Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms

Autor(es)

Ribeiro, Matheus C. S. ^[1] ; Senesi, Giorgio S. ^[2] ; Cabral, Jader S. ^[3] ; Cena, Cicero ^[1] ; Marangoni, Bruno S. ^[1] ; Kiefer, Charles ^[1] ; Nicolodelli, Gustavo ^[4]

Afiliação do(s) autor(es)

^[1] Univ Fed Mato Grosso do Sul, POB 549, BR-79070900 Campo Grande, MS - Brazil

^[2] CNR Ist Sci & Tecnol Plasmi ISTP, Sede Bari, Via Amendola 122-D, I-70126 Bari - Italy

^[3] Univ Fed Uberlandia, Inst Fis, POB 593, BR-38400902 Uberlandia, MG - Brazil

^[4] Univ Fed Santa Catarina, Dept Fis, POB 476, BR-88040900 Florianopolis, SC - Brazil

• Tipo de documento: Artigo Científico
• Fonte: APPLIED OPTICS; v. 59, n. 32, p. 10043-10048, NOV 10 2020.
• Citações Web of Science: 0

Resumo

Laser-induced breakdown spectroscopy (LIBS) for atomic multi-elementary analyses, and Fourier transform infrared spectroscopy (FTIR) for molecular identification, are often suggested as the most versatile spectroscopic techniques. The present work aimed to evaluate the performance of both techniques, LIBS and FTIR, combined with principal component analysis (PCA) and machine learning (ML) algorithms in the detection of the composition analysis and differentiation of four different types of rice, white, brown, black, and red. The two techniques were primarily used to obtain the elemental and molecular qualitative characterization of rice samples. Then, LIBS and FTIR data sets were subjected to PCA and supervised ML analysis to investigate which main chemical features were responsible for nutritional differences for the white (milled) and colored rice samples. In particular, PCA data analysis suggested that protein, fatty acids, and magnesium were the highest contributors to the sample's differentiation. The ML analysis based on this information yielded a 100% level of accuracy, sensitivity, and specificity on sample classification. In conclusion, LIBS and FTIR coupled with multivariate analysis were confirmed as promising tools alternative to traditional analytical techniques for composition analysis and differentiation when subtle chemical variations were observed. (C) 2020 Optical Society of America (AU)

• Processo FAPESP: 16/01513-0 - Combinação de LIBS (Laser-Induced Breakdown Spectroscopy) e ICP OES (Inductively Coupled Plasma Optical Emission Spectrometry) na análise de amostras de resíduo eletrônico (circuito eletrônico e polímeros), alimentos e líquidas de difícil preparação
• Beneficiário: Edenir Rodrigues Pereira Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular