PMID- 26672284
OWN - NLM
STAT- MEDLINE
DCOM- 20151229
LR  - 20151217
IS  - 1000-0593 (Print)
IS  - 1000-0593 (Linking)
VI  - 35
IP  - 8
DP  - 2015 Aug
TI  - [Prediction the Soluble Solid Content in Sugarcanes by Using Near Infrared 
      Hyperspectral Imaging System].
PG  - 2154-8
AB  - In order to explore the feasibility of prediction soluble solid contents (SSC) in 
      sugarcane stalks by using near infrared hyperspectral imaging techniques, two 
      hundred and forty sugarcane stalks which come from three different varieties were 
      studied. After obtaining the raw hyperspectral images of sugarcane stalks, the 
      spectral information and textural features were discussed respectively. The 
      prediction models were established by using partial least squares regression 
      (PLSR), principal components regression (PCR) and least squares support vector 
      machines (LS-SVM) algorithms. Besides, three different selected wavelengths 
      algorithms such as successive projection (SPA) algorithms, intervals partial 
      least squares (iPLS) algorithms and uninformation variables elimination (UVE) 
      algorithm were analyzed after building partial least squares regression model. 
      The results indicate that partial least squares regression model based on 
      spectral features can be an steady model to predict SSC and the correlation 
      coefficient (R2) of calibration sets and prediction sets are 0.879, 0.843. The 
      root mean square errors of calibration sets and prediction sets are 0.644, 0.742 
      respectively. The obtained 105 wavelengths which were selected by UVE algorithm 
      are effective spectral features. The R2 results of calibration sets and 
      prediction sets of its PLSR model are 0.860, 0.813. The root mean square errors 
      of calibration sets and prediction sets are 0.693, 0.810 respectively
FAU - Gao, Jun-feng
AU  - Gao JF
FAU - Zhang, Chu
AU  - Zhang C
FAU - Xie, Chuan-qi
AU  - Xie CQ
FAU - Zhu, Feng-le
AU  - Zhu FL
FAU - Guo, Zhen-hao
AU  - Guo ZH
FAU - He, Yong
AU  - He Y
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  - Algorithms
MH  - Least-Squares Analysis
MH  - Principal Component Analysis
MH  - Saccharum/*chemistry
MH  - Spectroscopy, Near-Infrared
MH  - Support Vector Machine
EDAT- 2015/12/18 06:00
MHDA- 2015/12/30 06:00
CRDT- 2015/12/18 06:00
PHST- 2015/12/18 06:00 [entrez]
PHST- 2015/12/18 06:00 [pubmed]
PHST- 2015/12/30 06:00 [medline]
PST - ppublish
SO  - Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Aug;35(8):2154-8.