Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
Nenhuma Miniatura disponível
Data
2019-11-01
Autores
Pérez-Rodríguez, Michael
Dirchwolf, Pamela Maia
Silva, Tiago Varão [UNESP]
Villafañe, Roxana Noelia
Neto, José Anchieta Gomes [UNESP]
Pellerano, Roberto Gerardo
Ferreira, Edilene Cristina [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced 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.
Descrição
Palavras-chave
Brown rice, Food authenticity, Pattern recognition, PDO, SD-LIBS
Como citar
Food Chemistry, v. 297.