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

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...

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Bibliographic Details
Published in:Applied optics (2004) 2020-11, Vol.59 (32), p.10043
Main Authors: Ribeiro, Matheus C. S., Senesi, Giorgio S., Cabral, Jader S., Cena, Cícero, Marangoni, Bruno S., Kiefer, Charles, Nicolodelli, Gustavo
Format: Article
Language:English
Subjects:
Algorithms
Atomic beam spectroscopy
Composition
Data analysis
Differentiation
Fatty acids
Fourier transforms
Infrared analysis
Infrared spectroscopy
Laser induced breakdown spectroscopy
Machine learning
Magnesium
Multivariate analysis
Performance evaluation
Principal components analysis
Qualitative analysis
Spectrum analysis
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Summary: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.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.409029