DocumentCode
526578
Title
Notice of Retraction
Growing area determination of Tremella fuciformis using visible and near-infrared spectroscopy
Author
Xiaojing Chen ; Meng Xu ; Xiangou Zhu
Author_Institution
Coll. of Phys. & Electron. Inf. Eng., Wenzhou Univ., Wenzhou, China
Volume
3
fYear
2010
fDate
9-11 July 2010
Firstpage
57
Lastpage
59
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.
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS) which have 675 spectra variables. Finally eleven variables were selected. Effective variables based LS-SVM model obtain 100% determination correct rate. It was proved that SPA was an effective algorithm for spectra variable selection. As a conclusion, Vis-NIR spectroscopy could be used to determine the growing areas of Tremella fuciformis 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.
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS) which have 675 spectra variables. Finally eleven variables were selected. Effective variables based LS-SVM model obtain 100% determination correct rate. It was proved that SPA was an effective algorithm for spectra variable selection. As a conclusion, Vis-NIR spectroscopy could be used to determine the growing areas of Tremella fuciformis fast and accurately.
Keywords
area measurement; bio-optics; biological techniques; biology computing; cellular biophysics; infrared spectra; infrared spectroscopy; least squares approximations; microorganisms; principal component analysis; spectral analysis; support vector machines; visible spectra; visible spectroscopy; LS-SVM model; Tremella fuciformis; least-square support vector machine; near-infrared spectroscopy; principal component analysis; spectra variable selection; successive projection algorithm; visible spectroscopy; Analytical models; Artificial neural networks; Least-square support vector machine; Principal component analysis; Successive projections algorithm; Tremella fuciformis; Visible and near infrared;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564628
Filename
5564628
Link To Document