Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning.

Muscle foods are an integral part of the human diet and during the last few decades consumption of poultry products in particular has increased significantly. It is important for consumers, retailers and food regulatory bodies that these products are of a consistently high quality, authentic, and have not been subjected to adulteration by any lower-grade material either by accident or for economic gain. A variety of methods have been developed for the identification and authentication of muscle foods. However, none of these are rapid or non-invasive, all are time-consuming and difficulties have been encountered in discriminating between the commercially important avian species. Whilst previous attempts have been made to discriminate between muscle foods using infrared spectroscopy, these have had limited success, in particular regarding the closely related poultry species, chicken and turkey. Moreover, this study includes novel data since no attempts have been made to discriminate between both the species and the distinct muscle groups within these species, and this is the first application of Raman spectroscopy to the study of muscle foods. Samples of pre-packed meat and poultry were acquired and FT-IR and Raman measurements taken directly from the meat surface. Qualitative interpretation of FT-IR and Raman spectra at the species and muscle group levels were possible using discriminant function analysis. Genetic algorithms were used to elucidate meaningful interpretation of FT-IR results in (bio)chemical terms and we show that specific wavenumbers, and therefore chemical species, were discriminatory for each type (species and muscle) of poultry sample. We believe that this approach would aid food regulatory bodies in the rapid identification of meat and poultry products and shows particular potential for rapid assessment of food adulteration.