International Journal of Food Processing Technology  (Volume 2 Issue 2)
 Hyperspectral Imaging for Beef Tenderness Assessment  International Journal of Food Processing Technology
Pages 18- 25

F. Saadatian, L. Liu and M. O. Ngadi


Published: 30 July 2015
 Tenderness is one of the principal properties of meat quality. The traditional way to measure tenderness the beef is time consuming and also destructive, and therefore not appropriate for rapidly identifying quality parameters on the processing line, with the minimum of human intervention. The objective of the present research was to measure the tenderness of cooked beef samples obtained from four types of muscles (i.e. infraspinatus (TB), gluteus medius (TS), psoas major (TL), and longissimus thorasis (RE)) at three different durations of dry aging (Fresh (0 days), 14 days, and 21 days), using near infrared hyperspectral imaging. Hyperspectral reflectance spectra (900 nm <l†< 1700 nm) were acquired for a total of 260 beef steak samples with dry-ages of 0, 14 or 21 days. After imaging, samples were cooked and the Warner-Bratzler shear force (WBSF), a parameter inversely related to meat tenderness, was measured. After reflectance calibration, a region of interest (ROI) was selected from each acquired hyperspectral image and stepwise regression was applied to the ROI to select wavelengths that were strongly related to cooked meat tenderness. Multiple Linear Regression (MLR) calibration models were developed for quantitative evaluation of beef tenderness. The correlation coefficient (R) and the root mean square error (RMSE) were employed to evaluate the calibration model’s predictive ability for each group. The calibration model developed predicted tenderness with R values of 0.89, 0.86, 0.81 and 0.83 for TS, RE, TB, and TL, respectively. The results revealed that the HSI could be used for non-destructive measurement of beef tenderness in beef having undergone three different durations of aging.
 Hyperspectral imaging, tenderness, beef, meat quality, warner-bratzlershear force, dry-aging, multiple linear regression.