{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:59:23Z","timestamp":1774292363698,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the application of machine learning for virtual sensing has revolutionized the monitoring and management of information. In particular, electrochemical sensors generate large amounts of data, allowing the application of complex machine learning\/AI models able to (1) reproduce the measured data and (2) predict and manage faults in the measuring sensor. In this work, data-driven models based on an autoregressive model and an artificial neural network have been identified and used to (i) evaluate sensor redundancy and (ii) predict and manage faults in the context of electrochemical sensors for the measurement of ethanol. The approach shows encouraging results in terms of both performance and sensitivity analyses, allowing for the reconstruction of the values measured by two sensors in a series of six sensors with different dopant levels and to reproduce their values after a fault.<\/jats:p>","DOI":"10.3390\/s24051396","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T03:30:26Z","timestamp":1708572626000},"page":"1396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Data-Driven Virtual Sensing for Electrochemical Sensors"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6650-2543","authenticated-orcid":false,"given":"Lucia","family":"Sangiorgi","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-9391","authenticated-orcid":false,"given":"Veronica","family":"Sberveglieri","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 42124 Reggio Emilia, Italy"},{"name":"Nano Sensor System srl (NASYS), Via Alfonso Catalani, 42124 Reggio Emilia, Italy"}]},{"given":"Claudio","family":"Carnevale","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7923-6668","authenticated-orcid":false,"given":"Sabrina","family":"De Nardi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4621-0658","authenticated-orcid":false,"given":"Estefan\u00eda","family":"Nunez-Carmona","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. 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