Protein content evaluation of processed pork meats based on a novel single shot (snapshot) hyperspectral imaging sensor

Protein is an important nutrient for people diet, and protein content is one of the most vital properties in quality assessment of pork meat. The potential of line-scanning based hyperspectral imaging (HSI) has been proved to be able to assess the protein content of meat, however quality of images obtained from line-scanning HSI is affected by sample transformation conditions. A novel single shot (snapshot) HSI sensor was employed to evaluate the protein content of numerous processed pork meat by using back propagation - neural network (BP-NN) and partial least squares regression (PLSR) predictive models. The results of BP-NN were better than that of PLSR. The best spectral profile was selected from reflectance, absorbance, and Kubelka-Munk (K-M) spectra by comparing the performances of their BP-NN models. The BP-NN model combined with absorbance spectra showed the best performance for evaluating the protein content of various processed pork meats with determination coefficient of cross-validation set (R2CV) = 0.8318, and root mean square error of cross-validation set (RMSECV) = 8.38 mg/g, respectively. Results indicated the feasibility of determining the protein content of pork meats by means of single shot HSI.