Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks. The results from preprocessing were generally of higher quality than what was achieved using a reference based on standardized methods (second-difference, asymmetric least squares, cross-validation). From 10⁵ simulated observations, 91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-to-noise (SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene, paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept for the automated preprocessing of Raman spectra.

Defocused laser speckle photography is used as a tool to measure the heat responses in a titanium component during laser heating. The evolution of the response is compared with a set of preprocessed Finite Element Simulations of the corresponding process with the aim to verify the simulation model and to find the simulation settings that best resemble the experimental results. The titanium component consists of a 300 x 100 mm2 substrate of thickness 3.2 mm on which a 200 x 30 x 11 mm3 ridge is built up using the laser metal deposition by wire process. The component is heated on the top of the ridge by a 300 W laser for 10 s and the deformation of the subtrate is followed throughout the heating-cooling cycle. The simulated deformation gradient is shown to resemble the measured response, and the magnitude of the response indicates that about 70 % of the laser power transferres into heat in the metal.

Acute hypoxia changes the redox-state of pulmonary arterial smooth muscle cells (PASMCs). This might influence the activity of redox-sensitive voltage-gated K⁺-channels (Kv-channels) whose inhibition initiates hypoxic pulmonary vasoconstriction (HPV). However, the molecular mechanism of how hypoxia-or the subsequent change in the cellular redox-state-inhibits Kv-channels remains elusive. For this purpose, a new multifunctional gas-tight microfluidic system was developed enabling simultaneous single-cell Raman spectroscopic studies (to sense the redox-state under normoxic/hypoxic conditions) and patch-clamp experiments (to study the Kv-channel activity). The performance of the system was tested by optically recording the O₂-content and taking Raman spectra on murine PASMCs under normoxic/hypoxic conditions or in the presence of H₂O₂. Oxygen sensing showed that hypoxic levels in the gas-tight microfluidic system were achieved faster, more stable and significantly lower compared to a conventional open system (1.6 ± 0.2%, respectively 6.7 ± 0.7%, n = 6, p < 0.001). Raman spectra revealed that the redistribution of biomarkers (cytochromes, FeS, myoglobin and NADH) under hypoxic/normoxic conditions were improved in the gas-tight microfluidic system (p-values from 0.00% to 16.30%) compared to the open system (p-value from 0.01% to 98.42%). In conclusion, the new redox sensor holds promise for future experiments that may elucidate the role of Kv-channels during HPV.