A Hyperspectral Imaging System for Prediction of Beef Internal Quality

Abstract

In this research, a line-scanning hyperspectral imaging system (400-1100nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak hyperspectral scattring images. Thirty-two fresh beef steaks were collected. After imaging, the steaks were aged to seventh day,and then Warner-Bratzler shear (WBSF) force values and color parameters(CIE L*a*b*) were collected as quality references. The optical scattering profiles were derived from the hyperspectral images and fitted to the Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), Stepwise regression was used to identify key wavelengths and parameters. The parameters were then used to predict the tenderness and color parameters. The models was able to predict tenderness with an Rcv = 0.91; and predicte color parameters of L*, a* and b* with Rcv of 0.96, 0.96 and 0.97 respectivly. The line-scanning imaging system have potential for on-line detecting beef tenderness and color.