The present short communication reports a promising analytical method for authentication of milk based on first-order near-infrared (NIR) spectroscopic data coupled to data driven soft independent modeling of class analogy (DD-SIMCA). This one-class classifier was able to correctly classify all samples of genuine milk powder as members of the target class from samples of milk powder adulterated with melamine and sucrose in a concentration range of 0.8–2% (w/w) and 1–3% (w/w), respectively. Multivariate curve resolution − alternating least-squares (MCR-ALS) was applied as a complementary chemometric model to DD-SIMCA aimed at retrieving pure profiles, allowing to identify the chemical composition of samples properly attributed in the target class or not, providing further investigation from forensic point of view. In order to extend the prime focus of the present report, which was aimed at developing an appropriate chemometric model for authentication purposes, the quantification analysis was also performed. This was done by successful bilinear data decomposition of NIR spectra into pure profiles for the contributing components contained in the system studied (milk and adulterants), allowing to quantify analytes with strong overlapping profiles, even in the presence of an uncalibrated interferent, as demonstrated in this short communication using MCR-ALS under various constraints in order to decrease the rotational ambiguity.

本短文报道了一种基于一阶近红外（NIR）光谱数据与数据驱动的类比法软独立建模（DD-SIMCA）耦合的牛奶认证分析方法。这一一类分类器能够将掺有三聚氰胺和蔗糖的奶粉样品中浓度范围为0.8–2％（w / w）和1-3的纯正奶粉样品正确分类为目标类别的成员。 ％（w / w）分别。多元曲线分辨率-交替最小二乘（MCR-ALS）作为DD-SIMCA的补充化学计量学模型，旨在检索纯分布图，从而可以确定目标类别是否正确归属的样品的化学成分，从而提供进一步的研究从法医的角度来看。为了扩展本报告的主要重点（旨在开发用于认证目的的适当化学计量模型），还进行了定量分析。这是通过将NIR光谱成功地双线性数据分解为所研究系统中所含成分（牛奶和掺假物）的纯谱图来完成的，即使在存在未经校准的干扰物的情况下，也可以定量分析具有强重叠谱图的分析物，在各种约束下使用MCR-ALS进行这种简短通信以减少旋转歧义。