Review of In-Situ Online LIBS Detection in the Atmospheric Environment

Zhang, Qihang; Liu, Yuzhu

doi:10.46770/as.2021.609

At.Spectrosc.

2021

DOI: 10.46770/as.2021.609

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Review of In-Situ Online LIBS Detection in the Atmospheric Environment

Qihang Zhang¹,

Yuzhu Liu²

Abstract: The aim of this review is to provide a brief introduction to recent research advances in in-situ online detection of atmospheric pollutants based on laser-induced breakdown spectroscopy (LIBS) under atmospheric environments. Atmospheric pollution has drawn much public attention, and there is increasing demand for rapid and accurate evaluation of atmospheric environments. LIBS has the advantages of in-situ online detection, simultaneous multi-element analysis, and noncontact measurement, making it a highly comp… Show more

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Cited by 2 publications

References 126 publications

(141 reference statements)

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Rare earth metals detection and recognition based on laser induced breakdown spectroscopy and machine learning

et al. 2023

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The rapid detection and identification of the electronic waste (e-waste) containing rare earth (RE) elements is of great significance for the recycling of RE elements. However, the analysis of these materials is extremely challenging due to extreme similarities in appearance or chemical composition. In this research, a new system based on laser induced breakdown spectroscopy (LIBS) and machine learning algorithms is developed for identifying and classifying e-waste of rare-earth phosphors (REPs). Three different kinds of phosphors are selected and the spectra is monitored using this new developed system. The analysis of phosphor spectra shows that there are Gd, Yd, and Y RE element spectra in the phosphor. The results also verify that LIBS could be used to detect RE elements. An unsupervised learning method, principal component analysis (PCA), is used to distinguish the three phosphors and training data set is stored for further identification. Additionally, a supervised learning method, backpropagation artificial neural network (BP-ANN) algorithm is used to establish a neural network model to identify phosphors. The result show that the final phosphor recognition rate reaches 99.9%. The innovative system based on LIBS and machine learning (ML) has the potential to improve rapid in situ detection of RE elements for the classification of e-waste.

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Rare earth metals detection and recognition based on laser induced breakdown spectroscopy and machine learning

et al. 2023

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Small-sample stacking model for qualitative analysis of aluminum alloys based on femtosecond laser-induced breakdown spectroscopy

Ma¹,

Liu²,

Sun³

et al. 2023

Opt. Express

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Material characterization using laser-induced breakdown spectroscopy (LIBS) often relies on extensive data for effective analysis. However, data acquisition can be challenging, and the high dimensionality of raw spectral data combined with a large-scale sample dataset can strain computational resources. In this study, we propose a small sample size stacking model based on femtosecond LIBS to achieve accurate qualitative analysis of aluminum alloys. The proposed three-layer stacking algorithm performs data reconstruction and feature extraction to enhance the analysis. In the first layer, random forest spectral feature selection and specific spectral line spreading are employed to reconstruct the data. The second layer utilizes three heterogeneous classifiers to extract features from the reconstructed spectra in different feature spaces, generating second-level reconstructed data. Finally, the third layer utilizes the reconstructed dataset for qualitative prediction. Results indicate that the Stacking algorithm outperforms traditional methods such as k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), including those combined with principal component analysis (PCA). The Stacking algorithm achieves an impressive 100% recognition rate in classification, with Accuracy, precision, recall, and F1 scores reaching 1.0. Moreover, as the number of samples decreases, the gap between the recognition accuracy of the Stacking algorithm and traditional approaches widens. For instance, using only 15 spectra for training, the Stacking algorithm achieves a recognition accuracy of 96.47%, significantly surpassing the improved RF's accuracy of 71.76%. Notably, the model demonstrates strong robustness compared to traditional modeling approaches, and the qualitative prediction error remains consistently below 5%. These findings underscore the model's enhanced generalization ability and higher prediction accuracy in small sample machine learning. This research contributes significantly to improving the applicability of the LIBS technique for fast detection and analysis of small samples. It provides valuable insights into the development of effective methodologies for material characterization, paving the way for advancements in the field.

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Review of In-Situ Online LIBS Detection in the Atmospheric Environment

Cited by 2 publications

References 126 publications

Rare earth metals detection and recognition based on laser induced breakdown spectroscopy and machine learning

Rare earth metals detection and recognition based on laser induced breakdown spectroscopy and machine learning

Small-sample stacking model for qualitative analysis of aluminum alloys based on femtosecond laser-induced breakdown spectroscopy

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