Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain

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dc.contributor.authorKaewsorn, Kannapot
dc.contributor.authorPhanomsophon, Thitima
dc.contributor.authorMaichoon, Pisut
dc.contributor.authorPokhrel, Dharma Raj
dc.contributor.authorPornchaloempong, Pimpen
dc.contributor.authorKrusong, Warawut
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorTanaka, Munehiro
dc.contributor.authorKojima, Takayuki
dc.date.accessioned2026-08-06T10:42:45Z
dc.date.available2026-08-06T10:42:45Z
dc.date.issued2023-12-01
dc.description.abstractIf a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm<sup>−1</sup>. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R<sup>2</sup>, r<sup>2</sup>, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm<sup>−1</sup>, which included the amylose vibration band at 6834.0 cm<sup>−1</sup>, while the toughness model was from 6094.3 to 9403.8 cm<sup>−1</sup> and included the 6834.0 and 8316.0 cm<sup>−1</sup> vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r<sup>2</sup> values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.
dc.identifier.citationFoods, 12(24), 2023
dc.identifier.doi10.3390/foods12244516
dc.identifier.issn23048158
dc.identifier.other2-s2.0-85180666344
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14751
dc.sourceFoods
dc.subjectartificial neural network
dc.subjectgerminated brown rice
dc.subjecthardness
dc.subjectmachine learning
dc.subjectnear-infrared spectroscopy
dc.subjectpartial least squares regression
dc.subjecttexture
dc.subjecttoughness
dc.titleModeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
dc.typeArticle

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