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Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
(2023-12-01)
Kaewsorn, Kannapot
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Phanomsophon, Thitima
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Maichoon, Pisut
;
Pokhrel, Dharma Raj
;
Pornchaloempong, Pimpen
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.
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The influence of processing parameters of parboiled rice on its physiochemical and texture properties
(2021-04-01)
Onmankhong, Jiraporn
;
Jongyingcharoen, Jiraporn Sripinyowanich
;
Sirisomboon, Panmanas
The impact of different parboiled rice process conditions on physical (whiteness and yellowness), chemical (amylose and fat contents), and texture (hardness and toughness) properties was studied. The parboiled rice was produced from the Suphanburi 1 variety. The correlation between chemical and texture properties was also analyzed. To study the effect of the soaking process, the time (2, 3, and 6 hr) and temperature (65 and 75°C) of soaking were altered, while the steaming condition was fixed at 100°C for 20 min. To study the effect of the steaming process, the soaking condition was fixed at 65°C for 6 hr while steaming condition was altered, including time (10 and 20 min) and temperature (90 and 100°C). The results show that the different conditions influenced the physical and chemical properties of parboiled rice. The amylose content was negatively correlated (Hardness, r = −0.52) (Toughness, r = −0.38) and fat content was positive low correlated (Hardness, r = 0.20) (Toughness, r = 0.12) with textural properties. Due to the specification of parboiled rice for exportation varying according to customer requirements, the results of this research provided some useful information for parboiled rice factories.

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