Application of Instrument-Based Multiple Texture Measurement of Cooked Milled-Rice Grains to Rice Quality Evaluation

Abstract

The subject of this study was to examine a method of multiple texture measurement of cooked milled-rice grains and to develop techniques for quality evaluation on the basis of the method. The low-and high-compression (LHC) test, which measures surface and overall texture properties (25% and 90% compression ratios for the single grain thickness after cooking), was effective in enhancing the classification accuracy of the texture. A multiple regression model based on surface texture properties indicated higher prediction accuracy for palatability than that with using overall texture properties. The surface adhesion distance was adopted for distinguishing stickiness. Concerning the effect of protein content on texture among the samples of the same cultivar given different amounts of nitrogen fertilizer, the protein content showed a higher correlation with surface hardness than with overall hardness. Surface hardness was a more reliable indicator of protein content and palatability in the same cultivar. It revealed that the surface texture can be used as a key quality index. A multiple regression model of the LHC test was able to predict amylose content more precisely than using the conventional method. It was possible, using the single-grain method with the LHC test, to evaluate not only grain texture but also many other qualities such as protein content, palatability and amylose content.