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通过机器学习彻底改变供应链中的新鲜食品质量控制:当前的进展和未来的挑战

Jiayi Xu^1,2, Zisheng Luo^1,2, Xuenan Zhang^1,2

¹College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory of Agri-food Resources and High-value Utilization, Zhejiang University, Hangzhou, China.

Comprehensive reviews in food science and food safety

|December 8, 2025

概括

机器学习 (ML) 提供先进的解决方案,通过改善收获后质量控制来减少全球食品浪费. 这种人工智能方法提高了食品保存和物流,尽管数据和模型通用性的挑战仍然存在.

科学领域:

食品科学食品科学食品科学
人工智能的人工智能
供应链管理供应链管理

背景情况:

全球新鲜食品浪费量很大,影响了供应链.
机器学习 (ML) 提供了改善收获后物流和食品保存的机会.

研究的目的:

为了质量控制,审查食品供应链中先进的ML应用.
探索ML在整合多omics数据的潜力,以了解分子变化.
评估在分级,传感器技术和智能包装中的实际ML应用.

主要方法:

审查目前在收获后食品质量控制中的ML应用.
分析ML模型,如支持向量机 (SVM) 和卷积神经网络 (CNN).
评估ML用于多omics数据集成和传感器开发.

主要成果:

ML通过对属性和成分的分析来增强食品分类,质量预测和传感器设计.
机器学习驱动的传感器和智能包装可以改善实时监控和食品保存.
ML模型显示了减少损坏和改善质量控制的变革潜力.

结论:

在收获后系统中集成ML是有希望的,但在泛化,透明度和数据可用性方面面临挑战.
解决ML采用挑战对于可持续和有效的食品质量管理至关重要.
需要进一步的研究,以确保模型的稳定性和ML在食品供应链中的广泛应用.

关键词:

新鲜食品新鲜食品机器学习是机器学习.质量控制质量控制质量控制供应链中的供应链.

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