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

Kannapot Kaewsorn; Thitima Phanomsophon; Pisut Maichoon; Dharma Raj Pokhrel; Pimpen Pornchaloempong; Warawut Krusong; Panmanas Sirisomboon; Munehiro Tanaka; Takayuki Kojima

doi:10.3390/foods12244516

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

Foods. 2023 Dec 18;12(24):4516. doi: 10.3390/foods12244516.

Authors

Kannapot Kaewsorn¹, Thitima Phanomsophon², Pisut Maichoon², Dharma Raj Pokhrel², Pimpen Pornchaloempong³, Warawut Krusong⁴, Panmanas Sirisomboon², Munehiro Tanaka⁵, Takayuki Kojima⁵

Affiliations

¹ Department of Agricultural Engineering, School of Engineering and Innovation, Rajamangala University of Technology Tawan-Ok, Chon Buri 20110, Thailand.
² Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
³ Department of Food Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
⁴ Division of Fermentation Technology, School of Food Industry, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
⁵ Laboratory of Agricultural Production Engineering, Faculty of Agriculture, Saga University, 1 Honjo-machi, Saga 840-8502, Japan.

Abstract

If 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^-1. 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 R², r², 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^-1, which included the amylose vibration band at 6834.0 cm^-1, while the toughness model was from 6094.3 to 9403.8 cm^-1 and included the 6834.0 and 8316.0 cm^-1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r² 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.

Keywords: artificial neural network; germinated brown rice; hardness; machine learning; near-infrared spectroscopy; partial least squares regression; texture; toughness.

Grants and funding

This research was funded by the NIRS research center for agricultural products and food, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand.