DocumentCode
3584130
Title
Notice of Retraction
Combination of ^1H NMR and Chemometrics for Discrimination of Rapeseed Oils
Author
Xiaojing Chen ; Xiangou Zhu ; Xinxiang Lei
Author_Institution
Coll. of Phys. & Electron. Inf., Wenzhou Univ., Wenzhou, China
Volume
2
fYear
2010
Firstpage
114
Lastpage
117
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
1H NMR spectroscopy was utilized to distinguish the brands of rapeseed oils. As there are more than four hundreds of NMR variables, uninformative variables should be eliminated to improve model´s discrimination ability and save the calculation time. A hybrid variable selection which is combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA), was used to achieve this objective. 77 effective variables were selected from the full-spectrum variables. They were inputted into least-square support vector machine (LS-SVM) to establish the discrimination model. A good result of 92.5% correct answer rate was obtained. It is improved compared to the result of the full spectrum- SPA-LS-SVM model. It is proved that it is necessary to do UVE before SPA. As a conclusion, the performance of UVE-SPALS-SVM model shows that 1H NMR spectroscopy is a feasible way to distinguish rapeseed oils fast and accurately.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
1H NMR spectroscopy was utilized to distinguish the brands of rapeseed oils. As there are more than four hundreds of NMR variables, uninformative variables should be eliminated to improve model´s discrimination ability and save the calculation time. A hybrid variable selection which is combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA), was used to achieve this objective. 77 effective variables were selected from the full-spectrum variables. They were inputted into least-square support vector machine (LS-SVM) to establish the discrimination model. A good result of 92.5% correct answer rate was obtained. It is improved compared to the result of the full spectrum- SPA-LS-SVM model. It is proved that it is necessary to do UVE before SPA. As a conclusion, the performance of UVE-SPALS-SVM model shows that 1H NMR spectroscopy is a feasible way to distinguish rapeseed oils fast and accurately.
Keywords
NMR spectroscopy; least squares approximations; medical computing; support vector machines; H NMR spectroscopy; LS-SVM; NMR variables; chemometrics; hybrid variable selection; least-square support vector machine; rapeseed oils; successive projections algorithm; uninformation variable elimination; Chemical analysis; Computer science education; Crops; Magnetic analysis; Nuclear magnetic resonance; Oils; Petroleum; Physics education; Spectroscopy; Support vector machines; NMR; least-square support vector machine; successive projections algorithm; uninformation variable elimination;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.587
Filename
5459725
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