Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

• Autores: Pérez Rodríguez, Michael; Dirchwolf, Pamela Maia; Silva, Tiago Varão; Villafañe, Roxana Noelia; Gómez Neto, José Anchieta; Pellerano, Roberto Gerardo; Ferreira, Edilene Cristina
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Food Chemistry, 2019, vol. 297, p. 1-6.
• Materia: Food authenticity; Pdo; Brown rice; Sd-Libs; Pattern recognition
• Institución: Universidad Nacional del Nordeste

Fil: Pérez Rodríguez, Michael. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina.
Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Dirchwolf, Pamela Maia. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; Argentina.
Fil: Silva, Tiago Varão. Universidad Estadual de São Paulo. Instituto de Química de Araraquara; Brasil.
Fil: Villafañe, Roxana Noelia. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química San Luis; Argentina.
Fil: Villafañe, Roxana Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet-San Luis; Argentina.
Fil: Gómez Neto, José Anchieta. Universidad Estadual de São Paulo. Instituto de Química de Araraquara; Brasil.
Fil: Pellerano, Roberto Gerardo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Ferreira, Edilene Cristina. Universidad Estadual de São Paulo. Instituto de Química de Araraquara; Brasil.
Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laserinduced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.