Jatropha species is a medicinal plant commonly used as a traditional medicine and raw material for biodiesel. The classification, identification, and discrimination of these closely related Jatropha spp. are crucial to ensure the raw material’s quality. This study developed an integrated method of Fourier transform infrared (FTIR) to explore functional groups’ information between Jatropha species combined with multivariate statistical analysis. Jatropha curcas and Jatropha multifida were collected from different regions. FTIR profiles were used to obtain the holistic fingerprinting pattern combined with principal component analysis (PCA). After that, the orthogonal partial least square-discriminant analysis (OPLS-DA) and variable importance in the projection (VIP) were used to screen potential characteristic functional group (VIP > 1) in Jatropha spp. FTIR data analysis using chemometrics, PCA, and hierarchical cluster analysis (HCA) classified and differentiated the leaves and stem bark of the samples from different origins. The OPLS-DA reveals that functional groups identified to distinguish J. curcas and J. multifida, namely C–H and C–O with VIP value > 1, are the most important functional groups. In summary, this research presented rapid discrimination between J. curcas and J. multifida from different regions and can be used to identify and discriminate closely related plant species.
Key words: chemometrics, Euphorbiaceae, FTIR, medicinal plant, metabolomics fingerprinting
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