such as PCA, LDA, back propagation-artificial neural networks (BP-ANN), and least-square support vector machine (LSSVM) were used to investigate the data obtained from one- and two-dimensional GC–MS and HPLC-DAD fingerprints [6]. Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA, which showed a reasonable separation of the samples for each technique. Then LDA, BP-ANN and LS-SVM chemometrics methods were applied to classify the training and prediction sets. The three supervised algorithms were chosen because LDA is a well-known method for data classification, BP-ANN is a versatile method, which responds well to non-linearity in the data, and LS-SVM is a completely different approach to data analysis for prediction and classification problems. It was found that the LS-SVM method performed better than radial basis function-artificial neural networks (RBF-ANN), KNN and PLS-DA [62]. The classification models were constructed with the use of one- and two-dimensional data matrices. All models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, while the BP-ANN calibration closely behind. This implicated that the one-dimensional data matrices alone produced inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.

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Cnidium officinale, one of the Umbelliferae medicinal herbs, has been used to treat various diseases such as anaemia gynecological disorder and rheumatism [63–65]. To develop a new chemometric classification method via direct analysis in real time–time of flight–mass spectrometry (DART–TOF–MS) fingerprinting for Umbelliferae medicinal herbs, DART–TOF–MS was employed on powdered raw materials to obtain a chemical fingerprint of each sample, after which the OPLS-DA was used for multivariate analysis [66]. Since the unsupervised analysis and discrimination between samples in PCA is based only on the projection of variance, OPLSDA is useful for overcoming some shortcomings of PCA when the identity of a sample is known, making model interpretation easier by removal of non-correlated variation. Multivariate analysis was helpful for discriminating Cnidium officinale species from other species of Umbelliferae by using Senkyunolide A (m/z = 193) as the main marker signal of the Cnidium officinale group. PCA and OPLSDA were also employed for metabolic profiling and identification of the genetic varieties and agricultural origin of Cnidium officinale and Ligusticum chuanxiong [67]. By PCA, the variance of the species differences between Cnidium officinale and L. chuanxiong was successfully captured in PC1. The loading plot of PC1 revealed the differences between Cnidium officinale and L. chuanxiong, in detail, Cnidium officinale contains higher levels of glucose and fructose, while L. chuanxiong contains higher levels of melezitose and citric acid. However, it was difficult to discriminate between the agricultural origin of groups 2 and 3 using PC1 and PC2. The distinct components of the samples of Cnidium officinale and L. chuanxiong were further analyzed based on species variety and agricultural origin by using OPLS-DA. An S-plot of OPLS-DA was applied because it helps identify biomarkers that are statistically significant, on the basis of the covariance and correlation loading profiles of each metabolite and it turned out to that the double compound biomarkers selected by the S-plot were able to significantly discriminate among the samples. Curcumae longae rhizome (Jianghuang in Chinese), belonging to the Zingiberaceae family, is a traditional herb widely used in Southeast Asia for various diseases, such as hyperlipidemia, cancer, stomach ache, diabetesmellitus, wounds, and hepatic disorders [68,69]. Essential oil, mainly constituted by aliphatic terpenes and aromatic compounds, was confirmed as the main constituent of Curcumae longae rhizome [70–72]. A chemometric technique combined with GC–MS method was established for the quality control and original discrimination of Curcumae longae rhizome by analyzing essential oils [73]. By PCA, all Curcumae longae rhizome samples could not be well classified according to their origins and some overlaps between samples from Guangxi and Sichuan province were observed. Consequently, PLS-DA was further performed to provide satisfactory results. The obtained 3D PLS-DA scatter plot based on the contents of identified compounds in essential oil of Curcumae longae rhizome showed that all test samples could be clearly classified into four groups according to their origins. Based