Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions

Abstract

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.