Quality Assessment of Beef Using Computer VisionTechnology

摘要

Imaging technique or computer vision (CV) technology has received huge attentionas a rapid and non-destructive technique throughout the world for measuringquality attributes of agricultural products including meat and meat products.This study was conducted to test the ability of CV technology to predict thequality attributes of beef. Images were captured from longissimusdorsi muscle in beef at 24 h post-mortem. Traits evaluated werecolor value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture,crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS),peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), totalviable count (TVC) and total yeast-mould count (TYMC). Images were analyzedusing the Matlab software (R2015a). Different reference values were determinedby physicochemical, proximate, biochemical and microbiological test. Alldetermination were done in triplicate and the mean value was reported. Dataanalysis was carried out using the programme Statgraphics Centurion XVI.Calibration and validation model were fitted using the software Unscrambler Xversion 9.7. A higher correlation found in a* (r=0.65) and moisture(r=0.56) with ‘a*’ value obtained from image analysis andthe highest calibration and prediction accuracy was found in lightness(r2c=0.73,r2p=0.69) in beef. Results of this work show thatCV technology may be a useful tool for predicting meat quality traits in thelaboratory and meat processing industries.