ArticleScientific reports2017
A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Integrating Phytochemical Bioactivity and Glycemic Risk to Evaluate Fruits for Type 2 Diabetes Management: A Korean Market Perspective.Foods (Basel, Switzerland) · 2026Article
- Identification of α-Glucosidase Inhibitors from Leaf Extract of Pepper (Metabolites · 2021Article
- Immunomodulatory Activities of Selected Essential Oils.Biomolecules · 2020Review
- Optimisation and feature selection of poly-beta-amino-ester as a drug delivery system for cartilage.Journal of materials chemistry. B · 2020Article
- Postprandial glycaemia-lowering effect of a green tea cultivar Sunrouge and cultivar-specific metabolic profiling for determining bioactivity-related ingredients.Scientific reports · 2018Article
- A Phytochemical-Sensing Strategy Based on Mass Spectrometry Imaging and Metabolic Profiling for Understanding the Functionality of the Medicinal Herb Green Tea.Molecules (Basel, Switzerland) · 2017Review
Corrections and comments
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Authors and funding
11 authors.
Funding
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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.
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Registered trials
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