Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-05, Vol.20 (9), p.2666
• Main Authors: Kwiatkowski, Andrzej, Chludziński, Tomasz, Saidi, Tarik, Welearegay, Tesfalem Geremariam, Jaimes-Mogollón, Aylen Lisset, El Bari, Nezha, Borys, Sebastian, Bouchikhi, Benachir, Smulko, Janusz, Ionescu, Radu
• Format: Article
• Language: English
• Subjects: AC fluctuation measurements; Adult; Aged; Alkanes; Biopsy; breath analysis; Breath Tests; chemical gas sensors; Chemical sensors; Classification; DC resistance measurements; diagnosis; Diagnostic systems; Discriminant analysis; echinococcosis; Echinococcosis - diagnosis; Electronics; Ethanol; Female; Function analysis; Gas sensors; Gases; Gold; Humans; Lipids; Male; Metabolism; Metabolites; Metal Nanoparticles; Methods; Middle Aged; Nanoparticles; Nanowires; Noise; Parasitic diseases; Sensor arrays; Sensors; VOCs; Volatile Organic Compounds; Volunteers
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20092666

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors' responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.