Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools

Pedro Sousa Sampaio; Bruna Carbas; Carla Brites

doi:10.3390/app13169081

Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools

Pedro Sousa Sampaio, Bruna Carbas, Carla Brites

COPELABS - Cognitive and People-centric Computing

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

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 (R_cal = 0.99; R_test = 0.70), FV (R_cal = 0.99; R_test = 0.85), PV (R_cal = 0.99; R_test = 0.80), ST (R_cal = 0.99; R_test = 0.76), and TR (R_cal = 0.99; R_test = 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.

Original language	English
Article number	9081
Journal	Applied Sciences (Switzerland)
Volume	13
Issue number	16
DOIs	https://doi.org/10.3390/app13169081
Publication status	Published - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Funding

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.

Funders	Funder number
European Union’s Framework Program for Research and Innovation
TRACE-RICE	1934
Fundação para a Ciência e a Tecnologia	UIDB/04551/2020, UIDB/04033/2020, BEST-RICE-4-LIFE, RECI/AGR-TEC/0285/2012
Horizon 2020

Keywords

NIR spectroscopy
artificial neural network
pasting parameters
rice

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10.3390/app13169081

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title = "Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools",

abstract = "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.",

keywords = "NIR spectroscopy, artificial neural network, pasting parameters, rice",

author = "Sampaio, {Pedro Sousa} and Bruna Carbas and Carla Brites",

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doi = "10.3390/app13169081",

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T1 - Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools

AU - Sampaio, Pedro Sousa

AU - Carbas, Bruna

AU - Brites, Carla

PY - 2023/8

Y1 - 2023/8

N2 - 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.

AB - 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.

KW - NIR spectroscopy

KW - artificial neural network

KW - pasting parameters

KW - rice

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DO - 10.3390/app13169081

M3 - Article

AN - SCOPUS:85169130693

SN - 2076-3417

VL - 13

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

IS - 16

M1 - 9081

ER -

Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools

Abstract

Bibliographical note

Funding

Keywords

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