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Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools
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Resumo(s)
Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs), allowed for the development of prediction models of pasting parameters, such as the breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), from 166 rice samples. The models developed using iPLS and siPLS were characterized, respectively, by the following regression values: BD (R = 0.84; R = 0.88); FV (R = 0.57; R = 0.64); PV (R = 0.85; R = 0.90); ST (R = 0.85; R = 0.88); and TR (R = 0.85; R = 0.84). Meanwhile, ANN was also tested and allowed for a significant improvement in the models, characterized by the following values corresponding to the calibration and testing procedures: BD (Rcal = 0.99; Rtest = 0.70), FV (Rcal = 0.99; Rtest = 0.85), PV (Rcal = 0.99; Rtest = 0.80), ST (Rcal = 0.99; Rtest = 0.76), and TR (Rcal = 0.99; Rtest = 0.72). Each model was characterized by a specific spectral region that presented significative influence in terms of the pasting parameters. The machine learning models developed for these pasting parameters represent a significant tool for rice quality evaluation and will have an important influence on the rice value chain, since breeding programs focus on the evaluation of rice quality.
Descrição
Funding Information: Funding for this research was received from TRACE-RICE—Tracing rice and valorizing side streams along with Mediterranean blockchain, grant no. 1934 (call 2019, Section 1 Agrofood)—of the PRIMA Program supported under Horizon 2020, the European Union’s Framework Program for Research and Innovation. This work was also supported by FCT, the Portuguese Foundation for Science and Technology through the R&D Unit, UIDB/04551/2020 (GREEN-IT, Bioresources for Sustainability) and project UIDB/04033/2020. P.S. Sampaio acknowledges the financial support of the postdoctoral research grant included in this project RECI/AGR-TEC/0285/2012, BEST-RICE-4-LIFE project. Publisher Copyright: © 2023 by the authors.
Palavras-chave
artificial neural network NIR spectroscopy pasting parameters rice General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes