Issue 10, 2022

High-accuracy quantitative analysis of coal by small sample modelling algorithm based laser induced breakdown spectroscopy

Abstract

In general, high-accuracy quantitative analysis of material components based on laser induced breakdown spectra requires large sample sizes for accurate modeling. However, in most cases, there are no abundant standard samples. Herein, small sample size (less than 20 standard samples) machine learning based on LIBS is utilized to realize high-accuracy quantitative analysis of coal. The algorithm is implemented through specific data extraction and data resampling. Partial spectra of each sample are extracted to form a batch of training data for establishing a prediction model based on the principal component analysis combined with partial least squares method (PCA-PLS). Repeatedly stochastic data are extracted from the spectra of the samples to form abundant batches of training data sets, and thus the most representative dataset is discovered in the training processes. The model trained by the new algorithm via 10–15 samples has a comparable performance to the model trained by the traditional method via 60–80 samples. With a training sample size of 15, the ARE of carbon prediction is less than 4% in the new prediction model, indicating that the proposed model has better generalization and higher prediction accuracy for small-sample-size machine learning. It will greatly improve the universality of the LIBS technology for fast elemental detection.

Graphical abstract: High-accuracy quantitative analysis of coal by small sample modelling algorithm based laser induced breakdown spectroscopy

Supplementary files

Article information

Article type
Paper
Submitted
13 jun 2022
Accepted
05 aug 2022
First published
11 aug 2022

J. Anal. At. Spectrom., 2022,37, 2022-2032

High-accuracy quantitative analysis of coal by small sample modelling algorithm based laser induced breakdown spectroscopy

A. Li, X. Zhang, X. Wang, Y. He, Y. Yin and R. Liu, J. Anal. At. Spectrom., 2022, 37, 2022 DOI: 10.1039/D2JA00204C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements