80 Empowering informed choices: How computer vision can assist consumers in making decisions about meat quality

Abstract Consumer perception of meat quality is highly influenced by meat tenderness and juiciness. Artificial intelligence technology, such as computer vision systems (CVS), can be a powerful tool to predict such meat attributes in retail markets and help consumers make informed decisions. This study aimed to develop a CVS using smartphone images to classify beef steaks and pork chops tenderness (1), predicting shear-force (SF) and intramuscular fat (IMF) content (2), and performing a comparative evaluation between consumer assessments and the CVS (3). The dataset for beef steak consisted of 915 images (one image per steak), with 691 in the training set, 82 in the validation set, and 142 in the testing set. For pork, there were 514 images (one image per chop), distributed as 377 in the training set, 80 in the validation set, and 57 in the testing set. We trained deep neural networks (Xception), pretrained on the ImageNet dataset, for image classification and regression. The tenderness of the steaks was categorized based on Warner-Bratzler shear-force (N) as tender, intermediate, and tough. To achieve the third object, 1,000 pairwise comparisons were drawn from the 142 testing set images, where both consumers and the CVS performed predictions on tender and tough meat. The option with the least SF in each pair was identified as the most tender meat, with ground truth established using SF measurements. The same pairs were presented to meat consumers through a survey. For classifying beef as tender and tough, the algorithms demonstrated F1-scores of 68.1% and 70.8%, respectively. However, intermediate meat yielded worse results, with an F1-score of 36.4%. The dataset was subsequently re-categorized into two classes: tender and tough, with all intermediate steaks classified as tough, and resulted in an F1-score of 76.6% for tough meat. For classifying pork chops as tender and tough, the algorithms demonstrated F1-scores of 81.4% and 85.7%, respectively. The intermediate class in pork chops also exhibited worse results, with an F1-score of 24.0%. After re-categorizing the dataset into two classes, the F1-score for classifying pork chops as tough was 83.9%. The regression model predicted SF in beef steak and pork chops with R2 of 0.64 and 0.76, and RMSEP of 16.9 N and 9.15 N, respectively. The regression model, after analyzing with 1,000 pairwise comparisons, accurately predicted the tenderest steak with an accuracy of 76.5%. In contrast, human recognition of meat tenderness achieved an average accuracy of 46.7% for beef steak. To predict IMF in beef steaks and pork chops the model achieved R2 values of 0.61 and 0.54, and RMSEP of 2.6% and 1.22%, respectively. These findings suggest that CVS can provide a more objective method for evaluating meat tenderness and IMF before purchase, potentially enhancing consumer satisfaction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.