Classification of Fungal-Damaged Soybean Seeds Using Near-Infrared Spectroscopy

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 fungaldamaged seeds. The highest classification accuracy was more than 99% when the wavelength region of 490–1690 nm 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 Contribution No. 03-163-J from the Kansas Agricultural Experiment Station. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that also may be suitable. *Correspondence: D. Wang, Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA; Fax: 785-532-5825; E-mail: [email protected]. INTERNATIONAL JOURNAL OF FOOD PROPERTIES Vol. 7, No. 1, pp. 75–82, 2004 DOI: 10.1081=JFP-120022981 1094-2912 (Print); 1532-2386 (Online) Copyright # 2004 by Marcel Dekker, Inc. www.dekker.com 75 D o w n l o a d e d B y : [ U S D A N a t l A g r i c u l t u l L i b ] A t : 1 9 : 5 9 4 F e b r u a r y 2 0 0 9