MRI-computer vision on fresh and frozen-thawed beef: Optimization of methodology for classification and quality prediction

Abstract

This study aims to evaluate the capability of Magnetic Resonance Imaging (MRI) and computer vision techniques to classify fresh (raw F) (n = 12) and frozen-thawed (FT) (n = 12) beef and predict physico-chemical, texture and sensory characteristics by optimization the methodology for image analysis (algorithm) and data analysis (regressor), testing different algorithm-regressor combinations. The accuracy of the classification and prediction results especially depend on the algorithm. Different optimum combinations were found for classification (Fractal with CForest, RF or SVM) and prediction of quality parameters of raw FT (Fractal-CForest or Fractal-RF) and cooked FT samples (Classic-RF). Thus, the computational analysis of MRI, especially the algorithm to analyze the image, may be set as a function of the aim (classification or prediction) and of the type of sample (raw or cooked), while the analysed characteristic is not relevant. This study firstly showed the capability of MRI to classify beef (raw F vs. raw FT) and to determine quality characteristics in a non-destructive way.