Peck caused by stink-bug is an important quality factor for rice grader, marketing, and end-use. The current visual method of separating pecky rice kernels from sound kernels is time consuming, tedious, and subject to error. The objective of this research was to develop an objective method for classifying pecky rice kernels and sound rice kernels using visible and near-infrared (NIR) spectroscopy.


A diode-array NIR spectrometer, which measured absorbance spectra (log(1/R)) from 400 to 1700 nm, was used to differentiate pecky rice kernels and sound rice kernels individually. Partial least squares (PLS) regression models with three wavelength regions (400–750, 400–1700, and 750–1700 nm) and two-wavelength models were developed. Results showed that both PLS models and two-wavelength models can be used to classify pecky rice kernels. For PLS models, the NIR wavelength region of 750–1,700 nm gave the highest percentage of correct classification for both calibration and validation (100%). For two-wavelength models, the model using wavelengths 480 and 590 nm yielded the highest classification accuracies for both calibration and validation sample sets (99.3%). Stink-bug feeding caused microorganism activity resulting in grain discoloration. The color change made the classification of pecky rice and sound rice kernels possible (accuracy>99%) even when the undamaged area faces the illumination light.


Due to the interaction effect, the trough angle needs to be increased appropriately when an increase in cylinder speed is made if a rapid drop of effectiveness of separation should be avoided. In commercial applications, S1 type indents are preferred because of their better manufacturability and easier maintenance. For successful separation of broken kernels, the indent size should be set slightly bigger than the actual sizes of broken kernels: an indent size of 3.0 mm for separating broken kernels shorter than 2.5 mm.


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