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Evaluation of Hardness and Retrogradation of Cooked Rice Based on Its Pasting Properties Using a Novel RVA Testing.

ABSTRACT: With rice being one of the most important crops worldwide, rapid and objective quality evaluation methods based on physicochemical measurements of rice are necessary. We compared the pasting properties of various rice samples using three different heating and cooling programs (maximum temperatures were 93, 120, and 140 °C, respectively) in a newly developed high-temperature-type Rapid Visco Analyzer (RVA , RVA 4800). Furthermore, we investigated the relationship between the different pasting properties measured by the three programs, with starch microstructure measured by iodine scanning analysis, the physical properties of the cooked rice measured by a Tensipresser after 2 h at 25 °C or after 24 h at 6 °C, and prolamin ratio measured by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The consistency value (final viscosity-minimum viscosity) yielded by a new program of maintenance for 2 min at 120 °C using RVA 4800 had a higher positive correlation with retrograded surface hardness H1(R) (r = 0.92), retrograded overall hardness H2(R) (r = 0.90), and the absorbance at λmax (Aλmax) of cooked rice (r = 0.88) and resistant starch (r = 0.80) than those by the conventional program at 93 °C. We developed estimation formulae for H1(R) for various kinds of rice, of which the determination coefficient was 0.86. It led to an easy and rapid assay method for the cooking properties of the various rice samples.

SUBMITTER: Nakamura S

PROVIDER: S-EPMC8147165 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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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Evaluation of Hardness and Retrogradation of Cooked Rice Based on Its Pasting Properties Using a Novel RVA Testing.

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