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

•First time hyperspectral imaging was used to monitor meat salting process.•Spectral profiles were compared to improve model performance.•Moisture contents of porcine meat was predicted with good accuracy.•Optimised PLSR and MLR models were developed.•Chemical images were developed to display moisture evolution and migration. The feasibility of using hyperspectral imaging technique (1000–2500nm) 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 (Rc2=0.969, RMSEC=0.921%; Rc2=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 Rc2 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.