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ARS Home » Research » Publications at this Location » Publication #274970
Title: Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

Author
item AGELET, LIDIA - Iowa State University
item Ellis, David
item Duvick, Susan
item GOGGIE, SUSANA - Iowa State University
item HURBURGH, CHARLES - Iowa State University
item Gardner, Candice

Submitted to: Journal of Cereal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2011
Publication Date: 1/1/2012
Citation: Agelet, L.E., Ellis, D.D., Duvick, S.A., Goggie, S., Hurburgh, C.R., Gardner, C.A. 2012. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels. Journal of Cereal Science. 55:160-165.

Interpretive Summary: When corn kernels are harvested there can be mechanical damage. Before going to market the kernels are graded to determine good from bad kernels which is a time consuming and visual inspections are variable. Accuracy could be efficiently improved if it were possible to scan kernels automatically. We tested the use of near red spectroscopy to see if this technology could differentiate between corn kernels and soybean seed which were alive versus heat killed. This technology was best able to tell is seed was completely dead but could not differentiate seed with abnormalities from normal seed. This application would be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated.

Technical Abstract: The current US corn grading system accounts for the portion of damaged kernels, which is measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and frost damage. Four classification algorithms were utilized: Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), k-nearest neighbors (K-NN), and least-squares support vector machines (LS-SVM). The feasibility of NIRS for discriminating normal or viable-germinating corn kernels and soybean seeds from abnormal or dead seeds was also tested. This application could be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated.