Quality Assessment of Beef Using Computer Vision Technology
Received: May 13, 2020 ; Revised: Jul 08, 2020 ; Accepted: Jul 22, 2020
Published Online: Jul 23, 2020
Imaging technique or computer vision technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of computer vision technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 hours post-mortem. Traits evaluated were color value (L*,a*,b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, ether extract, ash, Thiobarbituric acid reactive substance (TBARS), Peroxide value (POV), Free fatty acid (FFA), Total coliform count (TCC), Total viable count (TVC) and Total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination was done in triplicate and the mean value was reported. Data analysis was carried out using the programme Stat graphics Centurion XV.I. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r = 0.65) and moisture (r = 0.56) with ‘a*’ value obtained from image analysis and the highest calibration and prediction accuracy was found in Lightness (R²c = 0.73, R²p = 0.69) in beef. Results of this work show that computer vision technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.