Bayraklı, İsmailEken, Enes2023-02-232023-02-2320230030-3992https:/dx.doi.org/10.1016/j.optlastec.2022.108918https://hdl.handle.net/20.500.12451/10281A 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.eninfo:eu-repo/semantics/embargoedAccessSensorLaser SpectroscopyBreath Air AnalysisDeep Neural NetworksMachine LearningLong-short term MemoryArticle15810.1016/j.optlastec.2022.108918Q1WOS:000904474500002Q1