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Cited by 42

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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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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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CoChat. (2026). Citation check: Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect. https://app.cochat.ai/papers/10.1039/d1ja00076d
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Shabbir, S., Zhang, Y., Sun, C., Yue, Z., Xu, W., Zou, L., Chen, F., & Yu, J. (2021). Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect. Journal of Analytical Atomic Spectrometry. https://doi.org/10.1039/d1ja00076d

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