Structural Elucidation of Novel Stable and Reactive Metabolites of Green Tea Catechins and Alkyl Gallates by LC-MS/MS

Synthetic gallic acid derivatives are employed as additives in food, personal care products, and pharmaceutical formulations. Despite their widespread use, little is known about their human exposure, health effects, and metabolism. Green tea catechins are natural antioxidants, known for their health-promoting properties, and are also employed as food additives or in personal care products. The objective of this study was to establish metabolic pathways involved in the biotransformation of green tea catechins and synthetic gallate esters. Liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) was used to elucidate oxidative and methylated metabolites, in addition to glutathione conjugates, formed in vitro using human liver microsomal incubations. The developed method was applied to 14 different parent compounds with a wide range of polarities, for the structural elucidation of many known and novel metabolites. These results serve to inform about the wide variety of possible metabolites formed upon exposure to these compounds.

Supervised Statistical Learning Prediction of Soybean Varieties and Cultivation Sites Using Rapid UPLC-MS Separation, Method Validation, and Targeted Metabolomic Analysis of 31 Phenolic Compounds in the Leaves

Soybean (Glycine max; SB) leaf (SL) is an abundant non-conventional edible resource that possesses value-adding bioactive compounds. We predicted the attributes of SB based on the metabolomes of an SL using targeted metabolomics. The SB was planted in two cities, and SLs were regularly obtained from the SB plant. Nine flavonol glycosides were purified from SLs, and a validated simultaneous quantification method was used to establish rapid separation by ultrahigh-performance liquid chromatography-mass detection. Changes in 31 targeted compounds were monitored, and the compounds were discriminated by various supervised machine learning (ML) models. Isoflavones, quercetin derivatives, and flavonol derivatives were discriminators for cultivation days, varieties, and cultivation sites, respectively, using the combined criteria of supervised ML models. The neural model exhibited higher prediction power of the factors with high fitness and low misclassification rates while other models showed lower. We propose that a set of phytochemicals of SL is a useful predictor for discriminating characteristics of edible plants.