PMID- 26817580
OWN - NLM
STAT- MEDLINE
DCOM- 20170106
LR  - 20181113
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 6
DP  - 2016 Jan 28
TI  - Combination of the Manifold Dimensionality Reduction Methods with Least Squares 
      Support vector machines for Classifying the Species of Sorghum Seeds.
PG  - 19917
LID - 10.1038/srep19917 [doi]
LID - 19917
AB  - This study was carried out for rapid and noninvasive determination of the class 
      of sorghum species by using the manifold dimensionality reduction (MDR) method 
      and the nonlinear regression method of least squares support vector machines 
      (LS-SVM) combing with the mid-infrared spectroscopy (MIRS) techniques. The 
      methods of Durbin and Run test of augmented partial residual plot (APaRP) were 
      performed to diagnose the nonlinearity of the raw spectral data. The nonlinear 
      MDR methods of isometric feature mapping (ISOMAP), local linear embedding, 
      laplacian eigenmaps and local tangent space alignment, as well as the linear MDR 
      methods of principle component analysis and metric multidimensional scaling were 
      employed to extract the feature variables. The extracted characteristic variables 
      were utilized as the input of LS-SVM and established the relationship between the 
      spectra and the target attributes. The mean average precision (MAP) scores and 
      prediction accuracy were respectively used to evaluate the performance of models. 
      The prediction results showed that the ISOMAP-LS-SVM model obtained the best 
      classification performance, where the MAP scores and prediction accuracy were 
      0.947 and 92.86%, respectively. It can be concluded that the ISOMAP-LS-SVM model 
      combined with the MIRS technique has the potential of classifying the species of 
      sorghum in a reasonable accuracy.
FAU - Chen, Y M
AU  - Chen YM
AD  - College of Electrical Engineering, Yancheng Institute of Technology, No.1 Middle 
      Road Hope Avenue, Yancheng, Jiangsu Province 224051, P.R. China.
FAU - Lin, P
AU  - Lin P
AD  - College of Electrical Engineering, Yancheng Institute of Technology, No.1 Middle 
      Road Hope Avenue, Yancheng, Jiangsu Province 224051, P.R. China.
FAU - He, J Q
AU  - He JQ
AD  - College of Electrical Engineering, Yancheng Institute of Technology, No.1 Middle 
      Road Hope Avenue, Yancheng, Jiangsu Province 224051, P.R. China.
FAU - He, Y
AU  - He Y
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, China.
FAU - Li, X L
AU  - Li XL
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20160128
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Algorithms
MH  - *Least-Squares Analysis
MH  - Seeds/*classification
MH  - *Sorghum
MH  - *Support Vector Machine
PMC - PMC4730150
EDAT- 2016/01/29 06:00
MHDA- 2017/01/07 06:00
PMCR- 2016/01/28
CRDT- 2016/01/29 06:00
PHST- 2015/07/17 00:00 [received]
PHST- 2015/12/21 00:00 [accepted]
PHST- 2016/01/29 06:00 [entrez]
PHST- 2016/01/29 06:00 [pubmed]
PHST- 2017/01/07 06:00 [medline]
PHST- 2016/01/28 00:00 [pmc-release]
AID - srep19917 [pii]
AID - 10.1038/srep19917 [doi]
PST - epublish
SO  - Sci Rep. 2016 Jan 28;6:19917. doi: 10.1038/srep19917.