The infrared laser photoacoustic spectroscopy (LPAS) and the pattern-recognition-based approach for noninvasive express diagnostics of pulmonary diseases on the basis of absorption spectra analysis of the patient’s exhaled air are presented. The study involved lung cancer patients ( N = 9 ), patients with chronic obstructive pulmonary disease ( N = 12 ), and a control group of healthy, nonsmoking volunteers ( N = 11 ). The analysis of the measured absorption spectra was based at first on reduction of the dimension of the feature space using principal component analysis; thereafter, the dichotomous classification was carried out using the support vector machine. The gas chromatography–mass spectrometry method (GC–MS) was used as the reference. The estimated mean value of the sensitivity of exhaled air sample analysis by the LPAS in dichotomous classification was not less than 90% and specificity was not less than 69%; the analogous results of analysis by GC–MS were 68% and 60%, respectively. Also, the approach to differential diagnostics based on the set of SVM classifiers usage is presented.

A laser gas analyzer has been designed for determining the composition of exhaled air by means of photoacoustic spectroscopy. The analyzer, based on a broadband optical parametric oscillator and photoacoustic detector, provides high-precision rapid analysis of the multicomponent composition of human exhaled air for dynamic estimation of the efficiency of treating bronchus and lung diseases.

A human exhaled air analysis by means of infrared (IR) laser photoacoustic spectroscopy is presented. Eleven healthy nonsmoking volunteers (control group) and seven patients with chronic obstructive pulmonary disease (COPD, target group) were involved in the study. The principal component analysis method was used to select the most informative ranges of the absorption spectra of patients' exhaled air in terms of the separation of the studied groups. It is shown that the data of the profiles of exhaled air absorption spectrum in the informative ranges allow identifying COPD patients in comparison to the control group.

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