Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·lí Domingo s/n Campus Sescelades, E-43007 Tarragona, Spain.
Food Chem. 2012 Oct 15;134(4):2326-31. doi: 10.1016/j.foodchem.2012.03.100. Epub 2012 Mar 30.
This study evaluates the performance of multivariate calibration transfer methods in a classification context. The spectral variation caused by some experimental conditions can worsen the performance of the initial multivariate classification model but this situation can be solved by implementing standardization methods such as Piecewise Direct Standardization (PDS). This study looks at the adulteration of culinary spices with banned dyes such as Sudan I, II, III and IV. The samples are characterised by their UV-visible spectra and Partial Least Squares-Discriminant Analysis (PLS-DA) is used to discriminate between unadulterated samples and samples adulterated with any of the four Sudan dyes. Two different datasets that need to be standardised are presented. The standardization process yields positive classification results comparable to those obtained from the initial PLS-DA model, in which high classification performance was achieved.
本研究评估了多元校准转移方法在分类情况下的性能。某些实验条件引起的光谱变化会降低初始多元分类模型的性能，但通过实施标准化方法（如分段直接标准化（PDS））可以解决此问题。本研究着眼于烹饪香料与禁用染料（如苏丹红 I、II、III 和 IV）的掺假情况。样品的特征在于其紫外-可见光谱，并且偏最小二乘判别分析（PLS-DA）用于区分未掺假样品和用四种苏丹染料中任何一种掺假的样品。提出了两个需要标准化的不同数据集。标准化过程产生了与初始 PLS-DA 模型获得的分类结果相当的阳性分类结果，在初始 PLS-DA 模型中实现了较高的分类性能。
