Yang Xin, Ho Chi-Tang, Gao Xiaoyu, Chen Nuo, Chen Fang, Zhu Yuchen, Zhang Xin
College of Food Science and Nutritional Engineering, National Engineering Research Centre for Fruits and Vegetables Processing, Key Laboratory of Storage and Processing of Fruits and Vegetables, Ministry of Agriculture, Engineering Research Centre for Fruits and Vegetables Processing, Ministry of Education, China Agricultural University, Beijing 100083, PR China.
Department of Food Science, Rutgers University, New Brunswick, NJ 08901, United States.
Food Chem. 2025 Jun 15;477:143391. doi: 10.1016/j.foodchem.2025.143391. Epub 2025 Feb 12.
The domains of food safety, quality, and nutrition are inundated with complex datasets. Machine learning (ML) has emerged as a powerful tool in food science, offering fast, accessible, and effective solutions compared with conventional methods. This review outlines the applications of ML in safeguarding food safety, enhancing quality, and unraveling nutrition intricacies. The review encompasses the prediction of food contaminants, classification of food grades, detection of adulterants, and analysis of food nutrients and their correlations with nutritional diseases. Additionally, ML methods are highlighted to elucidate the relationships between gut microbiota, dietary patterns, and disease pathology, thereby positioning gut microbiota as potential biomarkers for disease intervention through dietary regulation. This study provides a valuable reference for future research on applications of ML to the field of food science.
食品安全、质量和营养领域充斥着复杂的数据集。机器学习（ML）已成为食品科学中的一种强大工具，与传统方法相比，它提供了快速、便捷且有效的解决方案。本综述概述了机器学习在保障食品安全、提高质量以及揭示营养复杂性方面的应用。该综述涵盖了食品污染物的预测、食品等级的分类、掺假物的检测以及食品营养成分分析及其与营养疾病的相关性。此外，还强调了机器学习方法以阐明肠道微生物群、饮食模式和疾病病理学之间的关系，从而将肠道微生物群定位为通过饮食调节进行疾病干预的潜在生物标志物。本研究为未来机器学习在食品科学领域应用的研究提供了有价值的参考。
