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Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

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Williams, P., Geladi, P., Fox, G.P. and Manley, M. (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta, 653 (2). pp. 121-130.

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Article Link: http://dx.doi.org/10.1016/j.aca.2009.09.005

Publisher URL: http://www.elsevier.com


The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Item Type:Article
Business groups:Crop and Food Science
Additional Information:© Elsevier B.V.
Keywords:Near infrared hyperspectral imaging; hyperspectral image analysis; maize hardness; principal component analysis; partial least squares discriminant analysis; corn hardness; spectroscopy.
Subjects:Technology > Technology (General) > Spectroscopy > NIR (Near Infrared)
Plant culture > Field crops > Corn. Maize
Live Archive:02 Mar 2010 07:10
Last Modified:07 Apr 2022 04:39

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