The Authentication of Java Turmeric (Curcuma xanthorrhiza) Using Thin Layer Chromatography and 1H-NMR Based-Metabolite Fingerprinting Coupled with Multivariate Analysis

Abstract

The identification of adulteration practices of medicinal plants used as herbal medicine is very important to ensure the quality, safety, and efficacy. In this study, thin layer chromatography (TLC) and proton nuclear magnetic resonance (1H-NMR)-based metabolite fingerprinting coupled with multivariate analysis were used for authentication of Curcuma xanthorrhiza extract from Curcuma aeruginosa. Curcumin contents obtained from C. xanthorrhiza extract from various regions were in the range of 0.74%–1.23%. Meanwhile, curcumin contents obtained from C. xanthorrhiza extract adulterated with 0%, 10%, 25%, 40%, 50%, and 75% of C. aeruginosa were 1.02%, 0.96%, 0.86%, 0.69%, 0.43%, and 0.27%, respectively. The decreasing of curcumin contents in adulterant concentrations of 40% and more in C. xanthorrhiza rhizome could indicate the adulteration with other rhizomes. Multivariate analysis of PCA (principal component analysis) using data set obtained from 1H-NMR spectra clearly discriminated pure and adulterated C. xanthorrhiza with C. aeruginosa. OPLS-DA (orthogonal projections to latent structures-discriminant analysis) successfully classified pure and adulterated C. xanthorrhiza with higher R2X (0.965), R2Y (0.958), and Q2(cum) (0.93). It can be concluded that 1H-NMR-based metabolite fingerprinting coupled with PCA and OPLS-DA offers an adequate method to assess adulteration practice and to evaluate the authentication of C. xanthorrhiza extracts.

Metabolite Fingerprinting Using 1H-NMR Spectroscopy and Chemometrics for Classification of Three Curcuma Species from Different Origins

“…Therefore, spectroscopic-based methods in combination with multivariate data analysis (MDA) or chemometrics were potential to be employed since this combination method was provided the way to analyze such an environmental big data [15]. Ultraviolet, visible, and vibrational spectroscopy (infrared and Raman) [16,17], and NMR spectroscopy [18] are widely reported for authentication of Curcuma species. NMR spectroscopy offers some advantages for authentication of medicinal plants such as fast time analysis, simple in sample preparation, high reproducibility, and high robust.…”

“…1 H-NMR spectroscopy and chemometrics have also been used for authentication of C. longa adulterated with C. manga and C. heyneana [7,24]. Authentication of C. xanthorrhiza from C. aeruginosa has been successfully investigated using 1 H-NMR and multivariate analysis [18]. However, study on authentication of Curcuma species from different origins using 1 H-NMR spectroscopy is still limited.…”

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

“…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/ultraperformance 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).…”

“…By using the developed methods using 1 H-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 plasmaoptical 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).…”

Determination and Chemometrics-Assisted Comparative Analysis of Active Components in Different Tissue of Rana chensinensis

“…OPLS-DA is a supervised statistical method of discriminant analysis and a regression modeling method of the multidependent variable to the multi-independent variable [42]. Its greatest feature is that the data variation that is irrelevant with the classification variable Y in the independent variable X can be removed [43] so that the classification information is mainly concentrated in a principal component, and thus the model becomes simple and easy to explain [41]. An OPLS-DA model of different parts of Rana chensinensis was established by using PUFAs data after orthogonal signal correction, which can improve the reliability of the model.…”