|LIM, JONGGUK - National Institute For Agricultural Engineering - Korea
|KIM, GIYOUNG - Us Forest Service (FS)
|MO, CHANGYEUN - Korean Rural Development Administration
|OH, KYOUNGMIN - Korean Rural Development Administration
|YOO, HYEONCHAE - National Institute For Agricultural Science & Technology
|HAM, HYEONHEUI - Korean Rural Development Administration
Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/26/2017
Publication Date: 9/30/2017
Citation: Lim, J., Kim, G., Mo, C., Oh, K., Yoo, H., Ham, H., Kim, M.S. 2017. Classification of Fusarium-infected Korean husked barley using near-infrared reflectance spectroscopy and partial least squares discriminant analysis. Sensors. 17(10):2258. https://doi.org/10.3390/s17102258.
Interpretive Summary: Fungal infection in the heads of cereal crops has been known to decrease the production yields and degrade product quality, resulting in enormous economic losses to farmers. In this study, a near-infrared spectroscopy method was investigated as a nondestructive and rapid method for differentiating between Fusarium-infected husked barley kernels from non-infected husked barley kernels. The resultant analysis, based on a numerical model developed using the spectroscopic data acquired from individual kernels of the barley grain, showed that normal and fungal-infected husked barley could be differentiated with 100% accuracy. These findings benefit crop producers and processors by providing a rapid and accurate means to evaluate fungal-infected grains.
Technical Abstract: The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy to nondestructively and rapidly discriminate Fusarium-infected husked barley. Both normal husked barley kernels and Fusarium-infected husked barley kernels were scanned using a NIR reflectance spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from scanning the non-creased side of the barley kernels achieved 100% accuracy in discriminating the normal and the Fusarium-infected barley. These results demonstrated the feasibility of combining multivariate analysis with NIR spectroscopic technique for use as a rapid and non-destructive detection method to discriminate between normal and Fusarium-infected husked barley.