Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks

Meat Sci. 2014 Feb;96(2 Pt A):837-42. doi: 10.1016/j.meatsci.2013.09.012. Epub 2013 Sep 19.

Abstract

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat.

Keywords: Beef; Color image texture features; STEPWISE regression; SVM; Troponin-T degradation.

MeSH terms

  • Animals
  • Blotting, Western
  • Cattle
  • Color
  • Electrophoresis, Polyacrylamide Gel
  • Food Handling / methods
  • Fourier Analysis
  • Image Processing, Computer-Assisted / methods*
  • Meat / analysis*
  • Muscle, Skeletal
  • Neural Networks, Computer
  • Support Vector Machine
  • Troponin T / chemistry*

Substances

  • Troponin T