| Revista: | Scientia Agricola |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000455490 |
| ISSN: | 0103-9016 |
| Autores: | Maione, Camila1 Araujo, Eloá Moura2 Santos Araujo, Sabrina Novaes dos2 Boim, Alexys Giorgia Friol2 Barbosa, Rommel Melgaço1 Alleoni, Luís Reynaldo Ferracciú2 |
| Instituciones: | 1Universidade Federal de Goias, Instituto de Informatica, Goiania, Goias. Brasil 2Universidade de Sao Paulo, Departamento de Ciencia do Solo, Piracicaba, Sao Paulo. Brasil |
| Año: | 2022 |
| Volumen: | 79 |
| Número: | 1 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico |
| Resumen en inglés | Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called F–score, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables |
| Disciplinas: | Agrociencias |
| Palabras clave: | Hortalizas, Lechuga, Lactuca sativa, Trópico, Brasil, Nutrientes, ICP-OES, Minería de datos |
| Keyword: | Vegetables, Lettuce, Lactuca sativa, Tropics, Brazil, Nutrients, ICP–OES, Data mining |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
