Implementation of chemometrics as a Solution to detecting and preventing falsification of herbal medicines in Southeast Asia: A review

INTRODUCTION

Herbal medicine has been known for centuries as one of the sophisticated traditional medicine systems in preventing or treating disease. It is one of the traditional medicines used for many generations and is acknowledged as an alternative medicine (Ahmad, 2002). People in Southeast Asian countries have a long history of using plants and herbs for their healing properties. The rich-in-flora biodiversity has influenced people to use plants as remedies besides food supplies (Granato et al., 2018). Some of them are turmeric (Curcuma longa) and ginger (Zingiber cassumunar) which are widely cultivated in Southeast Asian countries such as Indonesia (Rohaeti et al., 2015).

Since there are various factors that influence the chemical composition of the plants, it is important to make sure that the plant has an active component and it is there in a sufficient amount (Gad et al., 2013). The efficiencies of herbal medicine depend on several factors in order to ensure the reliability and repeatability of the pharmacological effect (Rohman et al., 2014). The problem is that the material used in herbal medicine by the communities and drug industry is sometimes found to be adulterated (Sultana et al., 2011). For this reason, authenticity is an important aspect to be considered as one of the quality controls. The other problems related to herbal medicine’s authenticity are incorrect identification, substitution, adulterations, dilution with lower grade materials, and labeling the products of different origins (Rohman et al., 2014).

There was much research applied and developed regarding the use of analytical methods to authenticate herbal medicine such as high-performance liquid chromatography (HPLC) (Xia et al., 2016), gas chromatography (GC) (Azemin et al., 2018), ultraviolet-visible (UV-Vis) spectroscopy (M. Rafi et al., 2018), and infrared spectroscopy techniques (Nurrulhidayah et al., 2011). The dataset obtained from those methods was usually large and complex in terms of sample numbers, types, and responses. Chemometrics is a multivariate statistical analysis which could help to process and interpret the data. The most common types of multivariate tests used for authentication include principal component analysis (PCA), partial least square (PLS), artificial neural network (ANN), and discriminant analysis (Granato et al., 2018).

The objective of this review was to analyze the application of chemometrics as an analytic tool in solving herbal medicine problems in Southeast Asia. The use of chemometrics has been explored for assisting chemical data treatment from various instrumental techniques compiled into four main categories including authentications, species classification, determination of origin, and quality control.

MATERIALS AND METHODS

This review was conducted as a systematic process using several databases or computer-based electronic search including PubMed, Scopus, and Google Scholar to access all journals related to the application of the chemometrics technique for herbal medicines in Southeast Asia. All primary research papers had to be published between 2000 and 2020 and had to be original articles. The keywords for searching the papers included “Southeast Asia,” “authentication,” “adulteration,” “herbal medicines,” “plants,” and “chemometrics” either separated or combined.

The duplicated articles had to be discarded manually and 84 journal articles were collected. All papers were initially reviewed by title, abstract, and methods to be selected before the final assessment. The inclusion criteria were papers (1) containing a pure or mixture extract of herbal medicines which originate from Southeast Asia, (2) using chemometrics for analysis, (3) published between January 2000 and November 2020, (4) in the English language, and (5) containing any research designs, while the exclusion criteria included those papers where (1) all studies have been cited and (2) the review paper was not included. The assessment was based on inclusion and exclusion criteria. There were eight more qualified journals that were added to the pooled journals to accomplish the review. A total of 34 papers were selected for a more detailed review. The research strategy used in the review is illustrated in Figure 1.

RESULTS AND DISCUSSION

Results

Authentication of Herbal Medicines

Consumption of herbal medicines and products has been increasing nowadays for primary healthcare needs. The uncontrollable quality in herbal productions can have an effect on the efficacy and safety of the starting material, preparations, and finished products. Herbal materials contain complex mixtures of chemical constituents which possess variability due to many factors such as climate, harvest seasons, plant origins, drying, storage, and the extraction procedure. The adulteration might lead to variant efficacy and safety. As for herbal medicines, identification, determination, and authentication techniques are commonly performed to fulfill the requirement of quality control (Azemin et al., 2018). The adulteration practice, either intentionally or unintentionally, usually includes partial or full substitution of original crude drugs with other substances which are cheaper and either free from or inferior in therapeutic properties.

