Study of spectral analytical data using fingerprints and scaled similarity measurements

Abstract

A new chemoinformatic model has been developed for enlarging the differences between spectra and applied to differentiation of wines according to the criteria grape origin and variety and ageing process. The model is based on generation of fingerprints from normalised spectra, using empirical parameters and a set of 120 samples. After generation of the fingerprints, similarity matrixes were built on the basis of the Tanimoto similarity index between the fingerprints of the samples. Calculation of the Tanimoto index was modified to adapt the index to the characteristics of the analytical measurements. Thus, scaling factors taking into account pattern fingerprints generated from a group of samples with common characteristics were used. In addition, a modified expression for calculating the Tanimoto index was employed. Principal-components analysis (PCA) and soft independent modelling of class analogy (SIMCA) were applied to the similarity matrixes. The results obtained are discussed as a function of the normalisation method employed, the empirical factor used in generation of the fingerprints, and selection of samples for building the pattern fingerprint, etc. Finally, results from differentiation of wines are compared with those obtained by applying PCA to the unprocessed spectra as stated by the proposed model.