CHEMOMETRIC TOOLS AND FTIR-ATR SPECTROSCOPY APPLIED IN MILK ADULTERATED WITH CHEESE WHEY

Brazilian law forbids the addition of cheese whey in milk. However, adulteration with cheese whey is one of the most applied fraud due to its low cost. The detection of this fraud is the quantification of Caseinomacropeptide (CMP). The CMP is a constituent of the whey that can be used as adulteration marker. Thus, an analytical method capable of identifying CMP by Fourier Transform Infrared Spectra (FTIR) was developed using chemometrics methods. Firstly, we attempted to develop an exploratory analysis model by Hierarchical Grouping Analysis (HCA) and Principal Component Analysis (PCA) that indicated similarity between samples of raw milk and semi-skimmed milk. Moreover, in the PCA scores, it was possible to observe a tendency of separation between samples with different concentrations of CMP. Afterwards, multivariate regression models were used for Partial Least Squares (PLS), Partial Least Square with Interval Synergism (siPLS) and Supporting Machines with Least Squares (LS-SVM) to quantify the adulteration in different types of milk by Cheese serum through the CMP. All the models were then compared to each other and the results of the official method with Liquid Chromatography Tandem mass spectrometry (LCMS/MS) analysis used by the Ministry of Livestock and Supply (MAPA). The model LS-SVM, employing the full spectrum, obtained the best result compared to the other models (PLS and siPLS) to quantify the CMP in the milk samples.

巴西法律禁止在牛乳中添加干酪乳清。然而，由于成本低廉，以干酪乳清进行掺假是当前最为普遍的食品欺诈手段之一。该类掺假欺诈的检测核心为对酪蛋白巨肽（Caseinomacropeptide，CMP）的定量分析——CMP是乳清的组成成分之一，可作为该掺假行为的特异性标志物。据此，本研究借助化学计量学方法，开发了一种基于傅里叶变换红外光谱（Fourier Transform Infrared Spectra，FTIR）的CMP定量检测分析方法。
首先，本研究尝试通过系统聚类分析（Hierarchical Grouping Analysis，HCA）与主成分分析（Principal Component Analysis，PCA）构建探索性分析模型，结果显示生牛乳与半脱脂牛乳样本间具有较高相似性；此外，在PCA得分图中，可观察到不同CMP浓度的样本呈现出明显的分离趋势。
随后，本研究采用偏最小二乘（Partial Least Squares，PLS）、区间协同偏最小二乘（Partial Least Square with Interval Synergism，siPLS）以及最小二乘支持向量机（Supporting Machines with Least Squares，LS-SVM）三类多元回归模型，以CMP为标志物实现不同品类牛乳中干酪乳清掺假行为的定量检测。
随后，将所有上述模型的预测结果与巴西畜牧和供应部（Ministry of Livestock and Supply，MAPA）所采用的液相色谱-串联质谱（Liquid Chromatography Tandem mass spectrometry，LC-MS/MS）标准分析方法的检测结果进行对比。实验结果表明，采用全光谱建模的最小二乘支持向量机（LS-SVM）模型，在牛乳样本CMP含量定量检测任务中，相较于PLS与siPLS模型取得了最优性能。