We report on the application of neural-network processing to pulsed photoacoustics for improving the detection limit by subtracting the window-heating-associated background. This technique was applied to the measurement of ethylene traces excited by a TEA (transverse electrical discharge in gas at atmospheric pressure) laser. The signal contains a term that shows absorption saturation, characteristic of the absorbing gas, and another, generated by window heating, linearly dependent on laser energy. At low concentrations, normalization to laser energy is not possible owing to the different absorption mechanisms. To overcome this problem we relied on a neural-network filter, trained with experimentally obtained patterns, that subtracts the background and returns the sample concentration. This way, we reduced the detection limit to 20% of the previous limit obtained by reading the main resonance peak amplitude.
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