Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data

Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data

Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads...

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Bibliographic Details
Main Authors: Jens Goldschmidt, Leonard Nitzsche, Sebastian Wolf, Armin Lambrecht, Jürgen Wöllenstein
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Sensors
Subjects:
spectral analysis
quantitative gas analysis
machine learning
artificial neural networks
dual comb spectroscopy
broadband spectroscopy
Online Access:https://www.mdpi.com/1424-8220/22/3/857

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