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 Bobrinetskiy, Maria Chiesa, Michele Zanotti, Stefania Pagliara, Fedor S. Fedorov, Albert G. Nasibulin, Paolo Montuschi, L. Sangaletti
Abstract
), 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.