Cavalcanti, Rodrigo Nunes 
; Barbosa, Vitor Pereira ; Gut, Jorge Andrey Wilhelms ; Tadini, Carmen Cecília
Fonte: Measurement: Food. Unidade: EP
Assuntos: PROCESSAMENTO DE ALIMENTOS, CONDUTIVIDADE ELÉTRICA, ESPECTROSCOPIA ELETRÔNICA
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CAVALCANTI, Rodrigo Nunes et al. Predicting dielectric properties of fruit juices at 915 and 2450 MHz using machine learning and physicochemical measurements. Measurement: Food, v. 14, p. 1-14, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.meafoo.2024.100158. Acesso em: 15 nov. 2025.APA
Cavalcanti, R. N., Barbosa, V. P., Gut, J. A. W., & Tadini, C. C. (2022). Predicting dielectric properties of fruit juices at 915 and 2450 MHz using machine learning and physicochemical measurements. Measurement: Food, 14, 1-14. doi:10.1016/j.meafoo.2024.100158DOINLM
Cavalcanti RN, Barbosa VP, Gut JAW, Tadini CC. Predicting dielectric properties of fruit juices at 915 and 2450 MHz using machine learning and physicochemical measurements [Internet]. Measurement: Food. 2022 ; 14 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.meafoo.2024.100158Vancouver
Cavalcanti RN, Barbosa VP, Gut JAW, Tadini CC. Predicting dielectric properties of fruit juices at 915 and 2450 MHz using machine learning and physicochemical measurements [Internet]. Measurement: Food. 2022 ; 14 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.meafoo.2024.100158
