基于机器学习的肉类新鲜度评估:原则,管道和应用
Yahong Han1, Xiaoxue Jia2, Lei Yu3
1Hubei Key Laboratory for Processing and Transformation of Agricultural Products, College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China.
Critical reviews in food science and nutrition
|January 22, 2026
概括
机器学习 (ML) 提供了一种快速,非破坏性的方法来检查肉的新鲜度,提高食品安全. 本综述探讨了ML算法和实时肉质评估的管道,克服了传统的局限性.
科学领域:
- 食品科学 食品科学 食品科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的肉类新鲜度评估方法是缓慢的,昂贵的和破坏性的.
- 机器学习 (ML) 为实时,非破坏性肉质评估提供了一个可行的替代方案.
- 准确的肉类新鲜度监测对于食品安全和消费者信心至关重要.
研究的目的:
- 审查 ML 在肉类新鲜度评估中的原则和应用.
- 详细介绍基于ML的检测管道,从数据采集到模型微调.
- 突出肉类质量评估的ML的进步和挑战.
主要方法:
- 关键的ML算法的审查:主要组件回归 (PCR),部分最小平方 (PLS),支持向量机 (SVM),K-最近邻居 (KNN) 和神经网络.
- 检查ML管道阶段:数据采集,预处理,模型选择和微调.
- 专注于先进的ML技术,如卷积神经网络 (CNN) 和合体学习.
主要成果:
- ML算法在实时,非破坏性肉类新鲜度检测方面表现出有效性.
- 像CNN和合奏方法这样的先进模型在解决腐烂和复杂的肉类组成方面表现有前途.
- 该审查概述了一个基于ML的全面检测框架.
结论:
- ML为监测肉类新鲜度提供了强大,高效的解决方案,提高了食品安全.
- 需要进一步的研究来解决诸如数据集质量和模型可解释性等挑战,以便更广泛地采用.
- 成功实施ML将为消费者带来更安全,更高质量的肉类产品.
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