Processing of Mass Spectra of Exhaled Gases Based on Correlation Algorithms

Abstract

Analyses of the composition of exhaled gases are among important areas of non-invasive medicine. Diagnostics based on the analysis of exhaled gases offers several advantages over traditional laboratory methods. An analysis of a gas mixture is safe for the staff, because it does not involve chemical and biological fluids. The analysis is relatively inexpensive, takes little time, and ensures the detection of volatile components in the exhaled gas at the trace level of substances in real time. This paper discusses algorithms for the processing of mass spectra of exhaled gases obtained on an MC7-200 quadrupole mass spectrometer with electron ionization and the direct capillary injection of a sample. A comparative evaluation of algorithms for the detection of spectral lines in mass spectra based on data convolution with a model of response function or its derivatives is given. The Hermite function, the Gaussian function, and its even-order derivatives are considered as convolution kernels. The results of evaluation of the potential for increasing the signal-to-noise ratio by algorithms of convolution with these functions are presented.