Feasibility of utilizing color imaging and machine learning for adulteration detection in minced meat

Abstract

Meat products are popular foods and there is a need for cost-effective technologies for rapid quality assessment. In this study, RGB color imaging coupled with machine learning algorithms were investigated to detect plant and animal adulterants with ratios of from 1 to 50% in minced meat. First, samples were classified as either pure or adulterated, then adulterated samples were classified based on the adulterant's type. Finally, regression models were developed to predict the adulteration quantity. Linear discriminant classifier enhanced by bagging ensembling performed the best with overall classification accuracies for detecting pure or adulterated samples up to 99.1% using all features, and 100% using selected features. Classification accuracies for adulteration origin were 48.9–76.1% using all features and 63.8% for selected features. Regression trees were used for adulterant level quantification and the r (RPD) values were up to 98.0%(5.0) based on all features, and 94.5%(3.2) for selected features. Gray-level and co-occurrence features were more effective than other color channels in building classification and regression models. This study presents a non-invasive, and low-cost system for adulteration detection in minced meats.

Keywords

• Machine learning
• RGB
• Meat adulteration
• Industry 4.0
• Digital manufacturing
• Non-invasive sensing