Identifying key wavenumbers that improve prediction of amylose in rice samples utilizing advanced wavenumber selection techniques

• Published in: Talanta (Oxford) Vol. 224; p. 121908
• Main Authors: Mishra, Puneet, Woltering, Ernst J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-03-2021
• Subjects: Amylose; Chemometrics; Feature selection; Food chemistry; Multi-spectral; Oryza; Spectroscopy, Near-Infrared; Feature selection; Multi-spectral; Food chemistry; Chemometrics
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2020.121908

This study utilizes advanced wavenumber selection techniques to improve the prediction of amylose content in grounded rice samples with near-infrared spectroscopy. Four different wavenumber selection techniques, i.e. covariate selection (CovSel), variable combination population analysis (VCPA), bootstrapping soft shrinkage (BOSS) and variable combination population analysis-iteratively retains informative variables (VCPA-IRIV), were used for model optimization and key wavenumbers selection. The results of the several wavenumber selection techniques were compared with the predictions reported previously on the same data set. All the four wavenumber selection techniques improved the predictive performance of amylose in rice samples. The best performance was obtained with VCPA, where, with only 11 wavenumbers-based model, the prediction error was reduced by 19% compared to what reported previously on the same data set. The selected wavenumbers can help in development of low-cost multi-spectral sensors for amylose prediction in rice samples.
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•Near-infrared models for amylose prediction in rice were optimized.•Four different variable selection technique were used for optimization.•Only 11 wavenumbers were sufficient to predict amylose in rice.•With variable selection prediction error reduced by 19%.