Feasibility of near infrared and Raman hyperspectral imaging combined with multivariate analysis to assess binary mixtures of food powders

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, R² 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, R² 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.