深度学习方法在LIBS成像数据中的应用

IF 3.8 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part B: Atomic Spectroscopy Pub Date : 2025-08-26 DOI:10.1016/j.sab.2025.107315

Javier E. Rinaldi, Andrés Lucía, Carlos Ararat-Ibarguen, C.A. Rinaldi

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引用次数: 0

摘要

本研究探讨了卷积神经网络（cnn）在分析LIBS图像中的应用，用于钢、锆和铝等材料的元素识别。采用ResNet-50架构和基于熵的预处理来增强特征提取。结果表明，cnn可以有效地提取复杂特征，并作为传统光谱仪器的一种经济有效的替代方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Application of deep learning methods to LIBS imaging data

查看原文本刊更多论文

Application of deep learning methods to LIBS imaging data

This study explores the application of Convolutional Neural Networks (CNNs) to analyze LIBS images for elemental identification in materials such as steel, zirconium, and aluminum. A ResNet-50 architecture was implemented alongside entropy-based preprocessing to enhance feature extraction. The results demonstrate that CNNs efficiently extract complex features and serve as a cost-effective alternative to conventional spectrometric instruments.

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来源期刊

Spectrochimica Acta Part B: Atomic Spectroscopy 物理-光谱学

CiteScore

6.10

自引率

12.10%

发文量

173

审稿时长

81 days

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.