on the PLS-DA, three compounds (turmerone, ar-turmerone, zingiberene) which have been reported for their good biological activities [74,75]. were found out to be the most important variables in distinguishing Curcumae longae rhizome, and therefore can be developed as marker compounds for quality control of Curcumae longae rhizome by quantitative analysis. Apparently, PCA and the supervised pattern recognition have played an important role on the analysis of the blood-activating and stasis-eliminating herbal medicines. Usually, PCA was used firstly to identify the main factors, namely the reduction of the data dimensionality by retaining as much as possible the information present in the original data. However, PCA could not always well distinguish the object samples from the other similar species. Accordingly, supervised methods that using the information about the class membership of the samples to a certain group were

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further performed for gaining better classification results in order to classify new unknown samples in one of the known classes on the basis of its pattern of measurements. 2.5. In other herbal medicines To date, researches combined with chemometrics were mainly focus on above 4 kinds of TCHMs, and only a small attention was draw on the other herbs such as expectorant herbs, expectorant herbs, acesodyne herbs, restringent herbs, qi-activating herbs, aromatic dampness drug. Among these kinds of TCHMs, interestingly, we found that PCA, SA, HCA and supervised chemometrics techniques were also widely applied in the analyses of them. Since these chemometrics have all been expatiated by giving plenty of examples above, and to avoid the redundant words, the relative literatures of this part are listed directly into Table 1. 3. Conclusions and future prospects In recent years, TCHMs have attracted the great interest of both patients and scientists globally, yet, still failed to promote their widespread applications in that the immature and insufficient quality controls system of them. In many reports, the identification and quality control of herbal medicines are still performed by qualitative and quantitative analysis of a few marker compounds. Whereas, the markers used may not always be suitable for the quality control of herbal samples as many herbal species lack unique chemical compounds, which can not reflect the complexity of the biological samples and ignores synergic effects among compounds. To overcome this problem, numerous chomemetric tools have been performed in various analytical methods to construct the fingerprints. Such techniques are powerful and informative on complex samples and mixtures. Fingerprint approaches rely on the inherent relationships between multiple compounds to display the chemical pattern of herbal sources. By employing pattern recognition analysis, it enables to compare the complex data by searching similarities, discrimination and classification. For that reason, this becomes the preferred method of sample comparison for quality assurance requirements. The importance of chemometrics method has been highlighted based on their wide uses in the quality control and pharmacodynamic analysis of TCHMs. For efficacious and safe use, a series of chemometrics methods for quality assessment have been developed to ensure the quality of TCHM species according to different parts of herb, geographical origin, growing years and processing conditions. In this review, we found that HCA and PCA were the most frequently used chemometric techniques in analysis of relieving exterior herbal medicines and benefiting yin/yang herbal medicines, while SA, HCA and PCA were the most frequently used in analysis of heat clearing herbal medicines. To benefiting qi herbal medicines and blood-activating and stasis-eliminating herbal medicines, PCA and supervised pattern recognition techniques were preferred. Combining investigation of TCHM species with identification of as many as possible bioactive markers will undoubtedly be the key for advanced research on quality control of TCHMs in the future. In other aspect, to circumvent the problem of marker identification, chromatographic fingerprinting will further be an accepted technique for quality assessment of the complexity of TCHMs. Facing the complex information in TCHMs analyses, the existing chemometric tools have exhibited their power and usefulness, however, some incompetence of these tools showed as the demand of quality control was increasingly high. For example, sometimes classical PCA scattered the data without a clear pattern, which made the analyst uncertain whether the results reveal the main features of the data set or only reflect the presence of outliers. One may