Figure 1. Review strategy scheme. [Click here to view]

The application of chemometrics can significantly improve the quality of the fingerprint obtained from complex chromatographic or spectroscopic profiles. Chemometrics is basically classified into two main categories: pattern recognition methods and multivariate calibration for quantitative purposes. Pattern recognition by means of multivariate statistical analysis can be divided into two categories: unsupervised and supervised. Unsupervised pattern recognition is utilized for data visualization by observing the relationship between samples and variables with no predetermined class. On the other hand, supervised pattern recognition methods have been used intensively for the classification of datasets. The most popular techniques for the classification of herbal products include linear discriminant analysis (LDA), k-nearest neighbor, soft independent modeling of class analogy (SIMCA), ANN, partial least square-discriminant analysis (PLS-DA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) (Gad and Bouzabata, 2017). Table 1 explains several analytical methods that have been reported which applied the fingerprinting analytical approach using chromatography [Thin-layer chromatography (TLC), GC, and HPLC/ultra-performance liquid chromatography] and spectrometry (UV–Vis, Infrared, near magnetic resonance (NMR), and MS) or both in tandem like gas chromatography-mass spectrometer (GC-MS) and liquid chromatography-mass spectrometer that could be used for fingerprint analysis and authentication of some herbal medicines (Rohman et al., 2020b; Septyanti et al., 2016).

The combination of chromatography fingerprint and multivariate analysis [Partial least square regression (PLSR), PCA, and discriminant analysis (DA)] has been extensively used for species identification, discrimination, and authentication of medicinal plant such as authentication of fresh ginger (Zingiber officinale) from another country (Yudthavorasit et al., 2014), authentication of KG (Kaempferia galangal) from related plant species (Septyanti et al., 2016), and discrimination of red and white rice bran (Sabir et al., 2017). Other methods of chemometrics, namely, PLSR, PCR, and PCA, were combined with fourier transform infrared spectroscopy (FTIR) spectroscopy to be successfully applied for the authentication of Eurycoma longifolia from different places in Kalimantan (Triyasmono et al., 2020) and authentication of Nigella sativa oil adulterated with grapeseed oil (Nurrulhidayah et al., 2011). PCA and DA were successfully used for the quantification of rice bran oil with extra virgin olive oil based on peak intensities at FTIR spectra (Rohman and Man, 2012). Rapid authentication using UV-Vis spectra was used in combination with chemometrics for discrimination in four species of Curcuma (Curcuma. longa, Curcuma xanthorrhiza, Curcuma aeruginosa, and Curcuma manga ). PCA and DA were used for the classification of the four species (M. Rafi et al., 2018).

FTIR and HPLC fingerprints of phytochemicals using chromatographic and spectroscopic techniques may provide valuable information about qualitative and quantitative analysis of medicinal herbs in which pattern recognition can be achieved using chemometrics including PCA and hierarchical cluster analysis (HCA). The application of combining both methods was used for the authentication of different extracts of Orthosiphon stamineus leaves combined with chemometrics (PCA and HCA) for quality control (Saidan et al., 2015). The combination of chemometrics (PCA, HCA, and DA) with GC-MS was also found to able to discriminate and classify the untargeted volatile compounds in some varieties of Ficus deltoidea based on untargeted volatile compounds which could be used for their quality control (Azemin et al., 2018).

