PMID- 36917485
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230403
LR  - 20230403
IS  - 1759-9679 (Electronic)
IS  - 1759-9660 (Linking)
VI  - 15
IP  - 13
DP  - 2023 Mar 30
TI  - Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised 
      algorithm based on thickness and mixing proportion-emission matrices.
PG  - 1649-1660
LID - 10.1039/d2ay01776h [doi]
AB  - In recent years, marine oil spill accidents have been occurring frequently during 
      extraction and transportation, and seriously damage the ecological balance. 
      Accurate monitoring of oil spills plays a vital role in estimating oil spill 
      volume, determination of liability, and clean-up. The oil that leaks into natural 
      environments is not a single type of oil, but a mixture of various oil products, 
      and the oil film thickness on the sea surface is uneven under the influence of 
      wind and waves. Increasing the mixed oil film thickness dimension and the mix 
      proportion dimension has been proposed to weaken the effect of the detection 
      environment on the fluorescence measurement results. To preserve the 
      relationships between the data of oil films with different thicknesses and the 
      relationships between the data of oil films with different mixing proportions, 
      the three-dimensional fluorescence spectral data of mixed oil films on a seawater 
      surface were measured in the laboratory, producing a thickness-fluorescence 
      matrix and a proportion-fluorescence matrix. The nonlinear variation of the 
      fluorescence spectra was investigated according to the fluorescence lidar 
      equation. This work pre-processes the data by sum normalization and 
      two-dimensional principal component analysis (2DPCA) and uses the dimensionality 
      reduction results as two feature-point views. Then, semi-supervised 
      classification of collaborative training (co-training) with K-nearest neighbors 
      (KNN) and a decision tree (DT) is used to identify the samples. The results show 
      that the average overall accuracy of this coupling model can reach 100%, which is 
      20.49% higher than that of the thickness-only view. Using unlabeled data can 
      reduce the cost of data acquisition, improve the classification accuracy and 
      generalization ability, and provide theoretical significance and application 
      prospects for discrimination of spectrally similar oil species in natural marine 
      environments.
FAU - Gong, Bowen
AU  - Gong B
AUID- ORCID: 0000-0002-8756-6730
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
AD  - University of Chinese Academy of Sciences, Beijing, 100049, China. 
      @mails.ucas.ac.cn.
FAU - Zhang, Hongji
AU  - Zhang H
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
FAU - Wang, Xiaodong
AU  - Wang X
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
FAU - Lian, Ke
AU  - Lian K
AD  - Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China.
FAU - Li, Xinkai
AU  - Li X
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
FAU - Chen, Bo
AU  - Chen B
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
FAU - Wang, Hanlin
AU  - Wang H
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
AD  - University of Chinese Academy of Sciences, Beijing, 100049, China. 
      @mails.ucas.ac.cn.
FAU - Niu, Xiaoqian
AU  - Niu X
AD  - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of 
      Sciences, Changchun, Jilin Province, 130033, China. ciomp@ciomp.ac.cn.
AD  - University of Chinese Academy of Sciences, Beijing, 100049, China. 
      @mails.ucas.ac.cn.
LA  - eng
PT  - Journal Article
DEP - 20230330
PL  - England
TA  - Anal Methods
JT  - Analytical methods : advancing methods and applications
JID - 101519733
SB  - IM
EDAT- 2023/03/15 06:00
MHDA- 2023/03/15 06:01
CRDT- 2023/03/14 12:43
PHST- 2023/03/15 06:01 [medline]
PHST- 2023/03/15 06:00 [pubmed]
PHST- 2023/03/14 12:43 [entrez]
AID - 10.1039/d2ay01776h [doi]
PST - epublish
SO  - Anal Methods. 2023 Mar 30;15(13):1649-1660. doi: 10.1039/d2ay01776h.