机器学习用于食品行业的质量控制:一篇评论
Konstantinos G Liakos1, Vassilis Athanasiadis2, Eleni Bozinou2
1Department of Electrical and Computer Engineering, University of Thessaly, Sekeri Street, 38334 Volos, Greece.
Foods (Basel, Switzerland)
|October 16, 2025
概括
机器学习 (ML) 提高了食品质量控制 (QC) 和食品安全. 本综述强调了食品工业六个领域的ML进步,重点关注神经网络和适应性QC系统的新兴趋势.
科学领域:
- 食品科学与技术 食品科学与技术
- 人工智能的人工智能
- 工业工程 工业工程 工业工程
背景情况:
- 现代食品生产需要先进的质量控制 (QC) 和安全监测.
- 机器学习 (ML) 为优化食品生产流程提供了潜在的解决方案.
研究的目的:
- 系统地审查食品工业中QC的ML最近的进展.
- 探索ML在食品生产的六个关键领域的应用.
- 确定在实施食品质量认证的ML方面出现的趋势和挑战.
主要方法:
- 基于PRISMA的系统文献审查进行了.
- 在Scopus数据库中搜索同行评审出版物 (2005-2025年).
- 根据纳入标准,严谨性和创新性,选出了25项研究.
主要成果:
- 神经网络是ML的主要方法,其次是合体学习.
- 监督学习方法在各种质量控制任务中普遍存在.
- 新兴趋势包括高光谱成像,传感器融合,可解释的人工智能和区块链.
结论:
- 机器学习,特别是神经网络,在改善食品质量控制和安全方面显示出重大前景.
- 在数据稀缺,实施和与现有系统的集成方面仍然存在挑战.
- 对于强大的质量控制系统,需要对无监督/混合方法和工业4.0/5.0集成进行进一步的研究.
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