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Shabbir, Sahar, Zhang, Yuqing, Sun, Chen, Yue, Zengqi, Xu, Weijie, Zou, Long, Chen, Fengye, Yu, Jin (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, 36 (7) 1441-1454 doi:10.1039/d1ja00076d

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Reference TypeJournal (article/letter/editorial)
TitleTransfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect
JournalJournal of Analytical Atomic Spectrometry
AuthorsShabbir, SaharAuthor
Zhang, YuqingAuthor
Sun, ChenAuthor
Yue, ZengqiAuthor
Xu, WeijieAuthor
Zou, LongAuthor
Chen, FengyeAuthor
Yu, JinAuthor
Year2021Volume36
Issue7
PublisherRoyal Society of Chemistry (RSC)
DOIdoi:10.1039/d1ja00076dSearch in ResearchGate
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Mindat Ref. ID13099265Long-form Identifiermindat:1:5:13099265:1
GUID0
Full ReferenceShabbir, Sahar, Zhang, Yuqing, Sun, Chen, Yue, Zengqi, Xu, Weijie, Zou, Long, Chen, Fengye, Yu, Jin (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, 36 (7) 1441-1454 doi:10.1039/d1ja00076d
Plain TextShabbir, Sahar, Zhang, Yuqing, Sun, Chen, Yue, Zengqi, Xu, Weijie, Zou, Long, Chen, Fengye, Yu, Jin (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, 36 (7) 1441-1454 doi:10.1039/d1ja00076d
In(2021) Journal of Analytical Atomic Spectrometry Vol. 36 (7) Royal Society of Chemistry (RSC)

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