Chemometric Processing of Visible and near Infrared Reflectance Spectra for Species Identification in Selected Raw Homogenised Meats

Abstract

Visible and near infrared reflectance spectra (400–2498 nm) of 230 homogenised meat samples (chicken, turkey, pork, beef and lamb) were collected. Classification of the spectra into individual species was attempted using factorial discriminant analysis (FDA), soft independent modelling of class analogy (SIMCA), K-nearest neighbour analysis and discriminant partial least squares (PLS) regression. A variety of wavelength ranges and data pre-treatments were investigated for optimum accuracy. Particular difficulty was encountered in distinguishing between chicken and turkey; models were, therefore, initially developed using five separate meat classes and again using four groups, with chicken and turkey being amalgamated into a single class. In a four-group classification, the best models produced between 85 and 100% correct identifications. Using five groups, classification rates were generally lower. FDA and PLS discrimination generally produced the best accuracy rates. SIMCA exhibited the poorest classification performance.