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可能的替代方案:使用中红外光谱学与现代统计机器学习方法相结合,识别和量化水牛,山羊和驼牛奶中的伪造

Chu Chu^1,2, Haitong Wang^1,2, Xuelu Luo^1,2

¹Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.

Foods (Basel, Switzerland)

|October 28, 2023

概括

检测牛奶造对消费者健康和市场完整至关重要. 现代机器学习技术与光谱学相结合,在识别牛奶或水在水牛,山羊和驼牛奶中的水等杂物方面提供了卓越的准确性.

科学领域:

食品科学食品科学食品科学
分析化学分析化学
机器学习机器学习

背景情况:

牛奶造对消费者健康和市场公平性构成风险.
经济动机的伪造往往涉及到将更便宜的牛奶或水添加到优质产品中.
准确的检测方法对于质量控制和消费者保护至关重要.

研究的目的:

区分和量化水牛奶 (BM),山羊奶 (GM) 和驼奶 (CM) 与牛奶或水的改.
将现代统计机器学习算法的性能与部分最小平方 (PLS) 等传统方法进行比较.
为各种伪造场景开发高度准确的预测模型.

主要方法:

使用中红外光谱 (MIRS) 作为主要的分析技术.
应用了先进的统计机器学习算法,包括支持向量机 (SVM),投影追踪回归 (PPR) 和贝叶斯规则化神经网络 (BRNN).
将这些先进的方法与部分最小方程 (PLS) 进行分类和回归任务的比较.

主要成果:

机器学习模型,特别是SVM,PPR和BRNN,在检测伪造方面表现优于PLS.
在几种二进制和多类模型中实现了100%的分类准确性,区分了牛奶类型和 adulterant 水平.
开发了具有高精度的回归模型 (例如,R_V²=0.99用于BM中的水改) 用于量化改剂.

结论:

现代的统计机器学习显著提高了MIRS的准确性,用于检测和量化各种牛奶类型中的造.
SVM,PPR和BRNN提供了强大而准确的解决方案,用于识别牛奶和水的改.
这些先进的分析方法为确保牛奶真实性和安全性提供了可靠的工具.

关键词:

机器学习是机器学习.中红外光谱学中红外光谱学.牛奶牛奶牛奶牛奶牛奶的伪造牛奶的伪造

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