Hidechika Toyoshima

Rheological Properties and Lipid Oxidation of Rice Decontaminated with Low-Energy Electrons

Microbial load and quality of rice grains which were exposed to electrons at different acceleration voltages of 170 to 200 kV were examined to evaluate the efficacy of decontaminating rice with low-energy electrons. Electrons at any acceleration voltage between 170 and 200 kV reduced microbial loads of brown rice grains to levels lower than 10 colony-forming units (CFU)/g. Higher acceleration voltages resulted in a lower viscosity and a higher thiobarbituric acid value (TBA). Milling at a yield of 90 or 88% after electron treatment made the viscosity and TBA of rice treated with electrons at 170 kV almost the same as untreated rice. Low and high compression analyses of rice grains which were exposed to electrons at 170 to 180 kV and milled at a yield of 90% followed by cooking showed almost the same hardness and stickiness as untreated grains which were milled at 90%. The results indicate that milling at a yield of 90% or lower removes the portion of rice exposed to electrons at 170 kV and that treatment of brown rice with low-energy electrons enables the preparation of milled rice with extremely low levels of microbial load and little quality deterioration.

Quality Prediction of Rice Flour by Multiple Regression Model with Instrumental Texture Parameters of Single Cooked Milled Rice Grains

ABSTRACT The purpose of this study was to develop highly accurate regression models with texture parameters of cooked milled rice grains for predicting pasting properties in terms of quality index of rice flour. Two methods were adopted as the texture measurement to acquire predictors for the models. In the calibration set, all the multiple regression models by a single-grain method exhibited a higher R2 than those by a three-grain method. Each of the former models also showed a lower SEP and a higher RPD in the validation set. The prediction performance was best for consistency (RPD = 2.4). The single-grain method was more advantageous for the pasting prediction. These results suggest that the models based on grain texture could predict rice flour quality.