Comparative antioxidant activity and untargeted metabolomic analyses of cherry extracts of two Chinese cherry species based on UPLC-QTOF/MS and machine learning algorithms

代谢组学 抗氧化剂 类黄酮 化学 机器学习 支持向量机 随机森林 肉桂酸 食品科学 人工智能 传统医学 色谱法 生物化学 计算机科学 医学
作者
Ziwei Wang,Lin Zhou,Wenqian Hao,Yu Liu,Xia Xiao,Shan Xiao,Chenning Zhang,Binbin Wei
出处
期刊:Food Research International [Elsevier BV]
卷期号:171: 113059-113059 被引量:19
标识
DOI:10.1016/j.foodres.2023.113059
摘要

P. pseudocerasus and P. tomentosa are the two native Chinese cherry species of high economic and ornamental worths. Little is known about the metabolic information of P. pseudocerasus and P. tomentosa. Effective means are lacking for distinguishing these two similar species. In this study, the differences in total phenolic content (TPC), total flavonoid content (TFC), and in vitro antioxidant activities in 21 batches of two species of cherries were compared. A comparative UPLC-QTOF/MS-based metabolomics coupled with three machine learning algorithms was established for differentiating the cherry species. The results demonstrated that P. tomentosa had higher TPC and TFC with average content differences of 12.07 times and 39.30 times, respectively, and depicted better antioxidant activity. Total of 104 differential compounds were identified by UPLC-QTOF/MS metabolomics. The major differential compounds were flavonoids, organooxygen compounds, and cinnamic acids and derivatives. Correlation analysis revealed differences in flavonoids content such as procyanidin B1 or isomer and (Epi)catechin. They could be responsible for differences in antioxidant activities between the two species. Among three machine learning algorithms, the prediction accuracy of support vector machine (SVM) was 85.7%, and those of random forest (RF) and back propagation neural network (BPNN) were 100%. BPNN exhibited better classification performance and higher prediction rate for all testing set samples than those of RF. The study herein found that P. tomentosa had higher nutritional value and biological functions, and thus considered for usage in health products. Machine models based on untargeted metabolomics can be effective tools for distinguishing these two species.
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