Discrimination of meat paté s according to the animal species by means of near infrared spectroscopy and chemometrics

Abstract

Commercial meat pate samples, comprised of 100% pork (n = 7), 100% beef (n = 5) meat, and binary mixtures (beef and pork, w/w) (n = 18) were used. Fresh samples were analysed in a scanning spectrophotometer NIRSystems 6500 in reflectance mode (1100-2500 nm). Principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to classify samples according to the animal species based on their near infrared reflectance (NIR) spectra. Full cross validation was used as validation method when classification models were developed. Both beef and pork pate samples were classified correctly (100%) while binary mixture samples only achieved 72% of correct classification using SLDA technique. The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method to classify pate samples according to meat type. Nevertheless, NIR spectroscopic methods might provide initial screening in the food chain and enable more costly methods to be used more efficiently.