Prediction of Beef Quality Attributes using Hyperspectral Scattering Imaging Technique

摘要

Hyperspectral scattering images was used to predict pH, cooking lose, tenderness (i.e. WBSF: Warner-Bratzler Shear Force) and color parameters. Thirty-three fresh strip loin cuts were collected from two days postmortem carcass. After imaging and pH value measurement, the samples were vacuum packaged and aging to seventh day, then the samples color parameters (L*, a*, b*), cooking lose (CL) and WBSF were collected as references. The optical scattering profiles were derived from the hyperspectral images and fitted to the Modified Lorentzian distribution (MLD) function. MLD parameters, such as the peak height, full scattering width at half maximum (FWHM) and the scattering asymptotic were determined at each wavelength. Stepwise regression was used to identify optimal combinations of wavelengths for individual parameters. The parameters were then used to establish multi-linear regression (MLR) models to predict the beef attributes. The models were able to predict beef WBSF with Rcv = 0.86, 2-day pH values with Rcv = 0.82 and color parameters (L*, a*, b*) with Rev of 0.93, 0.90 and 0.91, whereas the correlations between scattering parameters and CL were poor. This research provided available support for the development of multispectral system which could be implemented online to determine beef steaks color and tenderness.