Abstract
Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization–mass spectrometry (MALDI–MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI–MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems.
Highlights
Metabolic profiling (MP) is often used to evaluate the genotype, origin, quality, and nutraceutical value of medicinal herbs and agricultural products by their compositional balance on the basis of the relative abundance of each metabolite to the total abundance of all metabolites[2,3,4]
The score plot of the principal component analysis (PCA), an unsupervised multivariate statistical analysis, showed clear clusters, one consisting of the Sunrouge (SR) cultivar (C. sinensis x C. taliensis), and the other consisting of the remaining cultivars (Camellia sinensis L.) (Fig. 2C)
These results strongly suggest that the compositional differences among the green tea extract (GTE) can account for the different cultivars and picking seasons
Summary
Metabolic profiling (MP) is often used to evaluate the genotype, origin, quality, and nutraceutical value of medicinal herbs and agricultural products by their compositional balance on the basis of the relative abundance of each metabolite to the total abundance of all metabolites[2,3,4]. Such a technique enables us to theoretically calculate the relative contribution of all multicomponent factors detected in crude samples to the total bioactivity. Considering the principle of this methodology, it is expected that MP may become an effective strategy for obtaining a comprehensive understanding of the physiological activity of multicomponent drugs and nutraceuticals. To date, there has been little research on the use of MP to compare or predict their bioactivity
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