Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modeling Methods for Evaluation of Texture Properties of Cooked Germinated Brown Rice Using the Near Infrared Spectra of Uncooked Whole Grain

Kannapot Kaewsorn
,
Thitima Phanomsophon
,
Pisut Maichoon
ORCID logo ,
Dharma Pokhrel
,
Pimpen Pornchaloempong
ORCID logo ,
Warawut Krusong
ORCID logo ,
Panmanas Sirisomboon
* ORCID logo ,
Munehiro Tanaka
* ORCID logo ,
Takayuki Kojima
Version 1 : Received: 12 November 2023 / Approved: 13 November 2023 / Online: 13 November 2023 (08:57:36 CET)

A peer-reviewed article of this Preprint also exists.

Kaewsorn, K.; Phanomsophon, T.; Maichoon, P.; Pokhrel, D.R.; Pornchaloempong, P.; Krusong, W.; Sirisomboon, P.; Tanaka, M.; Kojima, T. Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain. Foods 2023, 12, 4516. Kaewsorn, K.; Phanomsophon, T.; Maichoon, P.; Pokhrel, D.R.; Pornchaloempong, P.; Krusong, W.; Sirisomboon, P.; Tanaka, M.; Kojima, T. Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain. Foods 2023, 12, 4516.

Abstract

The models of partial least squares (PLS) regression and artificial neural network (ANN) for evaluation of texture properties of cooked germinated brown rice (GBR) using the Fourier transform near infrared (NIR) spectra of uncooked whole grain combined with data separation methods, spectral pretreatment methods were investigated in this study. The ANN was outperformed in evaluation of hardness by back extrusion test of cooked GBR using the smoothing combined with standard normal variate pretreated NIR spectra in the range of 12,500-4,000 cm−1 of 188 whole grain samples where the calibration sample set was separated from prediction set by Kennard-Stone method where the best ANN model for hardness from hidden layers of 25 and 8 iteration time provided R2, r2, RMSEC, RMSEP, Bias and RPD of 0.9987, 0.9447, 0.1021 N, 0.7699 N, 0.0216 N and 4.3 respectively. The PLS regression of 64 sample KDML GBR group and 64 sample various variety GBR group, provided models for the hardness of the former and the toughness of the latter which developed by using 7506−5446.3, 4605.4−4242.9 cm−1 which included the amylose vibration band of 6834 cm−1 while toughness model was from 9403.8-6094.3 cm−1 where included 6834 and 8316 cm−1 vibration bands of amylose which influenced the texture of cooked rice. The PLS regression models for hardness and toughness were with the r2 of 0.85 and 0.82, and the RPD of 2.9 and 2.4, respectively. The ANN model for hardness of cooked GBR should be implemented to the practical use in GBR production factories for the quality assurance and further updating using more samples and several brands to obtain the robust models.

Keywords

germinated brown rice; hardness; toughness; texture; near infrared spectroscopy; partial least squares regression; artificial neural network

Subject

Biology and Life Sciences, Food Science and Technology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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