食品科学 ›› 2026, Vol. 47 ›› Issue (16): 364-378.doi: 10.7506/spkx1002-6630-20260126-218

• 专题论述 • 上一篇    下一篇

基于液相色谱-高分辨质谱联用和机器学习的食品组学研究进展

曾凡倜,李春宇,何洪源,景晶,龚晓晓   

  1. (1.中国人民公安大学侦查学院,北京 100038;2.国家体育总局反兴奋剂中心,北京 100029;3.新疆警察学院刑事技术系,新疆?乌鲁木齐 830011)
  • 出版日期:2026-08-25 发布日期:2026-09-03
  • 基金资助:
    国家队常用食材及调味品的兴奋剂风险研究项目(24KJCX048); 上海市现场物证重点实验室开放课题基金项目(2024XCWZK02); 国家重点研发计划科技冬奥重点专项(2019YFF0303405)

Advances in Foodomics Based on Liquid Chromatography High Resolution Mass Spectrometry and Machine Learning

ZENG Fanti, LI Chunyu, HE Hongyuan, JING Jing, GONG Xiaoxiao   

  1. (1. School of Criminal Investigation, People’s Public Security University of China, Beijing 100038, China;2. Anti-doping Center of General Administration of Sport of China, Beijing 100029, China;3. Criminal Technology Department, Xinjiang Police College, ürümqi 830011, China)
  • Online:2026-08-25 Published:2026-09-03

摘要: 随着生活水平的提高,人们对食品的质量和安全提出了更高的要求,亟需将先进的技术和方法应用于食品科学研究领域。基于液相色谱-高分辨质谱(liquid chromatography high resolution mass spectrometry,LC-HRMS)和机器学习(machine learning,ML)进行食品组学研究,是检测食品安全与评价食品质量的一种先进方法,正逐渐成为食品科学研究的新方向。该技术组合正广泛应用于食品真实性鉴定、食品安全评估、食品质量控制等领域。本文综述近年来LC-HRMS和ML在食品代谢组学、蛋白质组学、脂质组学及多组学研究中的应用进展,总结其在食品组学研究中的典型应用,并对未来发展进行展望,旨在为食品快速全面检测的智能化发展提供理论依据和技术指导。

关键词: 食品;液相色谱-高分辨质谱联用;机器学习;食品组学

Abstract: With the improvement of living standards, people’s demands for food quality and safety have increased, driving the urgent need for cutting-edge analytical methodologies in food science research. In this context, the integration of liquid chromatography high resolution mass spectrometry (LC-HRMS) with machine learning (ML) has established a sophisticated framework for foodomics. This powerful technological combination is gradually becoming a new direction for food science research and is widely being applied in food authenticity identification, safety assessment, and quality control. This review provides a critical overview of recent advances in the application of LC-HRMS combined with ML in food metabolomics, proteomics, lipidomics, and multi-omics approaches. Furthermore, we discuss the future development of the combination of LC-HRMS and ML, aiming to provide a theoretical basis and technical guidance for the intelligent development of rapid and comprehensive food testing.

Key words: food; liquid chromatography high resolution mass spectrometry; machine learning; foodomics

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