Species identification in selected raw homogenized meats by reflectance spectroscopy in the mid-infrared, near-infrared, and visible ranges

Mid-infrared (2000-800 cm(-1)), near-infrared (750-2498 nm), and visible (4 00-750 nm) reflectance spectra of 230 homogenized meat samples (chicken, tu rkey, pork, beef, and lamb) were collected. Species identification was atte mpted by using factorial discriminant analysis (FDA), soft independent mode ling of class analogy (SIMCA), K-nearest neighbor (KNN), analysis and discr iminant partial least-squares (PLS) regression. A variety of wavelength ran ges was investigated for optimum accuracy. Particular difficulty was encoun tered in distinguishing between chicken and turkey; models were therefore d eveloped with the use of five separate meat classes and again with the use of four, with chicken and turkey samples being treated as one group. Discri minant PLS, FDA, and KNN models provided similar levels of accuracy in this application. Correct classification rates in excess of 90% were achieved i n all cases.