Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/48184
metadata.artigo.dc.title: Machine learning in classification and identification of nonconventional vegetables
metadata.artigo.dc.creator: Ossani, Paulo César
Souza, Douglas Correa de
Rossoni, Diogo Francisco
Resende, Luciane Vilela
metadata.artigo.dc.subject: Classification models
Macro and micro nutrients
Supervised classification
Traditional vegetables
Vegetais - Modelos de classificação
Macronutrientes
Micronutrientes
Classificação supervisionada
Plantas alimentícias não convencionais
metadata.artigo.dc.publisher: Institute of Food Technologists
metadata.artigo.dc.date.issued: Nov-2020
metadata.artigo.dc.identifier.citation: OSSANI, P. C. et al. Machine learning in classification and identification of nonconventional vegetables. Journal of Food Science, [S. I.], v. 85, n. 12, p. 4194-4200, Dec. 2020. DOI: https://doi.org/10.1111/1750-3841.15514.
metadata.artigo.dc.description.abstract: Vegetables are important in economic, social, and nutritional matters in both the Brazilian and international scenes. Hence, some researches have been carried out in order to encourage the production and consumption of different species such as nonconventional vegetables. These vegetables have an added value because of their nutritional quality and nostalgic appeal due to the reintroduction of these species. For this reason, this article proposes the use of the machine learning technique in the construction of models for supervised classification and identification in an experiment with five leafy special of nonconventional vegetables (Tropaeolum majus, Rumex acetosa, Stachys byzantina, Lactuca cf. indica e Pereskia aculeata) assessing the characteristics of the macro and micro nutrients. In order to evaluate the classifiers’ performance, the cross-validation procedure via Monte Carlo simulation was considered to confirm the model. In ten replications, the success and error rates were obtained, considering the false positive and false negative rates, sensibility, and accuracy of the classification method. Thus, it was concluded that the use of machine learning is viable because it allows the classification and identification of nonconventional vegetables using few nutritional attributes and obtaining a success rate of over 89% in most of the classifiers tested.
metadata.artigo.dc.identifier.uri: https://doi.org/10.1111/1750-3841.15514
http://repositorio.ufla.br/jspui/handle/1/48184
metadata.artigo.dc.language: en
Appears in Collections:DAG - Artigos publicados em periódicos

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