Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo S/n, 43007, Tarragona, Spain.
Chemometrics, Qualimetric and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo S/n, 43007, Tarragona, Spain.
Anal Chim Acta. 2022 May 8;1206:339785. doi: 10.1016/j.aca.2022.339785. Epub 2022 Mar 30.
This paper proposes a strategy to assess the performance of a multivariate screening method for semi-quantitative purposes. The adulteration of olive oil with sunflower oil was considered as a case study using fluorescence spectroscopy and two-class Partial Least Squares Discriminant Analysis (PLS-DA). Building the proper screening methodology based on two-class multivariate classification model involve setting the cut-off value for the adulterated class (class 2). So, four classification models were established for four levels of adulterant (cut-off). Model validation involved calculating the main quality parameters (sensitivity, specificity and efficiency) and three additional semi-quantitative parameters (limit of detection, detection capability and unreliability region). The probability of successfully recognizing non-adulterated samples as such was set by the main performance parameters of the two-class model. However, the probability of successfully recognizing adulterated samples as such was more accurately extracted from the performance characteristic curves (PCC) curves instead of just from the sensitivity of the adulterated class. The main performance parameters of the PLS-DA models increased as the cut-off level increased although after a particular value the increase was less pronounced. As an example, when the cut-off was changed from 5% to 20%, sensitivity changed from 70 to 93%, specificity changed from 87 to 97%, and efficiency changed from 78 to 95%. The same can be stated for the semi-quantitative parameter's decision limit and detection capability, which moved from 0 to 1.6 and from 17.7 to 21.6 (% of adulterant), respectively.
本研究提出了一种用于半定量目的的多元筛选方法性能评估策略。本研究以橄榄油和葵花油的掺伪为例，使用荧光光谱法和两类偏最小二乘判别分析（PLS-DA）。基于两类多元分类模型建立适当的筛选方法涉及设定掺伪类（类别 2）的截止值。因此，建立了四个包含四个掺伪水平（截止值）的分类模型。模型验证包括计算主要质量参数（灵敏度、特异性和效率）和三个附加半定量参数（检测限、检测能力和不可靠区域）。非掺伪样品被成功识别为非掺伪样品的概率是由两类模型的主要性能参数设定的。然而，掺伪样品被成功识别为掺伪样品的概率更准确地从性能特征曲线（PCC）曲线中提取，而不仅仅是从掺伪类的灵敏度中提取。尽管在特定值之后增加幅度较小，但 PLS-DA 模型的主要性能参数随着截止值的增加而增加。例如，当截止值从 5%变为 20%时，灵敏度从 70%变为 93%，特异性从 87%变为 97%，效率从 78%变为 95%。同样可以说明半定量参数的决策限和检测能力，它们分别从 0 变为 1.6，从 17.7 变为 21.6（掺伪百分比）。
