Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice

文献类型： 外文期刊

第一作者： Zheng, Xing

作者： Zheng, Xing;Pan, Fei;Wu, Liming;Peng, Wenjun;Wang, Kai;Naumovski, Nenad;Wei, Yue

作者机构：

关键词： Honey; Sugar; Data mining; Machine learning; Biomarkers; Metabolomics

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2024 年 430 卷

页码：

收录情况： SCI

摘要： As a natural sweetener produced by honey bees, honey was recognized as being healthier for consumption than table sugar. Our previous study also indicated that metabolite profiles in mice fed honey and mixed sugar diets are different. However, it is still noteworthy about the batch-to-batch consistency of the metabolic differences between two diet types. Here, the machine learning (ML) algorithms were applied to complement and calibrate HPLC-QTOF/MS-based untargeted metabolomics data. Data were generated from three batches of mice that had the same treatment, which can further mine the metabolite biomarkers. Random Forest and Extra-Trees models could better discriminate between honey and mixed sugar dietary patterns under five-fold cross-validation. Finally, SHapley Additive exPlanations tool identified phosphatidylethanolamine and phosphatidylcholine as reliable metabolic biomarkers to discriminate the honey diet from the mixed sugar diet. This study provides us new ideas for metabolomic analysis of larger data sets.

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