Classification of crispness of food materials by deep neural networks (2023)
Source: Journal of Texture Studies. Unidade: FZEA
Subjects: REDES NEURAIS, INDÚSTRIA DE ALIMENTOS, COMPORTAMENTO DO CONSUMIDOR, CARNES E DERIVADOS, BATATA
ABNT
LOPES, Rafael Zinni e DACANAL, Gustavo César. Classification of crispness of food materials by deep neural networks. Journal of Texture Studies, v. 54, n. 6, p. 845-859, 2023Tradução . . Disponível em: https://doi.org/10.1111/jtxs.12792. Acesso em: 27 nov. 2025.APA
Lopes, R. Z., & Dacanal, G. C. (2023). Classification of crispness of food materials by deep neural networks. Journal of Texture Studies, 54( 6), 845-859. doi:10.1111/jtxs.12792NLM
Lopes RZ, Dacanal GC. Classification of crispness of food materials by deep neural networks [Internet]. Journal of Texture Studies. 2023 ; 54( 6): 845-859.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1111/jtxs.12792Vancouver
Lopes RZ, Dacanal GC. Classification of crispness of food materials by deep neural networks [Internet]. Journal of Texture Studies. 2023 ; 54( 6): 845-859.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1111/jtxs.12792
