Metabolomics-Driven Prediction of Vegetable Food Metabolite Patterns: Advances in Machine Learning Approaches

Abstract

ABSTRACT Understanding food metabolite patterns is crucial for various aspects of food science, including quality assessment and safety evaluation. This comprehensive review explores the integration of machine learning and metabolomics technologies for predicting vegetable food metabolite patterns. The significance of machine learning in analyzing omics data is highlighted. Various machine learning techniques, including supervised, unsupervised, and deep learning algorithms, are examined for their efficacy in predicting food metabolites. Challenges in integrating omics and machine learning, such as data preprocessing, dimensionality reduction, and model interpretability, are addressed, alongside potential solutions. Applications of machine learning in predicting food metabolite patterns, such as authentication, quality assessment, personalized nutrition, and flavor profiling, are explored through case studies. Future perspectives highlight emerging trends, potential applications in precision agriculture and the food industry, and the need for advancements to facilitate widespread adoption.