A soft discriminant model based on mid-infrared spectra of bovine meat purges to detect economic motivated adulteration by the addition of non-meat ingredients

Abstract

This paper described the development of a multivariate classification methodology to detect frauds in bovine meat based on mid-infrared spectroscopy and partial least squares discriminant analysis (PLS-DA). These frauds consisted of adding carrageenan, sodium chloride, and tripolyphosphate, ingredients that increase meat water holding capacity aiming to obtain economic gains. Meat pieces (fresh beef muscle) of the same bovine cut, M. semitendinosus, from different origins were injected with single to ternary mixtures of adulterants, and their purges were analyzed totaling 176 spectra. Multiclass PLS-DA models for specifically detecting each adulterant provided good results (correctly classification rates > 90%) only for tripolyphosphate. Nevertheless, a two-class PLS-DA model discriminating adulterated and non-adulterated meat provided high success rates (≥ 95%). Aiming to verify the model’s ability to detect other (non-trained) adulterant, this last model was combined with outlier detection in a soft version of a discriminant model that was able to correctly detect 100% of a new validation set consisting of 20 meat samples containing maltodextrin.