Predicting Pork Color Scores Using Computer Vision and Support Vector Machine Technology

Abstract

The objective of this study was to investigate the ability of image color features to predict subjective pork color scores. Subjective and instrumental color were assessed on the bloomed, cross-sectional surface of pork longissimus thoracis et lumborum chops. Images of pork chop samples were acquired using a computer vision system, and 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted for inclusion in partial least squares (PLS) and support vector machine (SVM) regression models. For color scores 2, 3, 4, and 5, the accuracies were 50.4, 75.9, 72.4, and 47.3% classified correctly by PLS, respectively, and 70.7, 72.8, 76.7, and 69.7% by SVM, respectively. The overall prediction accuracies of 2 models for pork color scores were 68.3% for PLS and 73.4% for SVM. There was minimal major misclassification of samples (< 0.5%). Image color features isolated through the development of PLS and SVM models, particularly SVM, show potential as a method to predict pork color scores.