希特勒
谱线
分光计
红外光谱学
吸收光谱法
微量气体
人工神经网络
光谱学
吸收(声学)
分析化学(期刊)
化学
生物系统
材料科学
计算机科学
人工智能
光学
物理
色谱法
复合材料
有机化学
生物
量子力学
天文
作者
Jens Goldschmidt,Leonard Nitzsche,Sebastian Wolf,A. Lambrecht,Jürgen Wöllenstein
出处
期刊:Sensors
[MDPI AG]
日期:2022-01-23
卷期号:22 (3): 857-857
被引量:21
摘要
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 to long computation times of the fitting routines due to the increased number of spectral features that need to be computed for each iteration during the fit. This hinders the capability of real-time analysis of the gas matrix. Here, an artificial neural network (ANN) is employed for rapid prediction of gas concentrations in complex infrared absorption spectra composed of mixtures of CO and N2O. Experimental data is acquired with a mid-infrared dual frequency comb spectrometer. To circumvent the experimental collection of huge amounts of training data, the network is trained on synthetically generated spectra. The spectra are based on simulated absorption profiles making use of the HITRAN database. In addition, the spectrometer’s influence on the measured spectra is characterized and included in the synthetic training data generation. The ANN was tested on measured spectra and compared to a non-linear least squares fitting algorithm. An average evaluation time of 303 µs for a single measured spectrum was achieved. Coefficients of determination were 0.99997 for the predictions of N2O concentrations and 0.99987 for the predictions of CO concentrations, with uncertainties on the predicted concentrations between 0.04 and 0.18 ppm for 0 to 100 ppm N2O and between 0.05 and 0.18 ppm for 0 to 60 ppm CO.
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