The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated

Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.

The application of gas sensors in breath analysis is an important trend in the early diagnostics of different diseases including lung cancer, ulcers, and enteric infection. However, traditional methods of synthesis of metal oxide gas-sensing materials for semiconductor sensors based on wet sol-gel processes give relatively high sensitivity of the gas sensor to changing humidity. The sol-gel process leading to the formation of superficial hydroxyl groups on oxide particles is responsible for the strong response of the sensing material to this factor. In our work, we investigated the possibility to synthesize metal oxide materials with reduced sensitivity to water vapors. Dry synthesis of SnO2 nanoparticles was implemented in gas phase by spark discharge, enabling the reduction of the hydroxyl concentration on the surface and allowing the production of tin dioxide powder with specific surface area of about 40 m2/g after annealing at 610 °C. The drop in sensor resistance does not exceed 20% when air humidity increases from 40 to 100%, whereas the response to 100 ppm of hydrogen is a factor of 8 with very short response time of about 1 s. The sensor response was tested in mixtures of air with hydrogen, which is the marker of enteric infections and the marker of early stage fire, and in a mixture of air with lactate (marker of stomach cancer) and ammonia gas (marker of Helicobacter pylori, responsible for stomach ulcers).

Early detection of lung cancer usually markedly increases the efficiency of therapy. However, the currently employed diagnostic approaches are not sufficiently effective, resulting in late detection of the disease and high patient mortality. Therefore, development of a high-throughput and reliable diagnostic method is a priority task requiring fast solution. Analysis of exhaled air for a number of organic compounds recognized as lung cancer biomarkers seems to be a promising approach for early diagnosis of the disease. This issue attracts growing interest, as indicated by increasing number of publications on this topic. This review surveys contemporary analytical techniques for analysis of exhaled air, including various spectroscopic and mass spectral methods and also gas sensor-based methods. The key benefits and shortcomings of the techniques, sample injection and pre-concentration methods, and the potential applicability of the methods for lung cancer detection are discussed. The prospects of simultaneous application of several analytical techniques and approaches for the early diagnosis are demonstrated. The bibliography includes 147 references.

A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ng/L. The experiments demonstrate the success of the E-tongue in combination with partial least squares (PLS) regression technique for the GE/MIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ng/L and at higher concentrations from 0.25 to 10 mg/L by means of PLS-discriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easy-to-handle tool for express evaluation of GE/MIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users.