As a first step, a smoothening of the raw data was carried out. This was done to account for the difference between the response time of the system (about 30 s) and the time to acquire the full spectrum (about 100 s). This means that the gas concentration may vary when acquiring the spectrum, leading to a wrong data fit. To deal with this problem we apply a Gaussian smoothing over five spectra. This was found to keep the signals sharp This pretreated data is then used for quantifying the different gases. For each single spectrum, the least-square solution is calculated against a calibration table, finding the most probable composition to explain the signals at a given time. This approach includes the boundary assumption that the fragments of the different components do not influence each other, i.e. the measured spectra is the sum of all individual components.