ABSTRACT
When infrared spectral data are used in classification and/or multivariate regression methods there can be problems related to both chemical understanding and computation speed due to the large number of wavenumbers in each spectrum. Here, it is shown that the Procrustes rotation technique can be used to select a minimum set of spectral variables (wavenumbers) to perform classification and regression. Procrustes rotation was coupled to several multivariate methods as PLS, SIMCA and potential curves (a maximum likelihood classification method). The practical problem of implementing a screening methodology for classifying apple juice-based beverages according to their contents of "pure" apple juice was addressed using attenuated total reflectance, mid-IR spectroscopy. It is found that two of the original wavenumbers are almost as good predictors as all the 176 initial ones.
