PMID- 20302123
OWN - NLM
STAT- MEDLINE
DCOM- 20141108
LR  - 20151119
IS  - 1000-0593 (Print)
IS  - 1000-0593 (Linking)
VI  - 30
IP  - 1
DP  - 2010 Jan
TI  - [Application of three-dimensional fluorescence spectra technique to 
      discrimination of distilled spirits based on support vector machines].
PG  - 243-6
AB  - In order to study the identifying and classification method of distilled spirits, 
      about 100 kinds of normative distilled spirits were selected as the laboratory 
      samples and their fluorescence spectra induced by ultraviolet light were measured 
      by Roper-Scientific SP-2558 respectively. Then, using the statistics and plot 
      software Origin 7.5, the authors composed the three-dimensional fluorescence 
      spectra of the distilled spirits. In the meanwhile, the three-dimensional 
      fluorescence spectra of the dis tilled spirits were also studied respectively. 
      The authros compared the three-dimensional fluorescence spectra of different 
      distilled spirits with the same brands and that of different spirits with the 
      same brands. The authors are very confident that there must be some typical 
      parameters, by which the authors can distinguish different kinds of distilled 
      spirits with different brands effectively. And the authors found that while the 
      three-dimensional fluorescence spectra of different distilled spirits with 
      different brands are definitely different by their three typical parameters 
      including the number of the major summit, position of the major summit and 
      optimum wavelength, the three typical parameters of the three-dimensional 
      fluorescence spectra of different distilled spirits with the same brand are very 
      similar. Finally, the authors extracted the three typical parameters that are the 
      number of the major summit, position of the major summit and optimum wavelength. 
      Then, the data were processed by computer for emulation. As a result, the authors 
      found that, using the three characteristic parameters, different kinds of 
      distilled spirits can be classified ac curately by the algorithm of LS-SVM 
      (least-squares support vector machine). The outcomes of the data emulated by 
      different algorithms were obtained seriatim. The authors compared the outcomes, 
      and the fact proved that more accurate outcome of identifying and classification 
      can be obtained by LS-SVM.
FAU - Yang, Jian-lei
AU  - Yang JL
AD  - School of Communication & Control Engineering, Jiangnan University, Wuxi 214122, 
      China. janlyyang@126.com
FAU - Zhu, Tuo
AU  - Zhu T
FAU - Xu, Yan
AU  - Xu Y
FAU - Fan, Wen-lai
AU  - Fan WL
FAU - Wu, Hao
AU  - Wu H
LA  - chi
PT  - English Abstract
PT  - Journal Article
PL  - China
TA  - Guang Pu Xue Yu Guang Pu Fen Xi
JT  - Guang pu xue yu guang pu fen xi = Guang pu
JID - 9424805
SB  - IM
MH  - Alcoholic Beverages/*analysis
MH  - Algorithms
MH  - Least-Squares Analysis
MH  - Spectrometry, Fluorescence
MH  - *Support Vector Machine
EDAT- 2010/03/23 06:00
MHDA- 2014/11/09 06:00
CRDT- 2010/03/23 06:00
PHST- 2010/03/23 06:00 [entrez]
PHST- 2010/03/23 06:00 [pubmed]
PHST- 2014/11/09 06:00 [medline]
PST - ppublish
SO  - Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Jan;30(1):243-6.