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Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

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.

Background Even though conceptually, Tuberculosis (TB) is almost always curable, it is currently the world’s leading infectious killer. Patients with pulmonary TB are the source of transmission. Approximately 23% of the world’s population is believed to be latently infected with TB bacteria, and 5–15% of them will progress at any point in time to develop the disease. There was a global diagnostic gap of 2.9 million between notifications of new cases and the estimated number of incident cases, and Indonesia carries the third-highest of this gap. Therefore, screening TB among the community is of great importance to prevent further transmission and infection. The electronic nose for screening TB (eNose-TB) project is initiated in Yogyakarta, Indonesia, to screen TB by breath test with an electronic-nose that is easy-to-use, point-of-care, does not expose patients to radiation, and can be produced at low cost. Methods/Design The objectives of the two-phase planned project are to: 1) investigate the potential of an eNose-TB as a screening tool in Indonesia, in comparison with screening with clinical symptoms and chest radiology, which are currently used as a standard, and 2) analyze the time and cost of a screening algorithm with eNose-TB to obtain additional case detection. A cross-sectional study will be conducted in the first phase to validate the eNose-TB. The validation phase will involve 395 presumptive TB patients in the Surakarta General Hospital, Central Java. In the second phase, a cross-sectional research will be conducted, involving 1,383 adults and children in the municipality of Yogyakarta and Kulon Progo district of Yogyakarta Province. Discussion The findings will provide data concerning the sensitivity and specificity of the eNose-TB as a screening tool for tuberculosis, and the time and cost analysis of a screening algorithm with the eNose. Trial registration NCT04567498; https://clinicaltrials.gov/.

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean < 20%, fine cocoa dark bean > 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation. © Intelligent Network and Systems Society., The authors thank the Directorate of Research and Community Service, Ministry of Research, Technology and Higher Education, the Republic of Indonesia for providing research grants of PTUPT 2019 (Contract No. 2688/UN1.DITLIT/DITLIT/LT/2019). The authors also like to acknowledge the financial support given by Associate Laboratory LSRE-LCM-UID/EQU/50020/2019, strategic funding UID/BIO/04469/2019-CEB, BioTecNorte operation (NORTE-01-0145-FEDER-000004) and strategic project PEst-OE/AGR/UI0690/2014 – CIMO, all funded by national funds through FCT/MCTES (PIDDAC)., info:eu-repo/semantics/publishedVersion

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&ndash, 0.30 mg&middot, L&minus, 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&middot, mg&minus, 1&middot, 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.

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.

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.

Tempeh is a popular traditional food from Indonesia, and its physical and chemical characteristics have been intensively investigated. Study on the fermentation process, however, still requires more attention. Here, the aroma profile during 10-day fermentation process was analyzed using an electronic nose (e-nose). The e-nose used in this study was based on eight kinds of the gas sensor (metal oxide semiconductor). A differential baseline manipulation and a maximum value as feature extraction were applied for the data processing. Meanwhile, the back-propagation neural network (BPNN) was used to classify the tempeh aroma profiles into four classes as described by principal component analysis (PCA). In this work, we optimized the BPNN and combined with the radial basis neural network (RBNN) to improve the performance of the e-nose. As a result, the combination between optimized BPNN and RBNN is able to recognize all data to the appropriate class as 100%.

Here, we describe an N,N-dimethyl formamide (DMF) vapour sensor fabricated by coating polyacrylonitrile (PAN) nanofiber structured on quartz crystal microbalance (QCM). The PAN nanofiber sensors with an average diameter of 225 nm to 310 nm were fabricated via electrospinning process with different mass deposition on QCM substrate. The nanostructured of PAN nanofiber offers a high specific surface area that improved the sensing performance of nanofiber sensors. Benefiting from that fine structure, and high polymer-solvent affinity between PAN and DMF, the development of DMF sensors presented good response at ambient temperature. Since there is no chemical reaction between PAN nanofiber and DMF vapour, weak physical interaction such absorption and swelling were responsible for the sensing behavior. The results are indicating that the response of PAN nanofiber sensors has more dependency on the nanofiber structure (specific surface area) rather than its mass deposition. The sensor also showed good stability after a few days sensing. These findings have significant implications for developing DMF vapour sensor based on QCM coated polymer nanofibers.