基于参量化二维相关红外谱和最小二乘支持向量机判别掺杂牛奶

Discrimination of Adulterated Milk Using Least Square Support Vector Machines Combined with Two-dimensional Correlation Infrared Spectroscopy

将参量化二维相关谱方法与最小二乘向量机结合起来,建立一种新的掺杂牛奶判别方法.采集48个合格纯牛奶样品,并分别配置浓度范围0.01~0.3g/L的掺杂尿素牛奶、掺杂三聚氰胺牛奶和掺杂四环素牛奶各16个.在研究纯牛奶和掺杂牛奶二维相关红外同步谱特性的基础上,提取并计算了各样品相关同步谱的6个统计特征参量.将处理后的6个特征参量输入最小二乘支持向量机,建立掺杂牛奶与纯牛奶的判别模型,该模型对未知样品的判别正确率为90.6%.结果表明:参量化二维相关谱和最小二乘支持向量机为快速判别牛奶是否掺杂提供了一种新的可能的方法.

A new method tor the discrimination ot actulteratect mlll~. Dabeu Un twu-u^l~l^tulmL~,~J correlation infrared spectroscopy and least square support vector machines （LS-SVM） was proposed. 48 pure milk samples were collected and 16 urea-tainted milk （0.01~0.3 g/L）, 16 melamine-tainted milk （0. 01~0. 3 g/L）, 16 tetracycline-tainted milk （0. 01~0. 3 g/L） were prepared. Based on the characteristics of 2D correlation infrared spectra of pure milk and adulterated milk, 6 apparent statistic parameters of all samples were extracted and calculated. These 6 parameters were used as input for LS-SVM to build discriminant model of adulterated milk and pure milk. The recognition rate of unknown samples was 90. 6%. The results reveal that parameterization of 2D correlation spectra in combination with LS-SVM method has a feasible potential to discrimination adulterated milk and pure milk.