应用二维相关红外光谱与支持向量机（SVM）和灰度共生矩阵统计方法相结合对分别掺杂有尿素、三聚氰胺和葡萄糖的牛奶进行判别。以质量浓度为外扰，建立掺杂牛奶的二维相关红外光谱图，选择角二阶矩、主对角线惯性矩、相关系数、熵的均值和标准差作为图像纹理特征并分别建立3种掺杂牛奶的SVM判别模型。结果表明：在对同步谱的分析中，上述3种掺杂牛奶样品中，掺杂尿素的牛奶样品训练集分类准确率为91.7%，预测集的准确率为85.0%；掺杂三聚氰胺和葡萄糖的牛奶的训练集分类准确率和预测集分类准确率分别为96.7%，90.0%和91.7%，100%。用同步谱和异步谱相结合的方法对准确率较低的掺杂尿素的牛奶做进一步试验，两种准确率分别提升为98.1%和92.3%；这一数据的提升是由于两者相结合提供了更大的信息量，有利于掺杂牛奶的判别。据此认为，此方法对掺杂牛奶的判别是可行的。

Discrimination of doped milk by two-dimensional correlation infrared spectroscopy in combination with support vector machine (SVM) and gray level co-occurrence matrix was carried out on 3 kinds of doped milk samples, i.e., urea-doped, melamine-doped and glucose-doped milk samples. Two-dimensional correlation infrared spectra of doped milk was established with mass concentration as the external disturbance. Second moment of angle, moment of inertia of main diagonal, correlation coefficient, mean and standard deviation of entropy were selected as the image texture features, and SVM models for discrimination were established for the 3 kinds of doped milk samples. It was shown by the results, that when the synchronous spectra were studied, values of accuracy of discrimination for the training sets of the 3 kinds of doped milk samples were 91.7% (for urea-doped milk), 96.7% (for melamine-doped milk) and 91.7% (for glucose-doped milk) and values for prediction sets were 85.0%, 90.0% and 100% respectively. Further tests for urea-doped milk were done by study with combination of synchronous spectra and asynchronous spectra, giving values of accuracy of discrimination for samples of training set of 98.1% and for samples of prediction set of 92.3%. The elevation of accuracy of discrimination in this case was found due to obtaining more information by using the synchronous and asynchronous spectra in combination which was more effective for the discrimination. It was concluded that the proposed method was feasible for discrimination of doped milk.