将二维相关近红外谱参数化方法与BP神经网络结合, 建立掺杂牛奶与纯牛奶的判别模型。 分别配制含有尿素牛奶（1～20 g·L-1）和三聚氰胺牛奶（0.01～3 g·L-1）样品各40个。 研究了纯牛奶、 掺杂牛奶的二维相关近红外谱特性, 在此基础上, 分别提取了各样品二维相关同步谱的5个特征参数。 将这5个特征参数作为BP神经网络的输入, 分别建立掺杂尿素、 掺杂三聚氰胺、 两种掺杂牛奶与纯牛奶的判别模型, 采用这些模型对未知样品进行预测, 其预测正确率分别为95%, 100%和96.7%。 研究结果表明: 该方法有效地提取了牛奶中掺杂目标物的特征光谱信息, 同时又减少了BP神经网络输入变量的维数, 实现了掺杂牛奶与纯牛奶的鉴别。

Discriminant models of adulterated milk and pure milk were established using BP neural network combined with two-dimensional (2D) correlation near-infrared spectra parameterization. Forty pure milk samples, 40 adulterated milk samples with urea (1～20 g·L-1) and 40 adulterated milk samples with melamine (0.01～3 g·L-1) were prepared respectively. Based on the characteristics of 2D correlation near-infrared spectra of pure milk and adulterated milk, 5 apparent statistic parameters were calculated based on the parameterization theory. Using 5 characteristic parameters, discriminant models of urea adulterated milk, melamine adulterated milk and two types of adulterated milk were built by BP neural network. The prediction rate of unknown samples were 95%, 100% and 96.7%, respectively. The results show that this method can extract effectively feature information of adulterant, reduce the input dimensions of BP neural network, and better realize qualitative analysis of adulterant in milk.