Vision-based method for tracking meat cuts in slaughterhouses

Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines

The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

A Potential Use of 3-D Scanning to Evaluate the Chemical Composition of Pork Meat

The aim of this study was to determine the possibility of 3-D scanning method in chemical composition evaluation of pork meat. The sampling material comprised neck muscles (1000 g each) obtained from 20 pork carcasses. The volumetric estimation process of the elements was conducted on the basis of point cloud collected using 3-D scanner. Knowing the weight of neck muscles, their density was calculated which was subsequently correlated with the content of basic chemical components of the pork meat (water, protein and fat content, determined by standard methods). The significant correlations (P ≤ 0.05) between meat density and water (r = 0.5213), protein (r = 0.5887), and fat (r = -0.6601) content were obtained. Based on the obtained results it seems likely to employ the 3-D scanning method to compute the meat chemical composition.

PRACTICAL APPLICATION

The use of the 3-D scanning method in industrial practice will allow to evaluate the chemical composition of meat in online mode on a dressing and fabrication line and in a rapid, noninvasive manner. The control of the raw material using the 3-D scanning will allow to make visual assessment more objective and will enable optimal standardization of meat batches prior to processing stage. It will ensure not only the repeatability of product quality characteristics, but also optimal use of raw material-lean and fat meat. The knowledge of chemical composition of meat is essential due to legal requirements associated with mandatory nutrition facts labels on food products.