Source: Measurement: Food. Unidade: EP
Subjects: PROCESSAMENTO DE ALIMENTOS, CONDUTIVIDADE ELÉTRICA, ESPECTROSCOPIA ELETRÔNICA
ABNT
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: 09 set. 2024.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 2024 set. 09 ] 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 2024 set. 09 ] Available from: https://doi.org/10.1016/j.meafoo.2024.100158