Standardization of UV-visible data in a food adulteration classification problem.

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.