Empirical Modal Decomposition Combined with Deep Learning for Photoacoustic Spectroscopy Detection of Mixture Gas Concentrations.

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL Analytical Chemistry Pub Date : 2024-11-07 DOI:10.1021/acs.analchem.4c04479
Zhenfeng Gong, Yeming Fan, Yuchen Guan, Guojie Wu, Liang Mei
{"title":"Empirical Modal Decomposition Combined with Deep Learning for Photoacoustic Spectroscopy Detection of Mixture Gas Concentrations.","authors":"Zhenfeng Gong, Yeming Fan, Yuchen Guan, Guojie Wu, Liang Mei","doi":"10.1021/acs.analchem.4c04479","DOIUrl":null,"url":null,"abstract":"<p><p>In photoacoustic spectroscopy based multicomponent gas analysis, the overlap of the absorption spectra among different gases can affect the measurement accuracy of gas concentrations. We report a multicomponent gas analysis method based on empirical modal decomposition (EMD), convolutional neural networks (CNN), and long short-term memory (LSTM) networks that can extract the exact concentrations of mixed gases from the overlapping wavelength-modulated spectroscopy with second harmonic (WMS-2f) detection. The WMS-2f signals of 25 different concentration combinations of acetylene-ammonia mixtures are detected using a single distributed feedback laser (DFB) at 1531.5 nm. The acetylene concentrations range from 2.5 to 7.5 ppm and the ammonia concentrations from 12.5 to 37.5 ppm. The data set is enhanced by cyclic shifting and adding Gaussian noise. The classification accuracy of the test set reaches 99.89% after tuning. The mean absolute errors of the five additional sets of data measured under different conditions are 0.092 ppm for acetylene and 1.902 ppm for ammonia, within the above concentration ranges.</p>","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.analchem.4c04479","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

In photoacoustic spectroscopy based multicomponent gas analysis, the overlap of the absorption spectra among different gases can affect the measurement accuracy of gas concentrations. We report a multicomponent gas analysis method based on empirical modal decomposition (EMD), convolutional neural networks (CNN), and long short-term memory (LSTM) networks that can extract the exact concentrations of mixed gases from the overlapping wavelength-modulated spectroscopy with second harmonic (WMS-2f) detection. The WMS-2f signals of 25 different concentration combinations of acetylene-ammonia mixtures are detected using a single distributed feedback laser (DFB) at 1531.5 nm. The acetylene concentrations range from 2.5 to 7.5 ppm and the ammonia concentrations from 12.5 to 37.5 ppm. The data set is enhanced by cyclic shifting and adding Gaussian noise. The classification accuracy of the test set reaches 99.89% after tuning. The mean absolute errors of the five additional sets of data measured under different conditions are 0.092 ppm for acetylene and 1.902 ppm for ammonia, within the above concentration ranges.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
经验模态分解与深度学习相结合,用于光声光谱学检测混合气体浓度。
在基于光声光谱的多组分气体分析中,不同气体之间吸收光谱的重叠会影响气体浓度的测量精度。我们报告了一种基于经验模态分解(EMD)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多组分气体分析方法,该方法可以从重叠波长调制光谱二次谐波(WMS-2f)检测中提取混合气体的准确浓度。利用波长为 1531.5 nm 的单个分布式反馈激光器 (DFB) 检测了 25 种不同浓度组合的乙炔-氨气混合物的 WMS-2f 信号。乙炔浓度范围为 2.5 至 7.5 ppm,氨浓度范围为 12.5 至 37.5 ppm。数据集通过循环移动和添加高斯噪声得到增强。经过调整后,测试集的分类准确率达到 99.89%。在上述浓度范围内,在不同条件下测量的另外五组数据的平均绝对误差分别为乙炔 0.092 ppm 和氨 1.902 ppm。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
期刊最新文献
Disruption Dynamics and Charge Transfer of a Single Attoliter Emulsion Droplet Revealed by Combined Fast-Scan Sinusoidal Voltammetry and Short Time Fourier Transform Analysis. A Rapid and Simplified Approach to Correct Atmospheric Absorptions in Infrared Spectra. Early Diagnosis of Triple-Negative Breast Cancer Based on Dual microRNA Detection Using a Well-Defined DNA Crown-Carbon Dots Structure as an Electrochemiluminescence Sensing Platform. End-To-End Automated Intact Protein Mass Spectrometry for High-Throughput Screening and Characterization of Bispecific and Multispecific Antibodies. Highly Sensitive Determination of Copper Ions as MnO2 Etching Inhibitor in Single-Particle Nanoplasmonic Imaging.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1