We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly-(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring.

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Safrole is the main precursor for producing the amphetamine-type stimulant (ATS) drug, N-methyl-3,4-methylenedioxyamphetamine (MDMA), also known as ecstasy. We devise a polyacrylonitrile (PAN) nanofiber-based quartz crystal microbalance (QCM) for detecting safrole. The PAN nanofibers were fabricated by direct electrospinning to modify the QCM chips. The PAN nanofiber on the QCM chips has a diameter of 240 ± 10 nm. The sensing of safrole by QCM modified with PAN nanofiber shows good reversibility and an apparent sensitivity of 4.6 Hz·L/mg. The proposed method is simple, inexpensive, and convenient for detecting safrole, and can be an alternative to conventional instrumental analytical methods for general volatile compounds.

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry. Author Contributions: Conceptualization, K.T. and A.M.P.; Data curation, I.F., D.L., and N.N.; Formal analysis, S.N.H., K.T., A.C.A.V. and A.M.P.; Funding acquisition, K.T.; Investigation, I.F.; Methodology, K.T., T.J., A.C.A.V.; Project administration, T.J.; Resources, Y.Y. and N.N.; Software, S.N.H., A.C.A.V. and A.M.P.; Supervision, K.T. and N.N.; Validation, K.T., T.J., Y.Y.; Visualization, D.L. and A.M.P.; Writing-original draft, S.N.H., D.L.; Writing-review & editing, K.T., A.C.A.V., A.M.P.

Short-chain alcohols are a group of volatile organic compounds (VOCs) that are often found in workplaces and laboratories, as well as medical, pharmaceutical, and food industries. Real-time monitoring of alcohol vapors is essential because exposure to alcohol vapors with concentrations of 0.15–0.30 mg·L−1 may be harmful to human health. This study aims to improve the detection capabilities of quartz crystal microbalance (QCM)-based sensors for the analysis of alcohol vapors. The active layer of chitosan was immobilized onto the QCM substrate through a self-assembled monolayer of L-cysteine using glutaraldehyde as a cross-linking agent. Before alcohol analysis, the QCM sensing chip was exposed to humidity because water vapor significantly interferes with QCM gas sensing. The prepared QCM sensor chip was tested for the detection of four different alcohols: n-propanol, ethanol, isoamyl alcohol, and n-amyl alcohol. For comparison, a non-alcohol of acetone was also tested. The prepared QCM sensing chip is selective to alcohols because of hydrogen bond formation between the hydroxyl groups of chitosan and the analyte. The highest response was achieved when the QCM sensing chip was exposed to n-amyl alcohol vapor, with a sensitivity of about 4.4 Hz·mg−1·L. Generally, the sensitivity of the QCM sensing chip is dependent on the molecular weight of alcohol. Moreover, the developed QCM sensing chips are stable after 10 days of repeated measurements, with a rapid response time of only 26 s. The QCM sensing chip provides an alternative method to established analytical methods such as gas chromatography for the detection of short-chain alcohol vapors.

Abstract. Quartz crystal microbalance (QCM) coated with poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate mixed with polyvinyl alcohol (PEDOT–PSS/PVA) nanofiber has been fabricated as a humidity sensor using the electrospinning method. Three types of PEDOT–PSS/PVA nanofiber sensors are fabricated with different needle-to-collector electrospinning distances. The scanning electron microscope images confirm the presence of beads in the nanofiber structure. The results show that the sensor mass deposition increased with the decrease in needle-to-collector distance. The best sensor performance is exhibited by the sample with medium needle-to-collector distance (QCM NF 2). The QCM NF 2 nanofiber sensor shows excellent sensitivity of up to 33.56 Hz per percentage point of relative humidity, with rapid response (5.6 s) and recovery (3.5 s) times, good linearity, excellent repeatability, low hysteresis, and long-term stability and response. The QCM PEDOT–PSS/PVA nanofiber sensor provides a simple method to fabricate high-performance humidity sensors.

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