[Study of spatially resolved hyperspectral scattering images for assessing beef quality characteristics].

Hyperspectral scattering techniques were used to predict beef pH, tenderness (i. e. WBSF: Warner-Bratzler Shear Force) and color parameters. Thirty-three fresh strip loin cuts were collected from 2-day postmortem carcass. After capturing scattering images and measuring pH values, the samples were vacuum packaged and aged to seventh day, then their color parameters (L*, a*, b*) and WBSF were measured as references. The optical scattering profiles were extracted from the hyperspectral images and fitted to the Lorentzian distribution (LD) function with three parameters. LD parameters, such as the peak height, full scattering width at half maximum (FWHM) and the scattering asymptotic were calculated at individual wavelength. Stepwise regression was used to determine optimal combinations of wavelengths for each of parameters. The optimal combinations were then used to establish multi-linear regression (MLR) models to predict the beef attributes. The full cross validation method was used to examine the performance of models. The models were able to predict beef WBSF with R(CV) = 0.86, and with the SE(CV) (the standard error of cross validation) of 11.7 N, 91% classification accuracy could be obtained. Two-day pH values with R(CV) = 0.86, SE(CV) = 0.07 and color parameters (L*, a*, b*) with R(CV) of 0.92, 0.90 and 0.88, with the SE(CV) of 0.90, 1.34 and 0.41 were obtained respectively. This research provided available technique for the development of multispectral system, which could be implemented online to determine beef steaks color and tenderness.