Machine Learning in the Assessment of Meat Quality
Bryan W. Penning, W. M. Snelling, M. Jennifer Woodward-Greene
Abstract
We compare two approaches to automate carcass quality grading using different artificial intelligence methods. The first is based on image analysis, and the second uses state-of-the-art Rapid Evaporative Ionization Mass Spectrometry. Both employ machine learning (ML) to increase the speed and accuracy of carcass quality evaluation. The image analysis method increased speed and accuracy for all quality measures except marbling when compared to human meat inspectors. The mass spectrometry method tested eight ML algorithms, and achieved an impressive 81.5% to 99% accuracy in predicting carcass quality traits. However, this accuracy was dependent on the trait examined, so ML algorithms were not the answer for all traits.