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牛奶的快速分类使用经济高效的近红外光谱仪器和可变集群支持矢量机 (VC-SVM) 混合模型

Eleonora Buoio¹, Valentina Colombo², Elena Ighina¹

¹Department of Veterinary Medicine and Animal Science, University of Milan, Via dell'Università 6, 26900 Lodi, Italy.

Foods (Basel, Switzerland)

|October 26, 2024

概括

本研究介绍了一种使用富里埃变换近红外 (FT-NIR) 光谱和可变集群支持矢量机 (VC-SVM) 模型来检测牛奶欺诈的快速方法. 该方法准确地分类牛奶类型,确保乳制品的真实性.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

食品欺诈,包括去除脂肪和添加水,危及牛奶的质量.
脂肪含量的偏差是牛奶造的关键指标.
红外光谱,特别是便携式近红外光谱 (NIRS),对于牛奶分析是有效的.

研究的目的:

开发和实施使用FT-NIR光谱和VC-SVM模型的快速牛奶分类方法.
评估模型在没有预处理的情况下识别牛奶脂肪含量变化的有效性.
为了符合欧盟法规EC No. 对于牛奶的分类和欺诈的检测.

主要方法:

使用一个紧的,低成本的里叶变换近红外 (FT-NIR) 光谱仪.
采用可变集群支持矢量机 (VC-SVM) 混合模型进行数据分析.
进行了外部验证,以评估不同牛奶类型的分类准确性.

主要成果:

实现了完美的分类 (100%的灵敏度,100%的特异性) 整个与非整个和脱脂与非脱脂牛奶使用VC-SVM模型与辐射基函数 (RBF) 内核.
对于半脱脂牛奶与非半脱脂牛奶的强烈分类 (94.4%的灵敏度,100%的特异性).
在没有任何样本预处理的情况下验证了该方法的有效性.

结论:

与VC-SVM模型相结合的FT-NIR光谱为乳制品行业提供了一种实用,简单和高效的解决方案.
这种方法可以快速识别脱脂,半脱脂和全脂牛奶,有助于检测潜在的食品欺诈行为.
该方法提供可靠的分析性能,可与现场分析的基准仪器相提并论.

关键词:

混合型混合型混合型混合型机器学习是机器学习.牛奶分类牛奶分类接近红外光谱学 (NIRS)支持矢量机器的支持矢量机器.变量集群的变量集群是什么

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