@Article{10798587.2017.1293927,
AUTHOR = {Feng-Nong Chen, Pu-Ln Chen, Ki Fn, Fng 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.
Abbreviations: 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}
}