Monitoring milling quality of rice by image analysis

Abstract

Rough rice is milled to produce polished edible grain by first subjecting to dehusking or removal of hulls and then to the removal of brownish outer bran layer known as whitening. The control of whiteness (degree of milling) and percentage of broken kernels in milled rice is required to minimize the economic loss to the millers. Digital image analysis was used to determine the head rice yield (HRY), representing the proportion by weight of milled kernels with three quarters or more of their original length, and the whiteness of milled rice. Ten varieties of Thai rice were subjected to varying degrees of milling by adjusting the test duration from 0.5 to 2.5 min. Three-dimensional features (namely, length, perimeter and projected area) were extracted from the images of individual kernels in a milled sample and used to compute a characteristic dimension ratio (CDR) defined as the ratio of the sum of a particular dimensional feature of all head rice kernels to that of all kernels comprising head and broken rice in the sample. HRY and CDR were found to be related by power functions based on the above-mentioned dimensional features, with R 2 more than 0.99 in all cases. The CDR based on the projected area of kernels in their natural rest position provided the best estimate of the HRY with the lowest root mean square error of 1.1% among all dimensional features studied. In case of the whiteness of milled samples, the values provided by a commercial whiteness meter and the mean of gray level distribution determined by image analysis correlated with an R 2 value of 0.99. The results of this study showed that two-dimensional imaging of milled rice kernels could be used for making quantitative assessment of HRY and degree of milling for on-line monitoring and better control of the rice milling operation.