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

Abstract

Consumers often find it challenging to assess meat sensory quality, influenced by tenderness and intramuscular fat (IMF). This study aims to develop a computer vision system (CVS) using smartphone images to classify beef and pork steak tenderness (1), predicting shear force (SF) and IMF content (2), and performing a comparative evaluation between consumer assessments and the method's output (3). The dataset consisted of 924 beef and 514 pork steaks (one image per steak). We trained a deep neural network for image classification and regression. The model achieved an F1-score of 68.1 % in classifying beef as tender. After re-categorizing the dataset into ‘tender’ and ‘tough’, the F1-score for identifying tender increased to 76.6 %. For pork loin tenderness, the model achieved an F1-score of 81.4 %. This score slightly improved to 81.5 % after re-categorization into two classes. The regression models for predicting SF and IMF in beef steak achieved an R² value of 0.64 and 0.62, respectively, with a root mean squared prediction error (RMSEP) of 16.9 N and 2.6 %. For pork loin, the neural network predicted SF with an R² value of 0.76 and an RMSEP of 9.15 N, and IMF with an R² value of 0.54 and an RMSEP of 1.22 %. In 1000 paired comparisons, the neural network correctly identified the more tender beef steak in 76.5 % of cases, compared to a 46.7 % accuracy rate for human assessments. These findings suggest that CVS can provide a more objective method for evaluating meat tenderness and IMF before purchase, potentially enhancing consumer satisfaction.