Relationships between Grain Physicochemical Characteristics and Flour Particle Size Distribution for Taiwan Rice Cultivars
JIH-JOU CHEN1, CHENG-YI LII1 AND SHIN LU2*
1. Institute of Chemistry, Academia Sinica, 128, Sec. 2, Academia Rd., Nangang District, Taipei City 115, Taiwan (R.O.C.)
2. Department of Biotechnology, Van Nung Institute of Technology, 1 Wanneng Rd., Jhongli City, Taoyuan County 320, Taiwan (R.O.C.)
(Received: January 10, 2003; Accepted: September 23, 2003)
ABSTRACT
Fourteen cultivars of rice, 5 indica, 6 japonica and 3 waxy types were used to investigate the relationship between grain physicochemical characteristics and particle size distributions of rice flour. The data showed that particle size index (PSI) negatively correlated with the resistance time (RT) and milling temperature of Stenvert hardness test, damaged starch of rice flour, and percentage retained on 60 mesh (> 60 mesh %) (p < 0.05), but positively correlated with percentage that passed through 60 mesh and retained on 100 mesh (60-100 mesh %) (p < 0.05). From principal component analysis (PCA), the first and the second principal components, describing 56.14 and 21.44% of the variance of rice samples, respectively. The first principal component highly correlated with variables including RT, > 60 mesh %, milling temperature of Stenvert hardness test and damaged starch of rice flour, but negatively correlated with hardness index of Brabender micro-hardness test (BMHT), 60-100 mesh % and PSI. The second principal component positively correlated with ash and fat contents, but negatively correlated with shape and thousand-kernel weight (TKW) of rice grains. The effect of protein content was intermediate between the first and second principal component. Based on the results of PCA, the rice grain physical characteristics (i.e., RT and BMHT) are major factors, followed by chemical compositions, affecting the rice flour particle size distributions.
Key words: rice grain, physicochemical characteristics, particle size distribution, principal component analysis