• Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

      Achata, Eva M.; Inguglia, Elena Sofia; Esquerre, Carlos; Tiwari, Brijesh K; O'Donnell, Colm P.; Department of Agriculture, Food and the Marine; 13/FM/508 (Elsevier, 2018-10-20)
      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 (RMSEp 1.9%, R2p 0.92) and cooked (RMSEp 2.6%, R2p 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.
    • Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

      Esquerre, Carlos A.; Achata, Eva M.; García-Vaquero, Marco; Zhang, Zhihang; Tiwari, Brijesh K; O'Donnell, Colm P. (Elsevier BV, 2020-09)
      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.
    • Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions

      Achata, Eva M.; Oliveira, Marcia; Esquerre, Carlos A.; Tiwari, Brijesh K; O'Donnell, Colm P.; Irish Department of Agriculture, Food & the Marine; 13/FM/508 (Elsevier BV, 2020-06)
      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 (RMSEp 0.58 log CFU/g, RPDp 4.13, R2p 0.96), 10 °C (RMSEp 0.97 log CFU/g, RPDp 3.28, R2p 0.94) or at either 4 or 10 °C (RMSEp 0.89 log CFU/g, RPDp 2.27, R2p 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.