The authentication methodology was developed using hyperspectral imaging using chemometrics (PCA, PLS-DA, and ANN) applied for exploring the data and constructing and authenticating the retail samples for nutmeg powder authentication from other species (Kiani et al., 2019). Proton nuclear magnetic resonance (¹H-NMR) spectroscopy gained its popularity because of its capability of detection, identification, and authentication between groups of samples with relatively simple sample preparation (Wijayanti et al., 2019). Proton NMR-based metabolite fingerprinting combined with chemometric methods of PCA and PLS-DA has been used for the authentication of C. xanthorrhiza adulterated with Z. cassumunar (Wijayanti et al., 2019). Another analytical method also can be considered such as TLC. TLC has been developed for a long time for the analysis of drugs and medicinal plants. TLC has many advantages for analysis such as simple preparation, use of only a small volume of organic solvent, and the fact that it can be used for qualitative and semiquantitative analysis.

By using the developed methods using ¹H-NMR spectroscopy and TLC methods combined chemometrics of PCA, OPLS-DA was successfully used for classifying between pure and adulterated samples such as Curcuma species, especially C. longa adulterated with C. manga (Windarsih et al., 2018), and java turmeric (Rohman et al., 2020b) which can be observed in Figure 2. Other methods for analytical techniques such as polarography, X-ray fluorescence spectrometry, inductively coupled plasma-optical emission spectroscopy (ICP-OES), and inductively coupled plasma-mass spectrometry (ICP-MS) were combined with multivariate PCA and LDA for the classification and authentication of varieties of Piper betle (Atikul Islam et al., 2020).

Species Classification

The chemometrics analysis, namely, PCA and HCA, combined with HPLC successfully differentiated three Panax species which is important to the locals in Southeast Asia (Table 1) (Xia et al., 2016). Although it is possible to identify and discriminate visually by the chromatograms, minor variations among the chromatograms might not be seen. The help from chemometrics could assist in evaluating these minor differences. The result of PCA and HCA was comparable and the three principal components accounted for >80.5%. The other studies conducted similar analysis and concluded that chemometrics PCA and HCA allowed the discrimination between the varieties based on metabolite fingerprint or volatile compounds (Azemin et al., 2018; Maulidiani et al., 2012; Srisopon et al., 2019; Wirasuta et al., 2017)

Meanwhile, Rafi et al. (2018) identified and discriminated Curcuma species. All of the four species are closely related and used as herbal drinks and food supplements in Indonesia. UV–Vis spectroscopy in combination with PCA and DA was used for the classification of the four species. The results showed that species classification using DA gave better separation with 95.5% of the samples classified correctly into their groups by leave-one-out cross-validation. Another study conducted by Rohman et al. (2020b) ingsuccessfully discriminated pure and adulterated C. xanthorrhiza with C. aeruginosa using TLC and proton ¹H-NMR coupled with PCA and OPLS-DA as multivariate analysis . OPLS-DA is supervised pattern recognition which uses orthogonal X and Y variables for classification. The result shows that both PCA and OPLS-DA successfully differentiated the two Curcuma species. The study’s findings were also similar to other studies (Atikul Islam et al., 2020; Azemin et al., 2018; Sabir et al., 2017; Septyanti et al., 2016).

Table 1. Implementation of chemometrics for herbal medicine authentication. [Click here to view]

Canonical variate analysis (CVA) was also one of the multivariate analyses used for species classifications. Rohaeti et al. (2015) used FTIR spectroscopy coupled with PCA and CVA to classify three Curcuma species (Curcuma. longa, Curcuma. xanthorrhiza, and Z. cassumunar). CVA is one of the supervised pattern recognition techniques with the goal of finding a linear combination of variables that exhibit the maximum among group variations to within-groups variations and work effectively when the samples are more than the variables. PCA and CVA could discriminate the three closely related species more accurately than by just visual analysis of the FTIR spectra, in which CVA gave clearer classification based on species. Dharmaraj et al. (2011) showed the application of PCA-LDA, genetic algorithm-based linear discriminant analysis (GA-LDA), SIMCA, and neural networks together with FTIR spectroscopy to distinguish six different species of Phyllanthus. The best result was obtained with GA-LDA in which the similar FTIR spectra from the six species could easily be distinguished compared to any other method.

