Real-Time Data Processing for an Electro-Optic Dual-Comb Spectrometer

Nitzsche, Leonard; Goldschmidt, Jens; Kießling, Jens; Wolf, Sebastian; Kühnemann, Frank; Wöllenstein, Jürgen

doi:10.1364/ais.2021.jtu2e.2

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the spectra, only the maximum positions of the comb modes are selected, which yields the respective sample

and reference

spectra. The coherent averaging and comb mode selection allow compressing the recorded data stream from 1 GByte/s down to 10 kByte/s, enabling the continuous spectra acquisition with a 10 Hz rate [ 28 ]. Absorbance spectra are obtained by normalization of the sample spectrum derived from the recorded signal of PD 1 by the spectrum derived from the signal of PD 2 given with [ 12 ]:

…”

Section: Methodsmentioning

confidence: 99%

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

Goldschmidt

Nitzsche

Wolf

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…From the spectra, only the maximum positions of the comb modes are selected, which yields the respective sample

and reference

…”

Section: Methodsmentioning

confidence: 99%

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

Goldschmidt

Nitzsche

Wolf

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, despite their theoretical hardware realization potential, prior demonstrations of computational-only phase correction involved manual data post-processing. Rapid developments in software (data processing techniques) and hardware (digitizer or fieldprogrammable gate array) have pushed DCS towards high-speed and real-time measurements [27][28][29]. In particular, the adaptive interferogram sampling technique presented by Ideguchi et al enables real-time DCS with free-running OFCs across different spectral regions and with different comb platforms [30][31][32][33], but at the expense of a significant complication on the hardware side, as it requires additional frequency multipliers, photodetectors, CW lasers, and dedicated digitizers.…”

Section: Introductionmentioning

confidence: 99%

Quasi-real-time dual-comb spectroscopy with 750-MHz Yb:fiber combs

Tian

Sterczewski

et al. 2022

Opt. Express

View full text Add to dashboard Cite

We present quasi-real-time dual-comb spectroscopy (DCS) using two Yb:fiber combs with ∼750 MHz repetition rates. A computational coherent averaging technique is employed to correct timing and phase fluctuations of the measured dual-comb interferogram (IGM). Quasi-real-time phase correction of 1-ms long acquisitions occurs every 1.5 seconds and is assisted by coarse radio frequency (RF) phase-locking of an isolated RF comb mode. After resampling and global offset phase correction, the RF comb linewidth is reduced from 200 kHz to ∼1 kHz, while the line-to-floor ratio increases 13 dB in power in 1 ms. Using simultaneous offset frequency correction in opposite phases, we correct the aliased RF spectrum spanning three Nyquist zones, which yields an optical coverage of ∼180 GHz around 1.035 µm probed on a sub-microsecond timescale. The absorption profile of gaseous acetylene is observed to validate the presented technique.

show abstract

Real-Time Data Processing for an Electro-Optic Dual-Comb Spectrometer

Abstract: To overcome the data burden of dual-comb spectrometers, we developed a data processing method for real-time spectra calculation during data acquisition. The spectra retain their envelope structure and the additional RMS-noise can be neglected.

Cited by 2 publications

References 4 publications

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

Quasi-real-time dual-comb spectroscopy with 750-MHz Yb:fiber combs

Contact Info

Product

Resources

About