Abstract
Rice is typically consumed after milling, a process of removing the husk and bran layers on rice surface. The degree of bran residue remaining on milled rice directly affects the rice quality. This work proposed to detect the bran residue on a single rice grain using fluorescence fingerprint-derived imaging nondestructively. In the experiment, combinations of fluorescence excitation and emission wavelengths that could effectively distinguish the bran and endosperm pixels were identified through fluorescence fingerprint (FF) spectroscopy. Fluorescence images of milled rice samples at these wavelengths were acquired. A support vector machine classifier was then developed to predict the bran residue on rice grains using the FF-derived images as the inputs. It was demonstrated that the proposed method could observe the distribution of bran residue and could predict the percentage of bran residue on milled rice grains with an error of 3.54%.