Origin Determination

Different chemometric studies have been carried out for classifying samples from their different environments which imply different characteristics (Table 1). Some samples produce some unique metabolites or profiles which can be used as bases for classifying their origin. The environmental differences like light intensity may affect the production of some plant metabolite contents. The difference in light intensity may be observed using research intended by Maulidiani et al. (2012). Using ¹H-NMR PCA and HCA, the difference in metabolite contents from different light intensities was observed. The effects of the growth-lighting condition toon metabolite contents wasere investigated. This approach successfully discriminates samples which grow in high- and low-light-intensity environments based on their metabolites (triterpenoids, flavonoids, and phenolic compounds). Based on the study, light exposure may accumulate triterpenoids, flavonoids, and phenolic compounds which possess antioxidant activity (Maulidiani et al., 2012). However, samples exposed to lower light intensity produced lower triterpenoids, flavonoids, and phenolic compounds. These compounds will help plants to reduce UV radiation damage from light. The extracts grown with full-day light exposure exhibited a stronger antioxidant activity and contained more metabolites as compared to plants grown in 50% shade.

Figure 2. PCA and OPLS-DA C. xanthorrhiza and C. aeruginosa (Rohman et al., 2020). [Click here to view]

TLC combined with PCA was also successful in classifying O. stamineus from different origins. This classification based on the Rf value, height, and area of each peak obtained from a videodensitogram had significant differences. The results of the PCA score plot of PC1 and PC2 clearly distinguished three3 clusters of samples with the Rf values of 0.0-–0.1, ; 0.1-–0.2,; 0.2-–0.3, and 0.9-–1.0 which are the most important compounds for clustering of samples (Kartini et al., 2020). Volatile organic compounds can also be used for classifying the samples’ origins. Using high-resolution-gas chromatography-mass spectrometry, over 200 different compounds have been found. Some signature peaks can be used for the classification of each sample’s origin. Using PCA and fold change analysis, the researcher could differentiate Piper nigrum L. from its origin (Malaysian and Indian black paper) by means of 11 unique non-polar compounds present in the pepper samples among others: 4(10)-thujene; bicyclo(3.1.0) hexan-2-ol; alpha-copaene; artemisinin; (2e,4e,14e)-n-isobutylicosa-2,4,14-trienamide; elemene isomer (e-elemene) andrographolide; (e)-1-(piperidin-1-yl) dodec-2-en-1-one; (2E,4E,6E)-7-(benzo[d](I,3)dioxol- 5-yl)-1-piperidin-1-yl) pent-2-en-1-one; and gibberellic acid.

A research was conducted for classifying three different origins, coffee Arabica Gayo, Arabica Kintamani, and Arabica Wamena, based on their unique taste. The research was carried out using UV-Vis spectroscopy at 190–1,100 nm wavelength combined with PLSR-DA and PCA to classify 296 samples from Gayo, Kintamani, and Wamena. By using this method, all of the samples were 100% correctly classified from their origin (Suhandy and Yulia, 2018). Another similar method conducted by Rohman et al. (2020a) classified mangosteen pericarps from different locations using FTIR spectra. Using PCA at 1,000–800 cm⁻¹, the researcher could classify mangosteen pericarp from different regions. Using ICP-MS combined with chemometrics provides another perspective in plant origin classification. The study was carried out by using the 13 elements found in kratom samples. Discriminant function analysis (DFA) using the leave-one-out model successfully classified kratom from its origins (100%), suborigins (100%), and strain (86%) (Braley and Hondrogiannis, 2020).

Activity and Quality Determination

Flavonoid contents in several herbal medicine extracts were reported to have antioxidant, anti-inflammatory, and anticancer activity (Miller, 1996). The biological activities are closely related to the medicine plant extracts’ quality. Using near-infrared spectroscopy combined with LDA, SIMCA, and support vector machines successfully determined the flavonoid content in each medicinal plant extract. Using this method, the quality of different medicinal plant extracts can be accurately identified with SIMCA as the best chemometric method with 91.2% accuracy (Wulandari et al., 2016).

