Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process

The feasibility of using hyperspectral imaging technique (1000–2500 nm) for predicting moisture content (MC) during the salting process of porcine meat was assessed. Different spectral profiles including reflectance spectra (RS), absorbance spectra (AS) and Kubelka–Munk spectra (KMS) were examined to investigate the influence of spectroscopic transformations on predicting moisture content of salted pork slice. The best full-wavelength partial least squares regression (PLSR) models were acquired based on reflectance spectra ( R c 2 = 0.969, RMSEC = 0.921%; R c 2 = 0.941, RMSEP = 1.23%). On the basis of the optimal wavelengths identified using the regression coefficient, two calibration models of PLSR and multiple linear regression (MLR) were compared. The optimal RS-MLR model was considered to be the best for determining the moisture content of salted pork, with a R c 2 of 0.917 and RMSEP of 1.48%. Visualisation of moisture distribution in each pixel of the hyperspectral image using the prediction model display moisture evolution and migration in pork slices.