中文摘要：

将二维相关红外谱与平行因子、多元线性回归方法相结合，建立了掺杂牛奶与纯牛奶的判别模型。采集48个合格牛奶样品，配置浓度范围均为0.01-0.30 g/L的掺杂三聚氰胺牛奶、掺杂尿素牛奶和掺杂四环素牛奶各16个，并在900-1700 cm-1采集各样品的常规一维谱。对各样品在900-1200 cm-1与1200-1700 cm-1进行同步二维相关计算，构建了纯牛奶与掺杂牛奶的二维红外相关谱。采用平行因子算法对所有样品二维相关谱构成的三维矩阵进行三线性分解，得到其得分矩阵。在此基础上，将其得分矩阵与多元线性回归方法相结合，分别建立了掺杂三聚氰胺牛奶、掺杂尿素牛奶、掺杂四环素牛奶与纯牛奶的判别模型。利用所建立的模型，对未知样品进行预测，取得了较好的判别结果。

英文摘要：

The discriminant models of adulterated milk and pure milk were constructed using two-dimensional （2D） infrared correlation spectroscopy by PARAFAC and multivariable linear regression （MLR）. First, a total of 96 samples including 48 pure milk samples and three types of adulterated milk （16 melamine-tainted milk, 16 urea-tainted milk, and 16 tetracycline-tainted milk） were prepared. The concentration ranges of all adulterants were 0.01-0.30 g？L-1. The mid-infrared spectra of all samples were measured in the regions of 900-1 700 cm-1. Then, the synchronous 2D correlation spectra of all samples were calculated in the region between 900-1 200 cm-1 and 1 200-1 700 cm-1. The 2D correlation spectra of all samples were analyzed based on trilinear decomposition using PARAFAC. Finally, the discriminant models for melamine-tainted milk, urea-tainted milk and tetracycline-tainted milk were constructed combined score matrix extracted from 2D correlation spectra using PARAFAC with MLR. The unknown samples were predicted using the constructed models in prediction set. The results show that using a combination of 2D IR correlation spectra and PARAFAC-MLR is an effective analytical method for the classification of adulterated milk and pure milk.