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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]