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Quality Prediction of Rice Flour by Multiple Regression Model with Instrumental Texture Parameters of Single Cooked Milled Rice Grains
Author(s) -
Okadome Hiroshi,
Toyoshima Hidechika,
Shimizu Naoto,
Suzuki Keitaro,
Ohtsubo Ken'ichi
Publication year - 2005
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cc-82-0414
Subject(s) - texture (cosmology) , rice flour , predictive modelling , food science , regression analysis , linear regression , consistency (knowledge bases) , chemistry , mathematics , biological system , statistics , artificial intelligence , computer science , raw material , geometry , organic chemistry , image (mathematics) , biology
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 R 2 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.