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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

Sahar Shabbir, Yuqing Zhang, Chen Sun, Zengqi Yue, Weijie Xu, Long Zou, Fengye Chen, Jin Yu

2021Journal of Analytical Atomic Spectrometry35 citationsDOI

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.

Topics & Concepts

Matrix (chemical analysis)Transfer matrixTransfer (computing)Materials scienceTransfer-matrix method (optics)Surface (topology)Transfer of learningBiological systemComputer scienceAnalytical Chemistry (journal)ChemistryComposite materialArtificial intelligenceMathematicsEnvironmental chemistryOptoelectronicsGeometryBiologyComputer visionParallel computingLaser-induced spectroscopy and plasmaAnalytical chemistry methods developmentIon-surface interactions and analysis
Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect | Litcius