Location: Processed Foods Research
Title: Relationship between Rice Sample Milling Conditions and Milling Quality Authors
|Amaratunga, K.S.P. - UC DAVIS, DAVIS, CA|
|Thompson, James - UC DAVIS, DAVIS, CA|
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 1, 2007
Publication Date: August 1, 2007
Citation: Pan, Z., Amaratunga, K., Thompson, J.F. 2007. Relationship between Rice Sample Milling Conditions and Milling Quality. Transactions of the ASABE. 50(4):1307-1313. Interpretive Summary: This research studied the effect of milling conditions on milling quality of medium grain rough rice and suggested the milling conditions for achieving high rice milling quality.
Technical Abstract: The objective of this study was to evaluate the effect of milling conditions on milling quality of medium grain rough rice M202. Using a McGill No. 3 mill, the conditions studied were milling weight and duration, and polishing weight and duration. This research examined the relationships among the milled rice quality parameters, namely total rice yield, head rice yield, whiteness index and total lipid content. Head rice yield was more sensitive to changes in milling conditions than total rice yield. It decreased by 4.6 percentage points when milling weight was increased from 2.72 to 6.36 kg. Milling weight and milling duration had more influence on head rice yield than polishing weight and duration. For both milling and polishing, using lower weights for a longer duration could increase head rice yield without changing the whiteness compared to the standard procedures of the Federal Grain Inspection Service. The total lipid content of milled rice, measured using a near infrared (NIR) method, correlated well with the whiteness index. This indicates that the NIR method could be used as an alternative to the whiteness index for measuring the degree of milling. Over-milling significantly decreased head rice yield with little improvement in whiteness. The milling quality of M202 can be predicted by using the developed non-linear regression models, when the milling and polishing conditions are known.