A novel breath molecule sensing system based on deep neural network employing multiple-line direct absorption spectroscopy
| dc.contributor.author | Bayraklı, İsmail | |
| dc.contributor.author | Eken, Enes | |
| dc.date.accessioned | 2023-02-23T12:57:30Z | |
| dc.date.available | 2023-02-23T12:57:30Z | |
| dc.date.issued | 2023 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | A novel ppb-level biomedical sensor is developed to analyze breath samples for continuous monitoring of diseases. The setup is very compact, consisting of a distributed feedback quantum cascade laser (DFB-QCL) and a single-pass absorption cell. To make the sensor more compact and functional, a deep neural network (DNN) model is utilized for predicting gas concentrations. In order to evaluate the performance of the sensor, N2O is used as the target molecule. A minimum detection limit of 500 ppb is achieved in a single-pass absorption cell configuration. The model is trained on multiple N2O/CO2 absorption lines (instead of an isolated line) with concentrations between 0 to 500 ppm generated using the HITRAN database. The trained model is tested on measured spectra and compared to a non-linear least squares fitting algorithm. The coefficients of determination (R2) were found to be 0.997 and 0.981 for the predictions of N2O concentrations in the N2O/N2 gas mixture and the breath air, respectively. The accuracies of 2.5% and 2.9% were achieved by the sensor for both cases. | |
| dc.identifier.doi | 10.1016/j.optlastec.2022.108918 | |
| dc.identifier.issn | 0030-3992 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https:/dx.doi.org/10.1016/j.optlastec.2022.108918 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/10281 | |
| dc.identifier.volume | 158 | en_US |
| dc.identifier.wos | WOS:000904474500002 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.ispartof | Optics and Laser Technology | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Sensor | |
| dc.subject | Laser Spectroscopy | |
| dc.subject | Breath Air Analysis | |
| dc.subject | Deep Neural Networks | |
| dc.subject | Machine Learning | |
| dc.subject | Long-short term Memory | |
| dc.title | A novel breath molecule sensing system based on deep neural network employing multiple-line direct absorption spectroscopy | |
| dc.type | Article |
