Abstract:It is of great importance for quick and nondestructive detection of meat freshness to ensure the quality of meat products and reduce the risks of food safety accidents. In this manuscript,the hyperspectral imaging technology has been used to study the nondestructive detection of pork freshness. The hyperspectral reflectance images between 400 and 1 000 nm of 180 pork samples were acquired. The mean and entropy feature were calculated from the hyperspectral reflectance image. Successive Projection Algorithm(SPA),Principal Component Analysis(PCA),and SPA combined with PCA were respectively used to extract the important feature variables which reflect well the characteristics of the meat freshness. And the Volatile Base Nitrogen(TVB-N) prediction models using Least Squares Support Vector Machine (LSSVM) were developed. On this basis,a visual detection method for the TVB-N content of pork was raised. The results demonstrated that:it can significantly improve the precision of model using the fusion of the mean and entropy features;and the feature dimension reduction method of SPA combined with PCA can significantly reduce the complexity of the model,while improving the model accuracy. The LSSVM prediction model using the fusion feature of mean and entropy based on dimension reduction method of SPA combined with PCA can obtain the best prediction accuracy. The Root Mean Square Error of Prediction (RMSEP) is 1.96,and the Relevance of Prediction (RP) is 0.948. The Residual Prediction Deviation(RPD) is 3.12,which meet the needs of the actual detection for pork freshness. The visualization detection method for pork freshness can display directly and clearly the area and degree of meat corruption.
