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Abstract Hyperspectral imaging in the visible and near infrared spectral range (450–1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and non-brined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957–1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450–960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSE p 1.9%, R2 p 0.92) and cooked (RMSE p 2.6%, R2 p 0.83) samples used the 957–1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed. Highlights • Vis-NIR hyperspectral imaging is suitable for the assessment of brining of raw and cooked pork loins. • Chemometric models were developed to classify brined and non-brined pork samples and to predict brining salt concentration. • Spectral pre-treatments and variable selection improved performance of models developed. [ABSTRACT FROM AUTHOR]

Both near infrared hyperspectral imaging (NIR-HSI) and Raman hyperspectral imaging (R-HSI) were investigated in combination with multivariate analysis to assess binary mixtures of food powders. NIR-HSI and R-HSI data of corn flour (CF), icing sugar (IS) and binary mixtures of both in concentrations of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% CF ( w /w) were used to develop CF and IS concentration prediction models. The best model for IS concentration prediction in CF using NIR-HSI was developed using PLS-R based on 7 selected bands (RMSE CV 0.58%, RPD CV 55.4, RMSE P 1.00%, RPD P 48.6, R 2 1.000, 2LV), while the best model for IS concentration prediction in CF using R-HSI was developed using PLS-R on 11 selected bands (RMSE CV 1.75%, RPD CV 18.4, RMSE P 1.26%, RPD P 24.9, R 2 0.997, 4LV). Mixing quality of samples was assessed using the standard deviation of NIR-HSI prediction maps of CF and IS mixtures (50:50 ( w /w)) mixed for 0, 1, 3, 7, 10, 15, and 30 s. The standard deviation reduced from 23.4% prior to mixing to 3.2% after 30 s of mixing. The prediction maps developed provide spatial information which can be used to evaluate mixture quality during both the convective and diffusion phases of mixing. Both hyperspectral imaging techniques investigated were demonstrated to have high potential for assessment of binary mixtures of food powders in process analytical technology applications. [ABSTRACT FROM AUTHOR]