Issue 10, 2022

A transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis

Abstract

Laser-induced breakdown spectroscopy (LIBS) combined with machine learning has demonstrated great capabilities for quantitative elemental analysis. When the distributions of training and test data differ due to changes in measurement and sample composition, machine learning models degrade in accuracy and reliability. This, coupled with the small sample problem caused by high cost and long time to certify the analyte content, poses a challenge to the performance of LIBS quantification. This work proposes a transfer learning method to improve limited sample size LIBS quantification performance with extra-spectrum from similar LIBS measurements out of the experimental series. The model inherits convolutional layers from a source neural network model and is trained on a small amount of target training samples. Multitask regularization is introduced to constrain the source model based on prior information of the sample composition. The experiments were designed based on coal datasets with a limited sample size and different analyte concentration ranges. The proposed method reduces RMSEp by 19.9%, 5.9% and 7.7% compared to PLSR, SVR and non-transfer CNN models. The results show that the proposed method can outperform baseline approaches in terms of accuracy and robustness on limited data sets and can take effect on various tasks.

Graphical abstract: A transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis

Article information

Article type
Paper
Submitted
26 Cax 2022
Accepted
19 Leq 2022
First published
20 Leq 2022

J. Anal. At. Spectrom., 2022,37, 2059-2068

A transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis

J. Cui, W. Song, Z. Hou, W. Gu and Z. Wang, J. Anal. At. Spectrom., 2022, 37, 2059 DOI: 10.1039/D2JA00182A

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