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KMID : 1200820200200020169
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Oriental Pharmacy and Experimental Medicine
2020 Volume.20 No. 2 p.169 ~ p.180
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Characterization of α-glucosidase inhibitory activity of Tetracera scandens leaves by Fourier transform infrared spectroscopy-based metabolomics
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Nokhala Ahmed
Ahmed Qamar Uddin
Saleh Mohammed S. M.
Nipun Tanzina Sharmin
Khan Al’aina Yuhainis Firus
Siddiqui Mohammad Jamshed Ahmad
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Abstract
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Tetracera scandens is a medicinal shrub that belongs to Dilleniaceae. The leaves of the plant have been traditionally used in the treatment of diabetes mellitus in Malaysia. The conventional quality control analysis of medicinal plants that relies on the quantification of few major metabolites is considered time-consuming since it requires extensive sample preparation and neglects the possible impacts that the minor metabolites could have on the activity. This study was aimed to investigate the α-glucosidase inhibitory (AGI) potential of different hydromethanolic extracts of T. scandens leaves and to establish a predictive multivariate model that could be used for the quality evaluation of T. scandens leaf based on the Fourier transform infrared (FT-IR) spectra of its extracts. Different solvent ratios (0%, 20%, 40%, 60%, 80% and 100% methanol in water) were used to prepare a total of 36 extracts. The AGI potential and the FT-IR fingerprint spectrum were acquired for each extract. A four components orthogonal partial least squares (OPLS) model (1?+?3?+?0) with R2Y of 0.951 and Q2Y of 0.916 was established to describe the correlation between the fingerprint FT-IR spectra of different T. scandens extracts and their corresponding AGI activities. The carbon-halide, carbon?oxygen single bonds, as well as the hydroxyl and carbonyl groups were identified to be positively correlated with the AGI activity. To sum up, an OPLS model was successfully developed as a rapid quality evaluation method to predict the AGI activity of T. scandens.
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KEYWORD
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Tetracera scandens, α-Glucosidase, Fourier transform infrared fingerprinting, Multivariate analysis, Orthogonal partial least squares, Metabolomics
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