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

Abstract

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.

Keywords:

• Repeatedly frozen meat
• Near-infrared spectroscopy
• Principal component analysis
• Self-organizing competitive neural network

Additional information

Funding

This work was supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (20124401120005), the Key Technologies R&D Program of Guangdong Province, China (2012A032300016), the Natural Science Foundation of Guangdong Province, China (S2011040001850), the Guangdong College of Outstanding Youth Innovation Talent Training Project in China (LYM11026), and the Fundamental Research Funds for the Central Universities, China (21612436 and 21612353).