Department of Physical and Chemical Sciences, University of L'Aquila, 67100 L'Aquila, Italy.
Molecules. 2022 Oct 31;27(21):7401. doi: 10.3390/molecules27217401.
The fatty acid (FA) profiles of 240 samples of ricotta whey cheese made from sheep, goat, cow, or water buffalo milk were analyzed by gas-chromatography (GC). Then, sequential preprocessing through orthogonalization (SPORT) was used in order to classify samples according to the nature of the milk they were made from. This strategy achieved excellent results, correctly classifying 77 (out of 80) validation samples. Eventually, since 36 (over 114) sheep ricotta whey cheeses were PDO products, a second classification problem, finalizing the discrimination of PDO and Non-PDO dairies, was faced. In this case, two classifiers were used, SPORT and soft independent modelling by class analogy (SIMCA). Both approaches provided more than satisfying results; in fact, SPORT properly assigned 63 (of 65) test samples, whereas the SIMCA model accepted 14 PDO individuals over 15 (93.3% sensitivity) and correctly rejected all the other samples (100.0% specificity). In conclusion, all the tested approaches resulted as suitable for the two fixed purposes. Eventually, variable importance in projection (VIP) analysis was used to understand which FAs characterize the different categories of ricotta. Among the 22 analyzed compounds, about 10 are considered the most relevant for the solution of the investigated problems.
采用气相色谱法（GC）分析了 240 个绵羊、山羊、奶牛或水牛奶乳清干酪的脂肪酸（FA）图谱。然后，通过正交化（SPORT）进行顺序预处理，以便根据乳源对样品进行分类。该策略取得了优异的结果，正确分类了 77 个（80 个中的 77 个）验证样本。最终，由于 36 个（超过 114 个）绵羊乳清干酪是 PDO 产品，因此面临第二个分类问题，即最终区分 PDO 和非 PDO 乳制品厂。在这种情况下，使用了两种分类器，SPORT 和类间独立建模（SIMCA）。这两种方法都提供了非常令人满意的结果；实际上，SPORT 正确分配了 63 个（65 个中的 63 个）测试样本，而 SIMCA 模型接受了 15 个 PDO 个体中的 14 个（93.3%的灵敏度），并正确拒绝了所有其他样本（100.0%的特异性）。总之，所有测试的方法都适用于这两个固定的目的。最终，使用变量重要性投影（VIP）分析来了解哪些 FA 特征不同类别的乳清干酪。在分析的 22 种化合物中，约有 10 种被认为是解决所研究问题的最相关化合物。
