@Article{10798587.2017.1293927,
AUTHOR = {Feng-Nong Chen, Pu-Lan Chen, Kai Fan, Fang Cheng},
TITLE = {Hyperspectral Reflectance Imaging for Detecting Typical Defects of Durum Kernel  Surface},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {351--358},
URL = {http://www.techscience.com/iasc/v24n2/39761},
ISSN = {2326-005X},
ABSTRACT = {In recent years, foodstuff quality has triggered tremendous interest and attention in our society as 
a series of food safety problems. The hyperspectral imaging techniques have been widely applied 
for foodstuff quality. In this study, we were undertaken to explore the possibility of unsound kernel 
detecting (Triticum durum Desf), which were defined as black germ kernels, moldy kernels and 
broken kernels, by selecting the best band in hyperspectral imaging system. The system possessed 
a wavelength in the range of 400 to 1,000  nm with neighboring bands 2.73  nm apart, acquiring 
images of bulk wheat samples from different wheat varieties. A series of technologies of hyperspectral 
imaging processing and spectral analysis were used to separate unsound kernels from sound kernels, 
including the Principal Component Analysis (PCA), the band ratio, the band difference and the best 
band. According to the selected bands, the best accuracy was 95.6, 96.7 and 98.5% for 710 black germ 
kernels, 627 break kernels and 1,169 healthy kernels，respectively. The result shows that the method 
based on the band selection was feasible.<br/>
<b>Abbreviations:</b> CCD: Charge-coupled Device; PC: Personal Computer; PCA: Principal Component 
Analysis; PLSDA: Partial Least Lquares Discriminant Analysis; ANN: Artificial Neural Networks; SVM: 
Support Vector Machine},
DOI = {10.1080/10798587.2017.1293927}
}