PMID- 25881418
OWN - NLM
STAT- MEDLINE
DCOM- 20150817
LR  - 20181202
IS  - 1000-0593 (Print)
IS  - 1000-0593 (Linking)
VI  - 34
IP  - 12
DP  - 2014 Dec
TI  - [Huanghua pear soluble solids contents Vis/NIR spectroscopy by analysis of 
      variables optimization and FICA].
PG  - 3253-6
AB  - The purpose of this study was to establish a mathematical model of the 
      visible/near-infrared (Vis/NIR) diffuse transmission spectroscopy with fine 
      stability and precise predictability for the non destructive testing of the 
      soluble solids content of huanghua pear, through comparing the effects of various 
      pretreatment methods, variable optimization method, fast independent principal 
      component analysis (FICA) and least squares support vector machines (LS-SVM) on 
      mathematica model for SSC of huanghua pear, and the best combination of methods 
      to establish model for SSC of huanghua pear was got. Vis/NIR diffuse transmission 
      spectra of huanghua pear were acquired by a Quality Spec spectrometer, three 
      methods including genetic algorithm, successive projections algorithm and 
      competitive adaptive reweighted sampling (CARS) were used firstly to select 
      characteristic variables from spectral data of huanghua pears in the wavelength 
      range of 550~950 nm, and then FICA was used to extract factors from the 
      characteristic variables, finally, validation model for SSC in huanghua pears was 
      built by LS-SVM on the basic of those parameters got above. The results showed 
      that using LS-SVM on the foundation of the 21 variables screened by CARS and the 
      12 factors selected by FICA, the CARS-FICA-LS-SVM regression model for SSC in 
      huanghua pears was built and performed best, the coefficient of determination and 
      root mean square error of calibration and prediction sets were RC(2)=0.974, 
      RMSEC=0.116%, RP(2)=0.918, and RMSEP=0.158% respectively, and compared with the 
      mathematical model which uses PLS as modeling method, the number of variables was 
      down from 401 to 21, the factors were also down from 14 to 12, the coefficient of 
      determination of modeling and prediction sets were up to 0.023 and 0.019 
      respectively, while the root mean square errors of calibration and prediction 
      sets were reduced by 0.042% and 0.010% respectively. These experimental results 
      showed that using CARS-FICA-LS-SVM to build regression model for the forecast of 
      SSC in huanghua pears can simplify the prediction model and improve the detection 
      precision.
FAU - Xu, Wen-li
AU  - Xu WL
FAU - Sun, Tong
AU  - Sun T
FAU - Hu, Tian
AU  - Hu T
FAU - Hu, Tao
AU  - Hu T
FAU - Liu, Mu-hua
AU  - Liu MH
LA  - chi
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  - Algorithms
MH  - Calibration
MH  - Least-Squares Analysis
MH  - Models, Theoretical
MH  - Principal Component Analysis
MH  - Pyrus/*chemistry
MH  - *Spectroscopy, Near-Infrared
MH  - Support Vector Machine
EDAT- 2015/04/18 06:00
MHDA- 2015/08/19 06:00
CRDT- 2015/04/18 06:00
PHST- 2015/04/18 06:00 [entrez]
PHST- 2015/04/18 06:00 [pubmed]
PHST- 2015/08/19 06:00 [medline]
PST - ppublish
SO  - Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Dec;34(12):3253-6.