Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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13
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RSC Advances, Volume 11, issue 48, pp. 30270-30282
Abstract
An array of carbon nanotube (CNT)-based sensors was produced for sensing selective biomarkers and evaluating breathomics applications with the aid of clustering and classification algorithms. We assessed the sensor array performance in identifying target volatiles and we explored the combination of various classification algorithms to analyse the results obtained from a limited dataset of exhaled breath samples. The sensor array was exposed to ammonia (NH3), nitrogen dioxide (NO2), hydrogen sulphide (H2S), and benzene (C6H6). Among them, ammonia (NH3) and nitrogen dioxide (NO2) are known biomarkers of chronic obstructive pulmonary disease (COPD). Calibration curves for individual sensors in the array were obtained following exposure to the four target molecules. A remarkable response to ammonia (NH3) and nitrogen dioxide (NO2), according to benchmarking with available data in the literature, was observed. Sensor array responses were analyzed through principal component analysis (PCA), thus assessing the array selectivity and its capability to discriminate the four different target volatile molecules. The sensor array was then exposed to exhaled breath samples from patients affected by COPD and healthy control volunteers. A combination of PCA, supported vector machine (SVM), and linear discrimination analysis (LDA) shows that the sensor array can be trained to accurately discriminate healthy from COPD subjects, in spite of the limited dataset.Description
Funding Information: Breath samples were collected (aer signed consent) from 11 volunteers aged 22–88 years. Among them, 7 volunteers suffer from COPD, while 4 were healthy control volunteers. All volunteers were recruited within a research project funded by the UniversitàCattolica del Sacro Cuore in the frame of the 2016–2018 D 3.2 Strategic Program “Anapnoi”. For each volunteer, several samples were collected on different days. An overall number of 52 samples were analysed. Subject characteristics including age, gender, COPD category as well as the number of tests carried out for each subject are shown in Table S1 (in the ESI†). Breath sampling was carried out in a disposable bag (volume = 0.6 liters), containing the sensor array, and inated by breath through a disposable plastic straw. This procedure took around 10–15 seconds until the bag was fully inated. We did not record signicant differences among volunteers during the bag ination phase, likely due to the reduced volume to ll and to the lack of any lter along the collection pipeline, which could hinder the bag ination step. The overall sensor exposure time inside the bag was set to 3 minutes, to let all sensors fully interact with the breath sample. Funding Information: G. D., S. P., M. C., S. F., P. M., and L. S. acknowledge funding by the UniversitàCattolica del Sacro Cuore in the frame of the 2016–2018 D 3.2 Strategic Program “Anapnoi”. L. S. and S. P. acknowledge funding by the Italian Ministry of Education, Universities, and Research (MIUR) through the PRIN 2017-N. 2017NYPHN8 (MADAM) program. F. S. F. and A. G. N. acknowledge Russian Foundation of Basic Research project no. 20-03-00804. I. I. B. acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 739570 (ANTARES). Publisher Copyright: © The Royal Society of Chemistry.
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Drera, G, Freddi, S, Emelianov, A V, Bobrinetskiy, I I, Chiesa, M, Zanotti, M, Pagliara, S, Fedorov, F S, Nasibulin, A G, Montuschi, P & Sangaletti, L 2021, 'Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease', RSC Advances, vol. 11, no. 48, pp. 30270-30282. https://doi.org/10.1039/d1ra03337a