A Multimatrix E-Nose With Optimal Multiranged AFE Circuit for Human Volatilome Fingerprinting

• Published in: IEEE sensors journal Vol. 25; no. 5; pp. 7722 - 7732
• Main Authors: Radogna, Antonio Vincenzo, Grassi, Giuseppe, D'Amico, Stefano, Siciliano, Pietro Aleardo, Forleo, Angiola, Capone, Simonetta
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog circuits; Analog front-end (AFE); Biological properties; biomedical electronics; Body fluids; Cancer; chemical sensors; Classification; Effectiveness; electronic nose (e-nose); Electronic noses; Fingerprint recognition; Fingerprinting; Fingerprints; Gas detectors; Gas mixtures; Gas sensors; Metal oxides; Optimization; Sensor arrays; Sensor phenomena and characterization; sensor systems and applications; Sensors; VOCs; Volatile organic compounds; Voltage
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3343762

Since hundreds of volatile organic compounds (VOCs) produced by cell metabolism and released into the blood are excreted through exhaled breath or body fluids, the volatile composition (volatilome) of human samples reflects a subject's state of health and early signals of any abnormal deviation from healthy to disease. The chemical volatilomic profile of biological matrices can be transduced in a digital fingerprint by low-cost and easy-to-use electronic nose (e-nose) devices based on gas sensor arrays. The e-noses can be used to aid clinical diagnosis supporting conventional diagnostic methods that sometimes require expensive or invasive medical procedures and delays in diagnoses. In this article, an e-nose devoted to human volatilome fingerprinting is presented. The device, code-named SPYROX, adopts an array of 8 metal-oxide (MOX) gas sensors and it can analyze response signals from different matrices (multimatrix samples), dealing with exhaled breath and headspace analysis of human biological samples. While other works in literature neglect the design of the interface circuit, here an optimal multiranged analog front-end (AFE) circuit is proposed. It aims to the optimization of the read-out sensitivity which, ultimately, leads to accurate training datasets and, consequently, to high classification scores. Finally, the efficacy of the device is proved by testing both chemical standards and mixtures. As a result, a classification accuracy of 100% is achieved with a linear discriminant model. The experimental results give proof of the system's efficacy in the fingerprint analysis of complex gas mixtures, which are typical of human volatilome.