question the reliability of quality criteria based on solely SA is that the use of the mean or median fingerprint when no standardized extract is available as the fingerprint is dependent on the composition and the size of the data set, which might influence the outcome. One problem with the LDA model is that it is not always clear which variables should be included in the analysis. SIMCA has the problem that the latent variables generated by SIMCA may be different from the directions separating the classes since they are based on the directions demonstrating the largest variation. Though the problem of SIMCA can be solved by PLS-DA, interpretation of the PLS-DA model becomes progressively more complicated as the number of classes increases. To meet the analysis demands, fortunately, the later developed robust PCA method can clustered the samples according to differences in content of individual compounds. Besides, weighted-PCA will become a valuable alternative to ordinary PCA as it has many advantages of flexibility, the possibility to differentiate between signal and baseline, and all results are always on the original scale of the data. To conquer the problem of SIMCA and PLS-DA mention above, the latest OPLS-DA methods integrates an Orthogonal Signal Correction filter to PLS-DA, separating the predictive and non-predictive data and hereby facilitating interpretation. In a word, the new pattern recognition techniques for TCHMs analysis in the future should combine the advantages of flexibility, facilitating interpretation and good predictive capability, and exploratory data tools should be increasingly sensitive to differentiate signals from baseline. But before a versatile chemometric tool which is available in any circumstances is born, there will be a long stage to employ several chemometric techniques together in TCHMs quality control. Therefore, more studies are still urgently needed to establish feasible and reliable approaches combined with chemometric resolution methods, which can provide a completely new way for quick and accurate analyses of unknown complex practical systems. Acknowledgement This study was supported by the National Natural Science Foundation of China (Nos. 21127008, 91232703 and 21065003). References [1] Y.P. Zhu, H.J. Woerdenbag, Pharm. World Sci. 17 (1995) 103. [2] Z.G. Wang, P. Chen, P.P. Xie, History and Development of Traditional Chinese Medicine, Printing House of the Chinese Academy of Sciences, Beijing, 1999. [3] D.K. Mok, F.T. Chau, Chemometr. Intell. Lab. Syst. 82 (2006) 210. [4] L. Yi Zeng, X. Pei Shan, C. Kelvin, J. Chromatogr. B 812 (2004) 53. [5] C.M. Fu, G.H. Lu, O.J. Schmitz, Z.W. Li, K.S.Y. Leung, Biomed. Chromatogr. 23 (2009) 280. [6] Y.N. Ni, M.H. Mei, S. Kokot, Anal. Chim. Acta 712 (2012) 37. [7] M. Wang, B. Avula, Y.H. Wang, J.P. Zhao, C. Avonto, J.F. Parcher, V. Raman, J.A. Zweigenbaum, P.L. Wylie, I.A. Khan, Food Chem. 152 (2014) 391. [8] X.R. Liang, H. Wu, W.K. Su, Food Anal. Methods 7 (2014) 197. [9] C. Wang, B. Xiang, W. Zhang, J. Chemom. 23 (2009) 463. [10] H.B. Zhu, Y.Z. Wang, Y.X. Liu, Y.L. Xia, T. Tang, Food Anal. Methods 3 (2010) 90. [11] Q.M. Fan, C.Y. Chen, Z.Q. Huang, C.M. Zhang, P.J. Liang, S.L. Zhao, Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 136 (2015) 1621. [12] H.J. Zhi, X.M. Qin, H.F. Sun, L.Z. Zhang, X.Q. Guo, Z.Y. Li, Phytochem. Anal. 23 (2012) 492. [13] Y.Y. Xie, D. Luo, Y.J. Cheng, J.F. Ma, Y.M. Wang, Q.L. Liang, G.A. Luo, J. Agric. Food Chem. 60 (2012) 8213. [14] L.J. Ni, L.G. Zhang, J. Hou, W.Z. Shi, M.L. Guo, J. Ethnopharmacol. 124 (2009) 79. [15] X. Wu, Y. Wei Wei, L. Xiang Yu, C. Wens Sheng, S. Xue Guang, Chromatographia 76 (2013) 849. [16] L.A. Berrueta, R.M. Alonso-Salces, K. Héberger, J. Chromatogr. A 1158 (2007) 196. [17] H.A. Gad, S.H. El-Ahmady, M.I. Abou-Shoer, M.M. Al-Azizi, Phytochem. Anal. 24 (2013) 1. [18] C.J. Xu, Y.Z. Liang, F.T. Chau, Y. Vander Heyden, J. Chromatogr. A 1134 (2006) 253. [19] C. Tistaert, B. Dejaegher, Y. Vander Heyden, Anal. Chim. Acta 690 (2011) 148.

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Please cite this article in press as: Y. Huang, et al., Current application of chemometrics in traditional Chinese herbal medicine research, J. Chromatogr. B (2015), http://dx.doi.org/10.1016/j.jchromb.2015.12.050