抑制气体混合物吸收光谱中的交叉干扰。

Li Wang , Tingting Zhang , Qinduan Zhang , Yubin Wei , Tongyu Liu , Zhengran Hou , Bohan Qiu , Mingchao Sun
{"title":"抑制气体混合物吸收光谱中的交叉干扰。","authors":"Li Wang ,&nbsp;Tingting Zhang ,&nbsp;Qinduan Zhang ,&nbsp;Yubin Wei ,&nbsp;Tongyu Liu ,&nbsp;Zhengran Hou ,&nbsp;Bohan Qiu ,&nbsp;Mingchao Sun","doi":"10.1016/j.saa.2024.125352","DOIUrl":null,"url":null,"abstract":"<div><div>In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH<sub>4</sub> and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R<sup>2</sup>, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Suppression of cross-interference in the absorption spectra of gas mixtures\",\"authors\":\"Li Wang ,&nbsp;Tingting Zhang ,&nbsp;Qinduan Zhang ,&nbsp;Yubin Wei ,&nbsp;Tongyu Liu ,&nbsp;Zhengran Hou ,&nbsp;Bohan Qiu ,&nbsp;Mingchao Sun\",\"doi\":\"10.1016/j.saa.2024.125352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH<sub>4</sub> and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R<sup>2</sup>, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.</div></div>\",\"PeriodicalId\":433,\"journal\":{\"name\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S138614252401518X\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S138614252401518X","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

摘要

在利用可调谐二极管激光吸收光谱对混合气体进行定量分析时,多组分气体吸收光谱的重叠和相互干扰会导致测量误差大、分析精度低的问题。本文提出了一种改进的萤火虫算法,并将其应用于支持向量机回归模型来解决这一问题。具体方法包括引入自适应步长来平衡局部搜索和全局搜索,利用梯度下降法加速参数优化过程,从而提高模型的泛化能力和预测精度。实验结果表明,改进算法在预测 CH4 和 CO 气体浓度时的最大误差不超过 0.0443 % 和 2 ppm,判定系数 R2 分别为 0.9994 和 0.99815。该系统取得的可喜成果为实现单光源高精度检测多组分气体提供了理论支持,同时也证明了该方法在实际检测中的高效性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Suppression of cross-interference in the absorption spectra of gas mixtures
In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH4 and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R2, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.40
自引率
11.40%
发文量
1364
审稿时长
40 days
期刊介绍: Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science. The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments. Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate. Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to: Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences, Novel experimental techniques or instrumentation for molecular spectroscopy, Novel theoretical and computational methods, Novel applications in photochemistry and photobiology, Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.
期刊最新文献
∼2 μm broadband luminescence in Tm3+/Ho3+/Er3+-doped tellurite glass Assessment of the binding mechanism of ergothioneine to human serum albumin: Multi-spectroscopy, molecular docking and molecular dynamic simulation Determination of aflatoxin B1 in wheat using Raman spectroscopy combined with chemometrics Mn4+-activated Sc-based hexafluoride red phosphor K5Sc3F14: Synthesis, luminescence, and its applications in blue-pump WLEDs Simultaneous quantitative analysis of multiple metabolites using label-free surface-enhanced Raman spectroscopy and explainable deep learning
×
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