Institute of Chemistry, University of Silesia, Katowice, 9 Szkolna Street, 40-006 Katowice, Poland.
Department of Clinical Pharmacology, Faculty of Pharmacy, Wrocław Medical University, 211a Borowska Street, 50-556 Wrocław, Poland.
Meat Sci. 2018 May;139:15-24. doi: 10.1016/j.meatsci.2018.01.009. Epub 2018 Jan 11.
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，这些分类器可以有效地根据近红外（NIR）光谱指纹来监测肉类的安全性、质量和真实性。判别技术通常在多元质量控制中被考虑。然而，当肉类产品的真实性是首要关注的问题时，它们往往会导致对新样本的错误识别。为了根据近红外光谱识别肉类样本的物种，比较了两种分类建模技术（CMT）的性能——偏最小二乘法（OCPLS）的一类分类器变体和类相似性的软独立建模（SIMCA）。基于获得的灵敏度和特异性值，可以认为 OCPLS 和 SIMCA 是一种有效的 CMT，可用于对复杂的自然样本（如研究中的肉类样本）进行分类（具有较大的可变性）。此外，特别关注了一类分类模型的优化和验证。
