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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]

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]

Beef jerky samples immersed in brine or water, both with and without ultrasound treatment were dried at 60 °C for 30, 60, 90, 120, 150, 180, 210 and 240 min. Drying behaviour was evaluated using ten drying kinetic models and hyperspectral imaging. All samples reached water activity values (Aw) < 0.85 during the first 90 min of drying. Moisture content (MC) of beef jerky was predicted using near infrared hyperspectral imaging and chemometrics. Partial least squares regression, band selection and spectral pre-treatments were applied to develop MC prediction models using beef jerky spectra. Most of the MC prediction models developed in this work had RPD values >4, indicating their suitability for process control applications. The best performing MC prediction models for the non-ultrasound and ultrasound treated samples were developed using the ensemble Monte Carlo variable selection (EMCVS) on second derivative of log(1/R) spectra and EMCVS-selectivity ratio on linear detrended log (1/R) spectra, respectively. This study demonstrated the potential of NIR-HSI and chemometrics as a PAT tool for drying of beef jerky. • Application of NIR hyperspectral imaging to predict beef jerky moisture content. • Band selection and spectral pre-treatments improved moisture prediction models. • Effect of ultrasound on drying kinetics of beef jerky demonstrated. • Hyperspectral imaging is suitable for process control of beef jerky processing. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the potential of a micro-electromechanical NIR spectrophotometer (NIR-MEMS) and visible (Vis)/NIR hyperspectral imaging (HSI) systems to predict the moisture content, antioxidant capacity (DPPH, FRAP) and total phenolic content (TPC) of treated ground peppercorns. Partial least squares (PLS) models were developed using spectra from peppercorns treated with hot-air, microwave and cold plasma. The spectra were acquired using three spectroscopy systems: NIR-MEMS (1350–1650 nm), Vis-NIR HSI (450–950 nm) and NIR HSI (957–1664 nm). Very good predictions of TPC (RPD > 3.6) were achieved using NIR-MEMS. The performance of models developed using Vis-NIR HSI and NIR HSI were good or very good for DPPH (RPD > 3.0), FRAP (RPD >2.9) and TPC (RPD > 3.8). This study demonstrated the potential of NIR-MEMS and Vis-NIR/NIR HSI to predict the moisture content, antioxidant capacity and total phenolic content of peppercorns. The spectroscopy technologies investigated are suitable for use as in-line PAT tools to facilitate improved process control and understanding during peppercorn processing. • NIR MEMS and Vis/NIR hyperspectral imaging predict antioxidant capacity of peppercorns. • NIR MEMS and Vis/NIR hyperspectral imaging predict total phenolic content of peppercorns. • Band selection and spectral pre-treatments were key for robust prediction model development. • NIR MEMS and hyperspectral imaging may be employed as in-line PAT tools in peppercorn processing. [ABSTRACT FROM AUTHOR]

There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique well suited to food analysis applications. In this study, HSI in both the visible and near infrared spectral ranges, and chemometrics were studied for prediction of the bacterial growth on beef Longissimus dorsi muscle (LD) under simulated normal (4 °C) and abuse (10 °C) storage conditions. Total viable count (TVC) prediction models were developed using partial least squares regression (PLS-R), spectral pre-treatments, band selection and data fusion methods. The best TVC prediction models developed for storage at 4 (RMSE p 0.58 log CFU/g, RPD p 4.13, R2 p 0.96), 10 °C (RMSE p 0.97 log CFU/g, RPD p 3.28, R2 p 0.94) or at either 4 or 10 °C (RMSE p 0.89 log CFU/g, RPD p 2.27, R2 p 0.86) were developed using high-level data fusion of both spectral regions. The use of appropriate spectral pre-treatments and band selection methods was key for robust model development. This study demonstrated the potential of HSI and chemometrics for real time monitoring to predict microbial growth on LD along the meat supply chain. • Microbial quality of beef stored under normal or abuse conditions can be predicted. • Spectral pre-treatments, band selection and data fusion methods are key for robust model development. • Hyperspectral imaging and chemometrics have potential for real-time monitoring of microbial quality. [ABSTRACT FROM AUTHOR]