Optimization of Metal Oxide Nanosensors and Development of a Feature Extraction Algorithm to Analyze VOC Profiles in Exhaled Breath-Reference-Cited by-同舟云学术

Optimization of Metal Oxide Nanosensors and Development of a Feature Extraction Algorithm to Analyze VOC Profiles in Exhaled Breath

Published:2023-08-01 Issue:15 Volume:23 Page:16571-16578
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Maciel Mariana¹^ORCID,Sankari Safiya²,Woollam Mark³,Agarwal Mangilal⁴^ORCID

Affiliation:

1. Department of Mechanical and Energy Engineering, Integrated Nanosystems Development Institute, Indiana University-Purdue University, Indianapolis, IN, USA

2. Integrated Nanosystems Development Institute, Indiana University--Purdue University, Indianapolis, IN, USA

3. Department of Chemistry and Chemical Biology, Integrated Nanosystems Development Institute, Indiana University--Purdue University, Indianapolis, IN, USA

4. Department of Mechanical and Energy Engineering and the Department of Chemistry and Chemical Biology, Integrated Nanosystems Development Institute, Indiana University-Purdue University, Indianapolis, IN, USA

Funder

Medical Chemical, Biological, Radiological and Nuclear (CBRN) Defense Consortium in collaboration with the Defense Threat Reduction Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10198840/10168124.pdf?arnumber=10168124

Reference44 articles.

1. Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review

2. Review of the algorithms used in exhaled breath analysis for the detection of diabetes

3. An eNose-based method performing drift correction for online VOC detection under dry and humid conditions

4. A Novel Multisensor Detection System Design for Low Concentrations of Volatile Organic Compounds

5. Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution

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