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Hyperspectral Reflectance Imaging for Detecting Typical Defects of Durum Kernel Surface
KTH, School of Technology and Health (STH). Hangzhou Dianzi Univ, Coll Life Informat Sci & Instrument Engn, Hangzhou, Zhejiang, Peoples R China..
Xinjiang Med Univ, Dept Econ Management, Canc Affiliated Hosp, Urumqi, Peoples R China..
Hangzhou Dianzi Univ, Coll Life Informat Sci & Instrument Engn, Hangzhou, Zhejiang, Peoples R China..
Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou, Zhejiang, Peoples R China..
2018 (English)In: Intelligent Automation and Soft Computing, ISSN 1079-8587, E-ISSN 2326-005X, Vol. 24, no 2, p. 351-357Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 24, no 2, p. 351-357
Keywords [en]
Foodstuff quality, Hyperspectral imaging, Unsound kernel detection, Effective band selection
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-235150DOI: 10.1080/10798587.2017.1293927ISI: 000443491400014OAI: oai:DiVA.org:kth-235150DiVA, id: diva2:1249363
Funder
Swedish Institute
Note

QC 20180919

Available from: 2018-09-19 Created: 2018-09-19 Last updated: 2018-09-19Bibliographically approved

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