DocumentCode :
2161809
Title :
Independent Component Analysis and Support Vector Machine combined for Brands Identification of Milk Powder Based on Visible and Short-Wave Near-Infrared Spectroscopy
Author :
Wu, Di ; Feng, Shuijuan ; Chen, Xiaojing ; Yang, Haiqing ; He, Yong
Volume :
5
fYear :
2008
fDate :
27-30 May 2008
Firstpage :
456
Lastpage :
459
Abstract :
The aim of this paper is to investigate the potential of Visible and short-wave near-infrared spectroscopy (Vis/SWNIR) technique used for brand discrimination of milk powder. Fifty samples for each brand were studied. Based on the independent components (ICs) as the input variable obtained from fast fixed-point independent component analysis (FastICA), Least-squares Support Vector Machine (LS-SVM) was applied to building the prediction model. The discrimination rate of LS-SVM model which was established based on FastICA was reached at 100 %. LS-SVM model and partial least squares model, which were both established based on the whole measurement region, were also established. The identification results of these two models are worse than LS-SVM which was established based on ICs. It is concluded that Vis/ SWNIR technique is available for the brand identification of milk powder fast and non-destructively.
Keywords :
Biomedical measurements; Chemical analysis; Dairy products; Independent component analysis; Least squares methods; Powders; Reflectivity; Spectroradiometers; Spectroscopy; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2008. CISP '08. Congress on
Conference_Location :
Sanya, China
Print_ISBN :
978-0-7695-3119-9
Type :
conf
DOI :
10.1109/CISP.2008.585
Filename :
4566869
Link To Document :
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