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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.