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

¹National Food Research Institute, Tsukuba City, Ibaraki Prefecture, 305-8642 Japan. ²Corresponding author. Phone: 81-29-838-8027. Fax: 81-29-838-7996. E-mail: okadome@nfri.affrc.go.jp ³University of Tsukuba, Tsukuba City, Ibaraki Prefecture, 305 Japan.

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.