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基于机器学习的食品安全分析:全面审查和未来前景

Jiaxun Li¹, Aihong Wu¹, Liqiang Liu¹

¹International Joint Research Laboratory for Biointerface and Biodetection, and School of Food Science and Technology, Jiangnan University, Wuxi, China.

|June 20, 2025

概括

机器学习 (ML) 通过改善污染物的检测和可追溯性来提高食品安全. 解决数据质量和可解释性方面的挑战是实现其全球粮食安全的全部潜力的关键.

科学领域:

食品科学与技术食品科学与技术
计算科学计算科学
分析化学分析化学

背景情况:

全球人口增加和复杂的供应链加剧了食品安全问题.
传统的食品安全分析方法往往是缓慢的,缺乏适应性.
需要新兴技术来解决当前食品安全协议的局限性.

研究的目的:

探索机器学习 (ML) 在应对食品安全挑战中的应用.
在食品安全分析中确定ML的好处和局限性.
概述ML在确保全球食品安全和可追溯性方面的未来方向.

主要方法:

使用机器学习算法进行数据驱动的食品安全参数分析.
应用ML用于检测污染物,包括农药,重金属和微生物.
通过图像和感官数据分析,使用ML进行食品质量评估.
利用ML提高供应链可追溯性.

主要成果:

机器学习在检测各种食品污染物方面显示出巨大的潜力.
ML有助于改善食品质量评估和供应链可追溯性.
挑战包括数据质量不一致,模型可解释性和缺乏标准化.

结论:

机器学习提供了一种强大,数据驱动的方法来提高食品安全.
未来的进步需要优化算法,可解释的人工智能,物联网集成和监管框架.
这些创新对于确保全球更安全,更可追溯的食品至关重要.

关键词:

分析技术分析技术深度学习是一种深度学习.食品的改食品的改食品分析食品分析食品安全食品安全机器学习机器学习

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