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

Giovanni Drera, Sonia Freddi, Aleksei V. Emelianov, Ivan I. Bobrinetskiy, Maria Chiesa, Michele Zanotti, Stefania Pagliara, Fedor S. Fedorov, Albert G. Nasibulin, Paolo Montuschi, Luigi Sangaletti*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
11 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)30270-30282
Number of pages13
JournalRSC Advances
Volume11
Issue number48
Early online date10 Sep 2021
DOIs
Publication statusE-pub ahead of print - 10 Sep 2021
MoE publication typeA1 Journal article-refereed

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