PMID- 23235456
OWN - NLM
STAT- MEDLINE
DCOM- 20131231
LR  - 20240109
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 12
IP  - 12
DP  - 2012 Dec 12
TI  - Application of hyperspectral imaging and chemometric calibrations for variety 
      discrimination of maize seeds.
PG  - 17234-46
LID - 10.3390/s121217234 [doi]
AB  - Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used 
      to develop a novel method for discriminating different varieties of commodity 
      maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of 
      maize seeds were acquired using a hyperspectral imaging system in the 380-1,030 
      nm wavelength range. Secondly, principal component analysis (PCA) and kernel 
      principal component analysis (KPCA) were used to explore the internal structure 
      of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) 
      were selected by implementing PCA directly on each image. Then four textural 
      variables including contrast, homogeneity, energy and correlation were extracted 
      from gray level co-occurrence matrix (GLCM) of each monochromatic image based on 
      the optimal wavelengths. Finally, several models for maize seeds identification 
      were established by least squares-support vector machine (LS-SVM) and back 
      propagation neural network (BPNN) using four different combinations of principal 
      components (PCs), kernel principal components (KPCs) and textural features as 
      input variables, respectively. The recognition accuracy achieved in the 
      PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that 
      hyperspectral imaging combined with texture analysis can be implemented for fast 
      classification of different varieties of maize seeds.
FAU - Zhang, Xiaolei
AU  - Zhang X
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, China. xiaoleizhang@zju.edu.cn
FAU - Liu, Fei
AU  - Liu F
FAU - He, Yong
AU  - He Y
FAU - Li, Xiaoli
AU  - Li X
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20121212
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Image Processing, Computer-Assisted
MH  - Light
MH  - Principal Component Analysis
MH  - *Seeds
MH  - Spectroscopy, Near-Infrared
MH  - Support Vector Machine
MH  - *Zea mays
PMC - PMC3571835
EDAT- 2012/12/14 06:00
MHDA- 2014/01/01 06:00
PMCR- 2012/12/01
CRDT- 2012/12/14 06:00
PHST- 2012/10/12 00:00 [received]
PHST- 2012/11/27 00:00 [revised]
PHST- 2012/12/10 00:00 [accepted]
PHST- 2012/12/14 06:00 [entrez]
PHST- 2012/12/14 06:00 [pubmed]
PHST- 2014/01/01 06:00 [medline]
PHST- 2012/12/01 00:00 [pmc-release]
AID - s121217234 [pii]
AID - sensors-12-17234 [pii]
AID - 10.3390/s121217234 [doi]
PST - epublish
SO  - Sensors (Basel). 2012 Dec 12;12(12):17234-46. doi: 10.3390/s121217234.