Submitted to: International Journal of Food Properties
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/23/2001
Publication Date: 9/1/2002
Citation: Wang, D., Dowell, F.E., Pasikatan, M.C., Maghirang, E., Lian, Y. 2002. Determining pecky rice kernels using visible and near-infrared spectroscopy. International Journal of Food Properties. 5(3): 629-639. Interpretive Summary: Peck in rice results when stink-bugs feed on kernels and is an important quality factors for rice grading. Pecky grains are discolored and shriveled, and have poor milling quality. We determine that NIR spectroscopy could be used to accurately detect pecky rice. This objective technology may provide the grain industry with an objective means of determining this important rice quality factor.
Technical Abstract: Peck, caused primarily by stink-bug, is one of the most important quality factor for rice grading, market, and end-use. The current visual method of determining pecky rice kernels from sound kernels is time consuming, tedious, and subject to large errors. The objective of this research was to develop an objective method for classifying pecky rice kernels from sound rice kernels using near-infrared (NIR) spectroscopy. A diode-array NIR spectrometer, which measured reflectance spectra (Log(1/R)) from 400 to 1,700 nm, was used to differentiate single pecky rice kernels and sound rice kernels. Partial least squares (PLS) regression models with three-wavelength regions (400-750 nm, 400-1,700 nm, and 750-1,700 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 cross-validation and prediction (100%). Stink-bug feeding causes microorganism activity resulting in grain discoloration. This makes possible the classification of pecky rice and sound rice kernels possible (accuracy >88%) even when the undamaged area faces the illumination light. For two-wavelenths models, the model using wavelengths 480 nm and 590 nm yielded the highest classification accuracies for both calibration and testing sample sets 99.3%).