Chemometric methods permit the construction of classifiers that effectively assist in monitoring safety, quality and authenticity of meat based on the near-infrared (NIR) spectral fingerprints. Discriminant techniques are often considered in multivariate quality control. However, when the authenticity of meat products is the primary concern, they often lead to an incorrect recognition of new samples. The performances of two class modeling techniques (CMT) in order to recognize meat sample species based on their NIR spectra was compared – a one-class classifier variant of the partial least squares method (OCPLS) and the soft independent modeling of class analogy (SIMCA). Based on obtained sensitivity and specificity values, OCPLS and SIMCA can be considered as an effective CMT for the classification of complex natural samples such as studied meat samples (with a relatively large variability). Moreover, particular attention was paid to the optimization and validation of a one-class classification model.

化学计量学方法允许构建分类器，该分类器可基于近红外（NIR）光谱指纹图有效地协助监视肉的安全性，质量和真实性。在多变量质量控制中经常考虑使用判别技术。但是，当肉类产品的真实性成为首要考虑因素时，它们通常会导致对新样品的错误识别。比较了两种基于肉类样本NIR光谱识别肉类样品的建模技术（CMT）的性能–偏最小二乘方法（OCPLS）的一类分类器变体和类比法的软独立建模（SIMCA） ）。根据获得的敏感性和特异性值，OCPLS和SIMCA被认为是对复杂的自然样本（例如研究的肉类样本（具有相对较大的变异性））进行分类的有效CMT。此外，特别关注一类分类模型的优化和验证。