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Cited by 42

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect

Shabbir, Sahar, Zhang, Yuqing, Sun, Chen, Yue, Zengqi, Xu, Weijie, Zou, Long, Chen, Fengye, Yu, Jin·2021·Journal of Analytical Atomic Spectrometry
DOI 10.1039/d1ja00076d
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Abstract

Transfer learning allows the effective corrections of both chemical and physical matrix effects in such a way that metals with irregular surfaces can be directly analyzed by LIBS for an accurate elemental determination.

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Citations

Cites

Research works printed but not linked: 42

  1. Hahn, 2012, Appl. Spectrosc., 66, 347, 10.1366/11-06574 · DOI
  2. Noll, 2018, J. Anal. At. Spectrom., 33, 945, 10.1039/C8JA00076J · DOI
  3. Legnaioli, 2020, Anal. Methods, 12, 1014, 10.1039/C9AY02728A · DOI
  4. Hahn, 2010, Appl. Spectrosc., 64, 335A, 10.1366/000370210793561691 · DOI
  5. Ciucci, 1999, Appl. Spectrosc., 53, 960, 10.1366/0003702991947612 · DOI
  6. T. Z. Zhao , Z. W.Fan , H.Xiao , Y. H.Lu , et al. , National Standard of the People's Republic of China, Laser-induced breakdown spectroscopy , GB/T 38257-2019, 2019
  7. Aguilera, 1992, Appl. Spectrosc., 46, 1382, 10.1366/0003702924123692 · DOI
  8. Wisbrun, 1994, Anal. Chem., 66, 2964, 10.1021/ac00090a026 · DOI
  9. Eppler, 1996, Appl. Spectrosc., 50, 1175, 10.1366/0003702963905123 · DOI
  10. Charléard, 1997, J. Anal. At. Spectrom., 12, 183, 10.1039/A604456E · DOI
  11. Thompson, 2005, Accredit. Qual. Assur., 10, 82, 10.1007/s00769-004-0871-5 · DOI
  12. El Haddad, 2014, Spectrochim. Acta, Part B, 101, 171, 10.1016/j.sab.2014.08.039 · DOI
  13. Boucher, 2015, Spectrochim. Acta, Part B, 107, 1, 10.1016/j.sab.2015.02.003 · DOI
  14. Sun, 2019, Sci. Rep., 9, 1, 10.1038/s41598-018-37186-2 · DOI · Paper page
  15. Takahashi, 2017, Spectrochim. Acta, Part B, 138, 31, 10.1016/j.sab.2017.09.010 · DOI
  16. Gilon, 2011, Anal. Bioanal. Chem., 401, 2681, 10.1007/s00216-011-5079-z · DOI
  17. Vrenegor, 2005, Spectrochim. Acta, Part B, 60, 1083, 10.1016/j.sab.2005.05.027 · DOI
  18. Zhang, 2020, Spectrochim. Acta, Part B, 166, 105802, 10.1016/j.sab.2020.105802 · DOI
  19. Zaytsev, 2018, Spectrochim. Acta, Part B, 140, 65, 10.1016/j.sab.2017.12.005 · DOI
  20. Lepore, 2017, Appl. Spectrosc., 71, 600, 10.1177/0003702816685095 · DOI
  21. Thornton, 2015, Deep Sea Res., Part I, 95, 20, 10.1016/j.dsr.2014.10.006 · DOI
  22. Meslin, 2013, Science, 341, 1238670, 10.1126/science.1238670 · DOI · Paper page
  23. Xu, 2020, J. Anal. At. Spectrom., 35, 1641, 10.1039/D0JA00157K · DOI
  24. Werheit, 2011, J. Anal. At. Spectrom., 26, 2166, 10.1039/c1ja10096c · DOI
  25. Gurell, 2012, Spectrochim. Acta, Part B, 74–75, 46, 10.1016/j.sab.2012.06.013 · DOI
  26. Kuzuya, 2019, Sci. J. Anal. Chem., 7, 65, 10.11648/j.sjac.20190703.11 · DOI
  27. Kim, 2020, Chemom. Intell. Lab. Syst., 203, 104072, 10.1016/j.chemolab.2020.104072 · DOI
  28. Legnaioli, 2012, Spectrochim. Acta, Part B, 71–72, 123, 10.1016/j.sab.2012.05.004 · DOI
  29. Scrap specifications circular , ISRI , 2020 , http://www.scrap2.org/specs/1/
  30. X. Z. Zhang , Y. B.Huang , J. W.Wang , L. X.Ge , H.Xu , Z. J.Wang , C. S.Huang , G. L.Ye and X. R.Li , National Standard of the People's Republic of China, Scraps of aluminum and aluminum alloys , GB/T 13586-2006, 2006
  31. C. Sun , W.Xu , Y.Tan , Y.Zhang , Z.Yue , S.Shabbir , M.Wu , L.Zou , F.Chen and J.Yu , 2020 , arXiv:2102.03768 [physics.app-ph] · arXiv
  32. W. Dai , G. R.Xue , Q.Yang , and Y.Yu , Proc. 13th ACM SIGKDD Int. Conf. on Knowl. Discov. Data mining , 2007 , 10.1145/1281192.1281218 · DOI
  33. T. F. Boucher , C.Carey , S.Mahadevan , and M. D.Dyar , AAAI Conference Proceedings , 2015 , paper 1951, pp. 2511–2517
  34. Chang, 2020, J. Anal. At. Spectrom., 35, 2639, 10.1039/D0JA00334D · DOI
  35. Zhang, 2020, Spectrochim. Acta, Part B, 166, 105802, 10.1016/j.sab.2020.105802 · DOI
  36. Barnett, 1968, Spectrochim. Acta, Part B, 23, 643, 10.1016/0584-8547(68)80045-X · DOI
  37. Yue, 2020, Opt. Express, 28, 14345, 10.1364/OE.392176 · DOI
  38. Zhang, 2020, Opt. Express, 28, 32019, 10.1364/OE.404722 · DOI
  39. Zou, 2021, J. Anal. At. Spectrom., 35, 1641
  40. Zhang, 2009, J. Raman Spectrosc., 41, 659, 10.1002/jrs.2500 · DOI
  41. T. H. Cormen , C. E.Leiserson , R. L.Rivest and C.Stein , Introduction to Algorithms , MIT Press and McGraw-Hill , 2nd edn, 2001
  42. P. Bruce and A.Bruce , Practical Statistics for Data Scientists , O'Reilly Media, Inc. , 2017

Metadata

Authors
Shabbir, S., Zhang, Y., Sun, C., Yue, Z., Xu, W., Zou, L., Chen, F., & Yu, J.
Venue
Journal of Analytical Atomic Spectrometry
Publisher
Royal Society of Chemistry (RSC)
Published
2021
Volume
36
Issue
7
Pages
1441-1454
ISSN
  • 0267-9477
  • 1364-5544
DOI
10.1039/d1ja00076d
Cited by
42
References
1
Language
en
Licence
rsc.li/journals-terms-of-use (am, 495 d)
Funders
  • National Natural Science Foundation of China (11574209, 11805126, 61975190)
  • Shanghai Jiao Tong University

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.