Principal component regression of near-infrared reflectance spectra for beef tenderness prediction

Tenderness is the most important factor affecting consumer perception of eating quality of meat. In this paper, the development of the principal component regression (PCR) models to relate near-infrared (NIR) reflectance spectra of raw meat to Warner-Bratzler (WB) shear force measurement of cooked meat was presented. NIR reflectance spectra with wavelengths from 1100 to 2498 nm were collected on 119 longissimus dorsi meat cuts. The 1st principal component (or factor) fi-om the absorption spectra log(1/R) showed that the most significant variance from the spectra of tough and tender meats were due to the absorptions of fat at 1212, 1722, and 2306 nm and water at 1910 nm. The distinctive fat absorption peaks at 1212, 1722, 1760, and 2306 nm were found in the 2nd factor of the second derivative spectra of meat. In addition, the local minima in the 2nd principal component of the second derivative spectra showed the importance of water absorption at 1153 nm and protein absorption at 1240, 1385, and 1690 nm. When the absorption spectra between 1100 nm and 2498 nm. were used, the coefficient of determination (R-2) of the PCR model to predict WB shear force tenderness was 0.692. The R-2 was 0.612 when the spectra between 1100 nm and 1350 nm were analyzed. When the second derivatives of the spectral data were used, the R2 of the PCR model to predict WB shear force of the meat was 0.633 for the full spectral range of 1100 to 2498 nm and 0.616 for the spectral range of 1100 to 1350 nm.