Drera Giovanni, Freddi Sonia, Emelianov Aleksei V, Bobrinetskiy Ivan I, Chiesa Maria, Zanotti Michele, Pagliara Stefania, Fedorov Fedor S, Nasibulin Albert G, Montuschi Paolo, Sangaletti Luigi
Department of Mathematics and Physics, Università Cattolica del Sacro Cuore via dei Musei 41 25121 Brescia Italy
Surface Science and Spectroscopy Lab @ I-Lamp, Università Cattolica del Sacro Cuore, Brescia Campus Italy.
RSC Adv. 2021 Sep 10;11(48):30270-30282. doi: 10.1039/d1ra03337a. eCollection 2021 Sep 6.
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 (NH), nitrogen dioxide (NO), hydrogen sulphide (HS), and benzene (CH). Among them, ammonia (NH) and nitrogen dioxide (NO) 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 (NH) and nitrogen dioxide (NO), 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.
制备了一系列基于碳纳米管（CNT）的传感器，用于借助聚类和分类算法传感选择性生物标志物并评估呼吸组学应用。我们评估了传感器阵列在识别目标挥发性物质方面的性能，并探索了各种分类算法的组合，以分析从有限的呼出气体样本数据集中获得的结果。该传感器阵列暴露于氨（NH₃）、二氧化氮（NO₂）、硫化氢（H₂S）和苯（C₆H₆）中。其中，氨（NH₃）和二氧化氮（NO₂）是慢性阻塞性肺疾病（COPD）的已知生物标志物。在暴露于四种目标分子后，获得了阵列中各个传感器的校准曲线。根据与文献中现有数据的基准对比，观察到对氨（NH₃）和二氧化氮（NO₂）有显著响应。通过主成分分析（PCA）分析传感器阵列的响应，从而评估阵列的选择性及其区分四种不同目标挥发性分子的能力。然后将该传感器阵列暴露于慢性阻塞性肺疾病患者和健康对照志愿者的呼出气体样本中。主成分分析（PCA）、支持向量机（SVM）和线性判别分析（LDA）的组合表明，尽管数据集有限，但该传感器阵列仍可经过训练准确区分健康人和慢性阻塞性肺疾病患者。
