This paper provides a selection of the last two decades publications on the development and application of chemical sensors based on piezoelectric quartz resonators for a wide range of analytical tasks. Most of the attention is devoted to an analysis of gas and liquid media and to industrial processes controls utilizing single quartz crystal microbalance (QCM) sensors, bulk acoustic wave (BAW) sensors, and their arrays in e-nose systems. The unique opportunity to estimate several heavy metals in natural and wastewater samples from the output of a QCM sensor array highly sensitive to changes in metal ion activity in water vapor is shown. The high potential of QCM multisensor systems for fast and cost-effective water contamination assessments “in situ” without sample pretreatment is demonstrated.

Currently, antibiotics are often prescribed to children without reason due to the inability to quickly establish the presence of a bacterial etiology of the disease. One way to obtain additional diagnostic information quickly is to study the volatile metabolome of biosamples using arrays of sensors. The goal of this work was to assess the possibility of using an array of chemical sensors with various sensitive coatings to determine the presence of a bacterial infection in children by analyzing the equilibrium gas phase (EGP) of urine samples. The EGP of 90 urine samples from children with and without a bacterial infection (urinary tract infection, soft tissue infection) was studied on the “MAG-8” device with seven piezoelectric sensors in a hospital. General urine analysis with sediment microscopy was performed using a Uriscan Pro analyzer and using an Olympus CX31 microscope. After surgical removal of the source of inflammation, the microbiological studies of the biomaterial were performed to determine the presence and type of the pathogen. The most informative output data of an array of sensors have been established for diagnosing bacterial pathology. Regression models were built to predict the presence of a bacterial infection in children with an error of no more than 15%. An indicator of infection is proposed to predict the presence of a bacterial infection in children with a high sensitivity of 96%.

One of the topical approaches in analysis – outside the framework of traditional ones – is the formation of an integral “image” of the object. There are several approaches to solving the issue of obtaining as much information about the sample by a certain portion of its properties or its composition as possible. The first approach is forming a visual image (diagram) of several different properties of the analyzed sample, for example, the content of certain metals, acids, volatile components and some other indicators of wine quality. The consolidated image of a sample enables us to distinguish samples identical or similar in the selected properties from crucially different ones, even in case of an acceptable change of each indicator. Or else, using the consolidated image one can evaluate the direction of an image shift of a certain sample compared to the set of standard samples. The analysis of the geometry of the sample image by diverse indicators affords ground for assumption of the reasons for this deviation, as well as identification of falsification, or even solution of a more complicated task: detecting the area of growth of raw materials. The second approach is close to the first one in terms of methodology, but it digitizes properties using detectors and presents this as an image (“visual print” of response) of signals of these detectors on some components of the sample (presence, content). The feature of this approach is the use of a detector system that is non-selective and cross-sensitive to certain sample components. These sample images are produced using a system of “electronic nose”. “Visual prints” of array signals of different character sensors contain qualitative and quantitative information about the part of the analyzed sample which is sorbed by sensors. Despite the uncertainty of this information, “electronic noses” of piezoelectric type are widely used in the analysis of samples with complex varying composition.

Currently, patients with stomach and duodenum diseases should be examined using diagnostic tests for Helicobacter pylori infection. Diagnostic methods available in the arsenal of clinicians make it possible to identify Helicobacter pylori in almost 100% of cases. A competent approach to the selection of a diagnostic test provides high-quality, timely, and economical verification of the pathogen. The paper provides information on the currently available diagnostic procedures for the detection of Helicobacter pylori infection and the results of the diagnosis of helicobacteriosis based on the activity of urease in saliva. The main area of research is the development of an analytical method based on the quantitative determination of ammonia and the detection of amines and aliphatic acids in exhaled air using signals from an array of diverse piezoelectric chemical sensors. Simple data processing algorithms were applied, and qualitative and quantitative decision-making criteria were substantiated. The method of analysis using chemical sensors is characterized by high specificity (96% for ammonia) and sensitivity (90%); it does not require special sample preparation and is indispensable in large-scale studies and personal monitoring and for choosing treatment tactics.

This paper discusses the application of two approaches (direct and inverse) to the identification of volatile substances by means of a gas sensor array in a headspace over nasal mucus swab samples taken from calves with differing degrees of respiratory damage. We propose a unique method to visualize sensor array data for quality analysis, based on the spectra of cross mass sensitivity parameters. The traditional method, which requires an initial sensor array trained on the vapors of the individual substances (database accumulation)—with their further identification in the analyzed bio-samples through the comparison of the analysis results to the database—has shown unsatisfactory performance. The proposed inverse approach is more informative for the pattern recognition of volatile substances in the headspace of mucus samples. The projection of the calculated parameters of the sensor array for individual substances in the principal component space, acquired while processing the sensor array output from nasal swab samples, has allowed us to divide animals into groups according to the clinical diagnosis of their lung condition (healthy respiratory system, bronchitis, or bronchopneumonia). The substances detected in the gas phase of the nasal swab samples (cyclohexanone, butanone-2,4-methyl-2-pentanone) were correlated with the clinical state of the animals, and were consistent with the reference data on disease markers in exhaled air established for destructive organism processes.