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

Williams, Paul, Geladi, Paul, Fox, Glen and Manley, Marena (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta, 653 2: 121-130. doi:10.1016/j.aca.2009.09.005


Author Williams, Paul
Geladi, Paul
Fox, Glen
Manley, Marena
Title Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis
Journal name Analytica Chimica Acta   Check publisher's open access policy
ISSN 0003-2670
Publication date 2009-10-27
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.aca.2009.09.005
Open Access Status Not yet assessed
Volume 653
Issue 2
Start page 121
End page 130
Total pages 10
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Formatted abstract
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.
Keyword Near infrared hyperspectral imaging
Hyperspectral image analysis
Maize hardness
Principal component analysis
Partial least squares discriminant analysis
Principal component analysis
Corn Hardness
Prediction
Calibration
Spectroscopy
Quality
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID UID 60958
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Agriculture and Food Sciences
 
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Created: Tue, 08 Mar 2011, 01:06:56 EST