Portal de Periódicos da CAPES

Artigo Produção Nacional Revisado por pares

BIBTEX RIS

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

2024; Elsevier BV; Volume: 219; Linguagem: Inglês

10.1016/j.meatsci.2024.109675

1873-4138

Guilherme Lobato Menezes, Dante Teixeira Valente Júnior, Rafael Ferreira, Dário Augusto Borges Oliveira, Julcimara A Araujo, Márcio de Souza Duarte, João Ricardo Rebouças Dórea,

Food Supply Chain Traceability

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 R