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Updated: May 25, 2025

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中国和阿根廷之间的牛肉可追溯性基于各种机器学习模型.

Xiaomeng Xiang^1,2, Chaomin Zhao^2,3, Runhe Zhang^2,3

¹Research Institute for Doping Control, Shanghai University of Sport, Shanghai 200438, China.

Molecules (Basel, Switzerland)

|February 26, 2025

概括

准确的牛肉起源预测现在可以使用元素分析和稳定的同位素. 机器学习模型,特别是PLS-DA,在追踪牛肉来源方面取得了很高的准确性,确保了食品安全和质量.

关键词:

牛肉的可追溯性牛肉的可追溯性分类模型的分类模型.基本面分析是指元素分析.机器学习是机器学习.稳定的同位素比率是稳定的.

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科学领域:

食品科学食品科学食品科学
分析化学分析化学
计算生物学计算生物学

背景情况:

消费者对高品质牛肉的需求需要可靠的原产地追踪.
目前的牛肉来源识别方法需要提高准确性和效率.
生产地区对牛肉的营养价值和质量产生重大影响.

研究的目的:

开发一个可靠的分类模型来预测牛肉的来源.
分析元素含量和稳定同位素以确定原产地.
为了比较不同机器学习算法的牛肉可追溯性性能.

主要方法:

使用ICP-MS和ICP-OES对52个元素进行元素分析.
通过EA-IRMS确定稳定的碳同位素比率.
机器学习模型的构建和评估,包括PLS-DA,CNN和随机森林.

主要成果:

PLS-DA模型实现了98.8%的分类准确率和94.12%的预测准确率.
确定了关键元素 (Fe, Cs, As, Co, V, Sc, Rb, Ru) 和 δ13C 对于起源预测至关重要.
PLS-DA模型表现出优异的性能,R2为0.924和Q2为0.787.

结论:

将元素和稳定同位素分析与机器学习相结合,可以有效地追踪牛肉的来源.
开发的模型提高了食品安全,并满足消费者对经过验证的牛肉来源的需求.
这种方法提供了一种可靠的方法来区分不同生产地区的牛肉.