Yu Ding , Qiang Tan , Jianan Xu , Ao Hu , Meiling Zhao , Xiangchu Li , Yan Shu , Xinxin Liu
{"title":"Spectral calibration for atmospheric particles analysis under non-precise focusing conditions using LIBS combined with transfer learning","authors":"Yu Ding , Qiang Tan , Jianan Xu , Ao Hu , Meiling Zhao , Xiangchu Li , Yan Shu , Xinxin Liu","doi":"10.1016/j.sab.2025.107171","DOIUrl":null,"url":null,"abstract":"<div><div>Elemental analysis of atmospheric particulate matter is crucial for air-pollution research. However, the dispersed nature of these particles can lead to variations in the laser-focusing positions, which cause fluctuations in the laser-induced breakdown spectroscopy spectral data. In this study, a transfer-learning approach called transfer component analysis (TCA) is introduced to reduce the impact of spectral-data fluctuations on quantitative-analysis model predictions. First, dictionary learning combined with logistic regression is used to select the effective spectral data for which the laser successfully interacts with the particles. Second, TCA is used to migrate the data of pellet and dispersed atmospheric particles. Using partial least squares regression (PLSR), a TCA-PLSR model is established. The results indicate that the TCA-PLSR model significantly enhances the prediction performance, with test set coefficient of determination (R<sub>P</sub><sup>2</sup>), root mean square error (RMSE<sub>P</sub>), and mean relative error (MRE<sub>P</sub>) values of 0.9869, 60.33, and 0.1005, respectively. Compared with the PLSR model, R<sub>P</sub><sup>2</sup> improved by 55.05 %, RMSE<sub>P</sub> decreased by 81.09 %, and MRE<sub>P</sub> decreased by 82.68 %. This method effectively detects metal concentrations in atmospheric particles and offers a scientific basis for air-pollution monitoring.</div></div>","PeriodicalId":21890,"journal":{"name":"Spectrochimica Acta Part B: Atomic Spectroscopy","volume":"228 ","pages":"Article 107171"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part B: Atomic Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0584854725000564","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
引用次数: 0
Abstract
Elemental analysis of atmospheric particulate matter is crucial for air-pollution research. However, the dispersed nature of these particles can lead to variations in the laser-focusing positions, which cause fluctuations in the laser-induced breakdown spectroscopy spectral data. In this study, a transfer-learning approach called transfer component analysis (TCA) is introduced to reduce the impact of spectral-data fluctuations on quantitative-analysis model predictions. First, dictionary learning combined with logistic regression is used to select the effective spectral data for which the laser successfully interacts with the particles. Second, TCA is used to migrate the data of pellet and dispersed atmospheric particles. Using partial least squares regression (PLSR), a TCA-PLSR model is established. The results indicate that the TCA-PLSR model significantly enhances the prediction performance, with test set coefficient of determination (RP2), root mean square error (RMSEP), and mean relative error (MREP) values of 0.9869, 60.33, and 0.1005, respectively. Compared with the PLSR model, RP2 improved by 55.05 %, RMSEP decreased by 81.09 %, and MREP decreased by 82.68 %. This method effectively detects metal concentrations in atmospheric particles and offers a scientific basis for air-pollution monitoring.
期刊介绍:
Spectrochimica Acta Part B: Atomic Spectroscopy, is intended for the rapid publication of both original work and reviews in the following fields:
Atomic Emission (AES), Atomic Absorption (AAS) and Atomic Fluorescence (AFS) spectroscopy;
Mass Spectrometry (MS) for inorganic analysis covering Spark Source (SS-MS), Inductively Coupled Plasma (ICP-MS), Glow Discharge (GD-MS), and Secondary Ion Mass Spectrometry (SIMS).
Laser induced atomic spectroscopy for inorganic analysis, including non-linear optical laser spectroscopy, covering Laser Enhanced Ionization (LEI), Laser Induced Fluorescence (LIF), Resonance Ionization Spectroscopy (RIS) and Resonance Ionization Mass Spectrometry (RIMS); Laser Induced Breakdown Spectroscopy (LIBS); Cavity Ringdown Spectroscopy (CRDS), Laser Ablation Inductively Coupled Plasma Atomic Emission Spectroscopy (LA-ICP-AES) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS).
X-ray spectrometry, X-ray Optics and Microanalysis, including X-ray fluorescence spectrometry (XRF) and related techniques, in particular Total-reflection X-ray Fluorescence Spectrometry (TXRF), and Synchrotron Radiation-excited Total reflection XRF (SR-TXRF).
Manuscripts dealing with (i) fundamentals, (ii) methodology development, (iii)instrumentation, and (iv) applications, can be submitted for publication.