Neural network data analysis for intracavity laser spectroscopy

Petr V. Nazarov; Vladimir V. Apanasovich; Katsiaryna U. Lutkovskaya; Vladimir M. Lutkovski; Pulat Y. Misakov

doi:10.1117/12.518070

Neural network data analysis for intracavity laser spectroscopy

Petr V. Nazarov^*, Vladimir V. Apanasovich, Katsiaryna U. Lutkovskaya, Vladimir M. Lutkovski, Pulat Y. Misakov

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

The method of data analysis in intracavity laser spectroscopy is considered. The artificial neural network was used as an analyzing tool for the determination of elements concentration in trace amounts samples using absorption spectra. The special neural network training algorithm based on simulation of experimental spectra was developed to solve the problem of non-sufficient experimental data set. The application of this method allows achieve the better sensitivity than conventional analytical methods and proved itself more robust. The proposed method was tested on spectra of Cs water solutions.

Original language	English
Pages (from-to)	61-69
Number of pages	9
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5135
DOIs	https://doi.org/10.1117/12.518070
Publication status	Published - 2002
Externally published	Yes
Event	PROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: International Conference on Lasers, Applications, and Technologies 2002 Optical Information, Data Processing and Storage, and Laser Communication Technologies - Moscow, Russian Federation Duration: 22 Jun 2002 → 27 Jun 2002

Keywords

Atomic absorption
Data processing
Intracavity laser spectroscopy
Neural network

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10.1117/12.518070

Cite this

@article{38f50841dbeb4eb291e4f403e18270c7,

title = "Neural network data analysis for intracavity laser spectroscopy",

abstract = "The method of data analysis in intracavity laser spectroscopy is considered. The artificial neural network was used as an analyzing tool for the determination of elements concentration in trace amounts samples using absorption spectra. The special neural network training algorithm based on simulation of experimental spectra was developed to solve the problem of non-sufficient experimental data set. The application of this method allows achieve the better sensitivity than conventional analytical methods and proved itself more robust. The proposed method was tested on spectra of Cs water solutions.",

keywords = "Atomic absorption, Data processing, Intracavity laser spectroscopy, Neural network",

author = "Nazarov, {Petr V.} and Apanasovich, {Vladimir V.} and Lutkovskaya, {Katsiaryna U.} and Lutkovski, {Vladimir M.} and Misakov, {Pulat Y.}",

year = "2002",

doi = "10.1117/12.518070",

language = "English",

volume = "5135",

pages = "61--69",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "PROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: International Conference on Lasers, Applications, and Technologies 2002 Optical Information, Data Processing and Storage, and Laser Communication Technologies ; Conference date: 22-06-2002 Through 27-06-2002",

}

TY - JOUR

T1 - Neural network data analysis for intracavity laser spectroscopy

AU - Nazarov, Petr V.

AU - Apanasovich, Vladimir V.

AU - Lutkovskaya, Katsiaryna U.

AU - Lutkovski, Vladimir M.

AU - Misakov, Pulat Y.

PY - 2002

Y1 - 2002

N2 - The method of data analysis in intracavity laser spectroscopy is considered. The artificial neural network was used as an analyzing tool for the determination of elements concentration in trace amounts samples using absorption spectra. The special neural network training algorithm based on simulation of experimental spectra was developed to solve the problem of non-sufficient experimental data set. The application of this method allows achieve the better sensitivity than conventional analytical methods and proved itself more robust. The proposed method was tested on spectra of Cs water solutions.

AB - The method of data analysis in intracavity laser spectroscopy is considered. The artificial neural network was used as an analyzing tool for the determination of elements concentration in trace amounts samples using absorption spectra. The special neural network training algorithm based on simulation of experimental spectra was developed to solve the problem of non-sufficient experimental data set. The application of this method allows achieve the better sensitivity than conventional analytical methods and proved itself more robust. The proposed method was tested on spectra of Cs water solutions.

KW - Atomic absorption

KW - Data processing

KW - Intracavity laser spectroscopy

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=0344943296&partnerID=8YFLogxK

U2 - 10.1117/12.518070

DO - 10.1117/12.518070

M3 - Conference article

AN - SCOPUS:0344943296

SN - 0277-786X

VL - 5135

SP - 61

EP - 69

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - PROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: International Conference on Lasers, Applications, and Technologies 2002 Optical Information, Data Processing and Storage, and Laser Communication Technologies

Y2 - 22 June 2002 through 27 June 2002

ER -

Neural network data analysis for intracavity laser spectroscopy

Abstract

Keywords

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