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Classification of fungal-damaged soybean seeds using near-infrared spectroscopy

Wang, D., Dowell, F.E., Ram, M.S., Schapaugh, W.T.
International journal of food properties 2004 v.7 no.1 pp. 75
soybeans, seeds, postharvest diseases, seed-borne fungi, stored product protection, plant pathogenic fungi, crop damage, near-infrared spectroscopy, downy mildew, color, Phomopsis, Cercospora kikuchii, least squares
Fungal damage has a devastating impact on soybean quality and end-use. The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective of this research was to classify healthy and fungal-damaged soybean seeds and discriminate among various types of fungal damage using near-infrared (NIR) spectroscopy. A diode-array NIR spectrometer, which measured reflectance [log(1=R)] from 400 to 1700 nm, was used to obtain spectra from single soybean seeds. Partial least square (PLS) and neural network models were developed to differentiate healthy and fungal damaged seeds. The highest classification accuracy was more than 99% when the wavelength region of 490-1690nm was used under a two-class PLS model. Neural network models yielded higher classification accuracy than the PLS models for five-class classification. The average of correct classifications was 93.5% for the calibration sample set and 94.6% for the validation sample set. Classification accuracies of the validation sample set were 100, 99, 84, 94, and 96% corresponding to healthy seeds, Phomopsis, Cercospora kikuchii, soybean mosaic virus (SMV), and downy mildew damaged seeds, respectively.