Identification of Repeatedly Frozen Meat Based on Near-Infrared Spectroscopy Combined with Self-Organizing Competitive Neural Networks

A rapid, effective method of identifying repeatedly frozen meat by near-infrared spectroscopy (NIRS) combined with a self-organizing competitive neural network (SCNN) model was established. A total of 180 samples were adopted, including hot, cold, frozen, and repeatedly frozen meats. We compared the treatment effects of four pretreatment methods on the spectrogram samples, namely, multiplicative scatter correction (MSC), standard normal variables (SNV), first-order differential and second-order differential. The second differential pretreatment exerted the optimum effect. A total of 120 pork samples were randomly selected and used to establish a calibration model, and the remaining 60 samples were used for prediction. SCNN analysis revealed that classification performance was the highest when the learning number was 250. The recognition ratio of the 60 prediction collection was 93.3%, in which the recognition ratio of the repeatedly frozen meat was 100%. Thus, combined NIRS and SCNN can rapidly and accurately detect repeatedly frozen meat without destruction.