Machine learning: An effective tool for monitoring and ensuring food safety, quality, and nutrition.

Journal: Food chemistry
PMID: 40010186

Abstract

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.

Authors

  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Chi-Tang Ho
    Department of Food Science, Rutgers University, 65 Dudley Rd., New Brunswick, NJ 08901, USA.
  • Xiaoyu Gao
    Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
  • Nuo Chen
    Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China.
  • Fang Chen
  • Yuchen Zhu
    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. Electronic address: zhuyuchen@cau.edu.cn.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.

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