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Filtros : "APRENDIZADO COMPUTACIONAL" "FCFRP-604" Removido: "Goularte, Rudinei" Limpar

Filtros

Artigo de periodico
Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques (2020)
Costa, Nattane Luíza da; Ximenez, João Paulo Bianchi; Rodrigues, Jairo Lisboa; Barbosa Junior, Fernando ; Barbosa, Rommel
Source: European Food Research and Technology. Unidade: FCFRP
Subjects: VINHO, APRENDIZADO COMPUTACIONAL, ESPECTROMETRIA DE MASSAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA, Nattane Luíza da et al. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques. European Food Research and Technology, v. 246, n. 6, p. 1193-1205, 2020Tradução . . Disponível em: https://doi.org/10.1007/s00217-020-03480-5. Acesso em: 16 ago. 2024.
APA
Costa, N. L. da, Ximenez, J. P. B., Rodrigues, J. L., Barbosa Junior, F., & Barbosa, R. (2020). Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques. European Food Research and Technology, 246( 6), 1193-1205. doi:10.1007/s00217-020-03480-5
NLM
Costa NL da, Ximenez JPB, Rodrigues JL, Barbosa Junior F, Barbosa R. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques [Internet]. European Food Research and Technology. 2020 ; 246( 6): 1193-1205.[citado 2024 ago. 16 ] Available from: https://doi.org/10.1007/s00217-020-03480-5
Vancouver
Costa NL da, Ximenez JPB, Rodrigues JL, Barbosa Junior F, Barbosa R. Characterization of Cabernet Sauvignon wines from California: determination of origin based on ICP-MS analysis and machine learning techniques [Internet]. European Food Research and Technology. 2020 ; 246( 6): 1193-1205.[citado 2024 ago. 16 ] Available from: https://doi.org/10.1007/s00217-020-03480-5
Artigo de periodico
Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review (2019)
Maione, Camila; Barbosa Junior, Fernando ; Barbosa, Rommel Melgaço
Source: Computers and Electronics in Agriculture. Unidade: FCFRP
Subjects: ANÁLISE DE DADOS, APRENDIZADO COMPUTACIONAL, ADULTERAÇÃO DE ALIMENTOS, MEL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAIONE, Camila e BARBOSA JUNIOR, Fernando e BARBOSA, Rommel Melgaço. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review. Computers and Electronics in Agriculture, v. 157, p. 436-446, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2019.01.020. Acesso em: 16 ago. 2024.
APA
Maione, C., Barbosa Junior, F., & Barbosa, R. M. (2019). Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review. Computers and Electronics in Agriculture, 157, 436-446. doi:10.1016/j.compag.2019.01.020
NLM
Maione C, Barbosa Junior F, Barbosa RM. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review [Internet]. Computers and Electronics in Agriculture. 2019 ; 157 436-446.[citado 2024 ago. 16 ] Available from: https://doi.org/10.1016/j.compag.2019.01.020
Vancouver
Maione C, Barbosa Junior F, Barbosa RM. Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: a review [Internet]. Computers and Electronics in Agriculture. 2019 ; 157 436-446.[citado 2024 ago. 16 ] Available from: https://doi.org/10.1016/j.compag.2019.01.020

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