A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging

Abstract

Current methods for detecting the bacterial contamination of meat are time-consuming, labor-intensive, and giving retrospective information; therefore, the objective of this study was to investigate the feasibility of hyperspectral scattering imaging for rapid and nondestructive determination of total viable count (TVC) in pork meat. Fresh pork meat was purchased from a local market and stored at 10 °C for 1–15 days. In total, 59 samples were used in this study, and three to four samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological analysis on each day of the experiment. Both the Lorentzian function and the Gompertz function were exploited to interpret the scattering profiles of pork meat samples, and good fitting results were obtained between 472 and 1,000 nm. Stepwise multiple linear regression (SMLR) method was performed to establish the prediction models, and moving average method with the filter size ranging from 3-point to 15-point was applied to improve the modeling results, respectively. Among the models established, the models developed by the Lorentzian parameter b and the Gompertz parameter β performed best for predicting pork meat TVC, with the correlation coefficient of validation set (Rv) of 0.94 and 0.93, respectively, after 13-point and 11-point moving average. The Lorentzian parameter a and the Gompertz parameters α and δ can also give good prediction results, with Rv of 0.83, 0.88, and 0.82, respectively. The results demonstrated that hyperspectral scattering imaging combined with the Lorentzian function and the Gompertz function can be a powerful tool for evaluating the microbial safety of meat in the future.