Intramuscular fat (IMF) content is an important quality trait of pork. It influences taste, juiciness and tenderness of the meat. The aim of this study was to develop an objective, rapid, and non-destructive method for predicting the IMF content of pork using hyperspectral imaging technology. Critical wavelengths were selected using correlation analysis based on the spectral profiles of pork samples. The visual IMF flecks on both sides of pork chops were extracted using the wide line detector at the selected critical wavelengths.
The proportion of IMF fleck areas (PFA) at critical wavelengths was used for modeling to predict the IMF content of pork. Both stepwise procedures and partial least squares (PLS) analysis were employed to establish the prediction models. Three different multilinear models were obtained using the stepwise procedure with different first entry variable of the initial model. A 3-component PLS model was developed for prediction of the IMF content. The PLS model outperformed the three multilinear models. The coefficients of determination (R2) of the PLS model on the calibration set and validation set were 0.94 and 0.97, respectively, and the adjusted R2 were 0.92 and 0.93, respectively.
The prediction results of mutilinear models and PLS models indicated the potentials of using hyperspectral imaging to predict the IMF content of pork.