DocumentCode
2848791
Title
Identification for Orange Quality with FTIR-CWT-SVM
Author
Zhang, Changjiang ; Yang, Bo ; Cheng, Cungui
Author_Institution
Coll. of Math., Phys. & Inf. Eng., Zhejiang Normal Univ., Jinhua, China
Volume
1
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
506
Lastpage
510
Abstract
Horizontal attenuation total reflection-Fourier transform infrared spectroscopy (HATR-FTIR) is used to measure the FTIR of coat of good orange and rotten orange. Continuous wavelet transform (CWT) is used to decompose the FTIR of theirs. Three main scales are selected as the feature extracting space in the CWT domain. Three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to support vector machine (SVM) to train so as to accurately classify the good orange and rotten orange. 80 couples of FTIR are used to train and test the proposed method, where 60 couples of data are used as training samples and 20 couples of data are used as testing samples. Experimental results show that the accurate recognition rate between good orange and rotten orange is respectively 98.8% and 97.6% by using the proposed method.
Keywords
Fourier transform spectroscopy; agricultural products; feature extraction; identification technology; inspection; support vector machines; FTIR-CWT-SVM; HATR-FTIR; continuous wavelet transform; feature extraction; horizontal attenuation total reflection-fourier transform infrared spectroscopy; orange quality identification; support vector machine; Chemistry; Continuous wavelet transforms; Discrete wavelet transforms; Educational institutions; Feature extraction; Fourier transforms; Infrared spectra; Support vector machine classification; Support vector machines; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location
Tianjin
Print_ISBN
978-0-7695-3736-8
Type
conf
DOI
10.1109/ICNC.2009.723
Filename
5365246
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