Determining pecky rice kernels using visible and near-infrared spectroscopy

Abstract

Peck caused by stink-bug is an important quality factor for rice grading, 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.