The study conducted by Tejamukti et al. (2020) reveals that xanthone derivate contents can be determined using FTIR combined multivariate calibration of PLSR and PCR. Like flavonoids, a xanthone derivate also has various biological activities among others (Rivero and Garibay, 2019), such as antioxidant, analgesic, anti-inflammatory, antifungal, and anthelmintic activities (El-Kenawy et al., 2018). Using FTIR combined PLSR, the xanthone derivative contents can be predicted precisely and accurately with the coefficient of determination (R²) 0.9573 followed by an RMSEC value of 0.0487% and RMSEP value of 0.120% (Tejamukti et al., 2020).

Another study was conducted to evaluate the antifungal activity of Piper betle L. This research using numerical chromatographic parameters of the HPLC peak markers combined HCA and PCA. Using PCA, antifungal activity can be determined from the first component’s PCA, which linearly correlated to the biomarker used. This method proved it can be used to predict the antifungal activity from P. betle L. (Wirasuta et al., 2017).

Moreover, determining the high quality and low quality of nutmeg (Myristica fragrans) has been carried out based on volatile and nonvolatile compounds using FI-ESI-MS and PTR-MS combined with PCA (Van Ruth et al., 2019). Intensities of volatiles (trimyristin and essential oil) and non-volatiles are highly correlated but can diminishes gradually and even be reserveds with rising molecular masses of the non-volatiles. By using this approach, nearly 100% correct prediction was obtained and high-quality and low-quality nutmeg were successfully discriminated.

Discriminating quality has been carried out for ground roasted robusta coffee. UV-Vis spectroscopy combined with PCA and SIMCA is used to evaluate fresh and expired samples based on caffeine, trigonelline, and chlorogenic acid contents. The spectral data were pre-treated using standard normal variaaite (SNV) with thea spectral range spectral of 230–-400 nm. The results of this research show that this approach can be used to discriminate each sample successfully with a 100% correct classification rate (Yulia and Suhandy, 2018).

Comprehensive research carried out by Saidan et al. (2015) correlated HPLC and FTIR combined with chemometrics with different biological activities . This approach shows that chemometrics can be used as a tool to classify and discriminate distinct features of extracts that can be correlated with their biological activities (Saidan et al., 2015).

DISCUSSION

CONCLUSION

The application of chemometrics for authentication of herbal medicines in Southeast Asia was advantageous and could be developed as a new approach. However, this review exhibits various researches related to the application of chemometrics which support the development of chemometrics for the analysis of herbal medicines in the future. Collaboration and integration data among countries in Southeast Asia have become an important issue to be discussed to maximize this new approach’s potency.

ABBREVIATIONS

1H-NMR : Proton Nuclear Magnetic Resonant

ANN : Artificial Neural Network

CVA : Canonical Variate Analysis

DA : Discriminant Analysis

DFA : Discriminant Function Analysis

FTIR : Fourier Transform Infrared Spectroscopy

GA-LDA : Genetic Algorithm based Linear Discriminant Analysis

GC : Gas Chromatography

GC-MS : Gas Chromatography-Mass Spectrometer

HCA : Hierarchical Cluster Analysis

HPLC : High Performance Liquid Chromatography

HR-GC-MS : High Resolution Gas Chromatography Mass Spectrometry

ICP-OES : Inductively Coupled Plasma-Optical Emission Spectroscopy

LC-MS : Liquid Chromatography-Mass Spectrometer

LDA : Linear Discriminant Analysis

OPLS-DA : Orthogonal Projections To Latent Structures-Discriminant Analysis

PCA : Principal Component Analysis

PLS-DA : Partial Least Square- Discriminant Analysis

PLSR : Partial Least Square Regression

SIMCA : Soft Independent Modeling Of Class Analogy

TLC : Thin Layer Chromatography

AUTHOR CONTRIBUTIONS

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the international committee of medical journal editors (ICMJE) requirements/guidelines.

FUNDING

There is no funding to report.

CONFLICTS OF INTEREST

The authors report no financial or any other conflicts of interest in this work.

ETHICAL APPROVALS

This study does not involve experiments on animals or human subjects.

PUBLISHER’S NOTE

This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.

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