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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]

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]

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]