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Due to its nutritional value, and taste, minced beef meat (MBM) is highly valuable therefore is susceptible to economically motivated adulteration. High-throughput adulteration detection methods are required to guarantee the authenticity and safety of the MBM. Hyperspectral imaging (HSI) in the 400–1000 nm spectral range, in tandem with multivariate analysis, was evaluated to demonstrate the feasibility of developing generic models to detect minced beef meat adulterated with other types of meats. Partial least squares regression (PLSR) method, the ensemble Monte Carlo variable selection method (EMCVS), a range of spectral pre-treatments, and combinations of any two of them were evaluated to predict the amount of MBM in each prepared sample scanned. The beef prediction model was developed using data from MBM adulterated with minced chicken and turkey meats and validated with data from MBM adulterated with pork meat at adulteration levels ranging from 0 to 51% at approximately 3% increments. Good prediction results were obtained using the EMCVS, on the asymmetric least squares (AsLs) + Standard Normal variate (SNV) pre-treated reflectance spectra (Rp2 0.96, RMSEP 2.9% and RPD 5.4) with 23 selected wavelengths. An independent set of MBM samples adulterated with lamb and duck meat were then prepared similarly at concentrations ranging from 3 to 21% to test the developed method. Good results were obtained in the full range (R2p 0.94, RMSEp 3.92%, RPDp 3.83) and excellent prediction results (R2p 0.95, RMSEp 2.78%, RPDp 5.29) were obtained with 9 optimum wavelengths (484, 508, 593, 596, 602, 736, 739, 924 and 960 nm). PLS discriminant analysis (PLS-DA), the EMCVS and spectral pre-treatments were used to discriminate MBM from other adulterants and achieved almost perfect classification for calibration, cross-validation, and prediction using 12 selected spectral bands. This study developed a high-throughput generic minced meat adulteration prediction and discrimination method based on selected spectral bands that can be used to further develop low-cost portable sensors for the digital sorting of adulterated MBM, and demonstrates the feasibility to develop generic models to detect minced beef meat adulterated with other types of meat. • A generic calibration model was developed for adulteration detection in minced beef. • Calibration performance improved after band selection. • Image processing algorithm was developed to show the level of adulteration. [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]