{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:39:53Z","timestamp":1780533593115,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T00:00:00Z","timestamp":1642896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fraunhofer Internal Programs","award":["MAVO 840 130"],"award-info":[{"award-number":["MAVO 840 130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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 \u00b5s 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.<\/jats:p>","DOI":"10.3390\/s22030857","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:36:27Z","timestamp":1642970187000},"page":"857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8312-804X","authenticated-orcid":false,"given":"Jens","family":"Goldschmidt","sequence":"first","affiliation":[{"name":"Laboratory for Gas Sensors, Department of Microsystems Engineering-IMTEK, University of Freiburg, Georges-K\u00f6hler-Allee 102, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonard","family":"Nitzsche","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-K\u00f6hler-Allee 301, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Wolf","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-K\u00f6hler-Allee 301, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Lambrecht","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-K\u00f6hler-Allee 301, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"W\u00f6llenstein","sequence":"additional","affiliation":[{"name":"Laboratory for Gas Sensors, Department of Microsystems Engineering-IMTEK, University of Freiburg, Georges-K\u00f6hler-Allee 102, 79110 Freiburg, Germany"},{"name":"Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-K\u00f6hler-Allee 301, 79110 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0925-4005(93)01183-5","article-title":"Environmental gas sensing","volume":"20","author":"Yamazoe","year":"1994","journal-title":"Sens. Actuators B"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1364\/OPTICA.1.000290","article-title":"Frequency-comb-based remote sensing of greenhouse gases over kilometer air paths","volume":"1","author":"Rieker","year":"2014","journal-title":"Optica"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00340-007-2894-1","article-title":"New method for isotopic ratio measurements of atmospheric carbon dioxide using a 4.3 \u03bcm pulsed quantum cascade laser","volume":"90","author":"Nelson","year":"2008","journal-title":"Appl. Phys. B"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1103\/PhysRevLett.99.263902","article-title":"Cavity Enhanced Optical Vernier Spectroscopy, Broad Band, High Resolution, High Sensitivity","volume":"99","author":"Gohle","year":"2007","journal-title":"Phys. Rev. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8230","DOI":"10.3390\/s91008230","article-title":"Breath analysis using laser spectroscopic techniques: Breath biomarkers, spectral fingerprints, and detection limits","volume":"9","author":"Wang","year":"2009","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1088\/0957-0233\/13\/2\/302","article-title":"FTIR emission spectroscopy methods and procedures for real time quantitative gas analysis in industrial environments","volume":"13","author":"Bak","year":"2002","journal-title":"Meas. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0021-9673(99)00123-5","article-title":"Capillary gas chromatography for the determination of halogenated micro-contaminants","volume":"843","year":"1999","journal-title":"J. Chromatogr. A"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0168-1176(97)00281-4","article-title":"On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS) medical applications, food control and environmental research","volume":"173","author":"Lindinger","year":"1998","journal-title":"Int. J. Mass Spectrom. Ion Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/S0165-9936(02)00813-0","article-title":"The application of gas chromatography to environmental analysis","volume":"21","author":"Santos","year":"2002","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/0168-1176(95)04294-U","article-title":"Proton transfer reaction mass spectrometry: On-line trace gas analysis at the ppb level","volume":"149","author":"Hansel","year":"1995","journal-title":"Int. J. Mass Spectrom. Ion Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Demtr\u00f6der, W. (2011). Laserspektroskopie 1, Springer.","DOI":"10.1007\/978-3-642-21306-9"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Griffiths, P.R., and de Haseth, J.A. (2007). Fourier Transform Infrared Spectrometry, Wiley-Interscience. [2nd ed.].","DOI":"10.1002\/047010631X"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1366\/000370277774463760","article-title":"A Nonlinear Least Squares Method of Determining Line Intensities and Half-Widths","volume":"31","author":"Chang","year":"1977","journal-title":"Appl. Spectrosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0022-4073(79)90116-X","article-title":"Least squares analysis of Voigt-shaped lines","volume":"22","author":"Lin","year":"1979","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jqsrt.2017.06.038","article-title":"The HITRAN2016 molecular spectroscopic database","volume":"203","author":"Gordon","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11035","DOI":"10.1021\/acs.analchem.0c00075","article-title":"Partial Least-Squares Regression as a Tool to Retrieve Gas Concentrations in Mixtures Detected Using Quartz-Enhanced Photoacoustic Spectroscopy","volume":"92","author":"Zifarelli","year":"2020","journal-title":"Anal. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ouyang, T., Wang, C., Yu, Z., Stach, R., Mizaikoff, B., Liedberg, B., Huang, G.-B., and Wang, Q.-J. (2019). Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model. Sensors, 19.","DOI":"10.3390\/s19245535"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1364\/OL.410762","article-title":"AI-enabled real-time dual-comb molecular fingerprint imaging","volume":"45","author":"Voumard","year":"2020","journal-title":"Opt. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1063\/1.1144830","article-title":"Neural networks and their applications","volume":"65","author":"Bishop","year":"1994","journal-title":"Rev. Sci. Instrum."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1039\/C7AN01371J","article-title":"Deep convolutional neural networks for Raman spectrum recognition: A unified solution","volume":"142","author":"Liu","year":"2017","journal-title":"Analyst"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4447","DOI":"10.1007\/s00216-020-02687-5","article-title":"Artificial neural networks for quantitative online NMR spectroscopy","volume":"412","author":"Kern","year":"2020","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jqsrt.2016.03.005","article-title":"HITRAN Application Programming Interface (HAPI): A comprehensive approach to working with spectroscopic data","volume":"177","author":"Kochanov","year":"2016","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25449","DOI":"10.1364\/OE.428709","article-title":"Tunable dual-comb spectrometer for mid-infrared trace gas analysis from 3 to 4.7 \u00b5m","volume":"29","author":"Nitzsche","year":"2021","journal-title":"Opt. Express"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/nphoton.2015.250","article-title":"Frequency-agile dual-comb spectroscopy","volume":"10","author":"Millot","year":"2016","journal-title":"Nat. Photon."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.snb.2016.03.089","article-title":"Simultaneous atmospheric CO, N 2 O and H 2 O detection using a single quantum cascade laser sensor based on dual-spectroscopy techniques","volume":"231","author":"Li","year":"2016","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"28106","DOI":"10.1364\/OE.20.028106","article-title":"Compact and portable open-path sensor for simultaneous measurements of atmospheric N2O and CO using a quantum cascade laser","volume":"20","author":"Tao","year":"2012","journal-title":"Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1364\/OL.27.000766","article-title":"Spectrometry with frequency combs","volume":"27","author":"Schiller","year":"2002","journal-title":"Opt. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nitzsche, L., Goldschmidt, J., Kie\u00dfling, J., Wolf, S., K\u00fchnemann, F., and W\u00f6llenstein, J. (2021, January 19\u201323). Real-Time Data Processing for an Electro-Optic Dual-Comb Spectrometer. Proceedings of the OSA Optical Sensors and Sensing Congress, Washington, DC, USA.","DOI":"10.1364\/AIS.2021.JTu2E.2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_30","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Negassi, M., Wagner, D., and Reiterer, A. (2021). Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation. arXiv.","DOI":"10.3390\/a15050165"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1515\/teme-2021-0107","article-title":"Two-component gas sensing with MIR dual comb spectroscopy","volume":"89","author":"Nitzsche","year":"2022","journal-title":"TM-Tech. Mess."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:06:12Z","timestamp":1760133972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,23]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030857"],"URL":"https:\/\/doi.org\/10.3390\/s22030857","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,23]]}}}