A study integrated metabolomics, network pharmacology, and machine learning to investigate differential metabolites of volatile compounds in food evaluation: A case of porcini mushrooms

文献类型： 外文期刊

第一作者： Li, Chaoping

作者： Li, Chaoping;Wang, Yuanzhong;Shen, Tao;Li, Tao

作者机构：

关键词： Porcini mushrooms; Infrared spectroscopy; Metabolomics; Network pharmacology; Machine learning

期刊名称：FOOD BIOSCIENCE （ 影响因子：5.9； 五年影响因子：6.1 ）

ISSN： 2212-4292

年卷期： 2024 年 62 卷

页码：

收录情况： SCI

摘要： Porcini mushrooms have garnered significant attention due to their palatable meaty texture, high nutritional value, and promising commercial potential. However, there is a paucity of information regarding the metabolic profiling of various porcini mushroom species. In the present study, a total of 773 metabolites across 16 distinct classes were analyzed and identified using Fourier transform infrared (FT-IR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) to elucidate the differences in chemical composition among different porcini mushroom species. Notably, volatile compounds (VCs) such as terpenoids, esters, heterocycles, hydrocarbons, and alcohols constituted 72.98% of the total metabolites identified. The comparative analysis identified 20 differential metabolites (DMs) among the species examined. Furthermore, network pharmacology and molecular docking studies indicated that porcini mushrooms were involved in inflammatory mediator regulation of TRP channels and other pathways, thereby exerting anti-inflammatory effects. Utilizing synchronous two-dimensional correlation spectroscopy (2DCOS) images within the 1750-400 cm-1 range, image recognition models were successfully developed to authenticate the species of porcini mushrooms. This study offers a novel approach for the quality assessment of porcini mushrooms and provides theoretical guidance for the comprehensive development and utilization of porcini mushroom resources.

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