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使用机器学习和基本测量,预测原牛奶中的亚拉托克素M₁

Haohan Ding^1,2, Long Wang¹, Xiaodong Song³

¹School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.

Current research in food science

|March 2, 2026

概括

这项研究引入了一种机器学习方法,使用常规测试预测原牛奶中的 aflatoxin M1 (AFM1). 这种方法提供了一种具有成本效益的AFM1查方法,确保乳制品安全.

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:

非洲毒素M1 (AFM1) 是生牛奶中发现的一种有害的真菌毒素,需要持续监测乳制品的安全性.
传统的AFM1检测实验室方法是准确的,但成本高且耗时,限制了它们在大量查中的使用.

研究的目的:

开发一种具有成本效益和质量的方法,用于预测生牛奶中AFM1的存在.
通过使用常规物理化学指标,评估用于预先选AFM1水平的机器学习算法.

主要方法:

五个机器学习模型被评估为对法规值对AFM1级别的二元分类.
作为输入特征,使用了常规测量在生牛奶中的物理化学指标.

主要成果:

多层感知子模型表现出超过80%的准确性和负样本回忆.
机器学习显示出作为AFM1预选的有效工具的潜力.

结论:

机器学习为大规模生牛奶安全监测提供了一种可行,快速和经济的方法.
这种方法补充了传统技术,加强了乳制品安全监督.

关键词:

非洲毒素M1 机器学习机器学习预测模型的预测模型.生牛奶生牛奶是什么意思

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