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Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis./
作者:	Miller, Tiffany C.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	141 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28644812
ISBN:	9798535550435

Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis.
Miller, Tiffany C.

Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 141 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--University of South Florida, 2021.

This item must not be sold to any third party vendors.

Detection of specific target biomarkers is a critical step for electronic nose technologies in breath analysis. Electronic nose technologies have been successfully demonstrated in the air quality, meat processing, and the citrus industries, as well as cancer clinical trials. The novel design, assembly, and implementation of a test bed prototype of an electronic nose system is introduced in this Dissertation. To verify the feasibility of a test bed prototype design, a Corona Virus Disease 2019 (COVID-19) breath simulation mixture of alcohol, acetone, and carbon monoxide was presented to the test bed prototype in the laboratory. The COVID-19 breath simulation mixture was developed from a literature review and from preliminary gas chromatography ion mobility spectrometry data obtained from parallel COVID-19 breath studies based in Edinburgh and Dortmund. As a result, the test bed prototype was fit with metal oxide semiconductor (MOS) MQ-2 and MQ-135 gas sensors due to their availability, affordability, high sensitivity, high selectivity, and low recovery time properties related to their respective target gasses. The sensors were configured to show a detection range for alcohol, acetone, and carbon monoxide in part per million (ppm) level.The problem with the use of MOS based sensors in electronic nose technologies is system instability, due to a result of gradual drifts in the output responses of the MQ-2 and MQ-135 sensors. This instability directly pertains to the baseline output response of the MQ-2 and MQ-135 sensors not being accurately obtained. A main focus of this Dissertation analyzes a signal processing approach involving the manipulation of the gas sensor output response baseline using Orthogonal Signal Correction (OSC) in an attempt to decrease the negative influence of gradual drifts associated with the output response of the MQ-2 and MQ-135 sensors. The proposed signal processing approach of applying baseline manipulation with OSC was performed to eliminate and/or minimize the drift effects of the MQ-2 and MQ-135 gas sensors responding to varying concentrations of the COVID-19 breath simulated mixture of methyl alcohol, acetone, and carbon monoxide (CO). A Partial Least Squares (PLS) regression model is then employed to determine the approximate concentration of gas detected in ppm after the corrected datasheet undergoes baseline manipulation. This Dissertation will detail how the proposed signal processing approaches increase the reproducibility of the sensor output responses by enhancing the regression model stability while maintaining an accuracy suitable for breath analysis applications.

ISBN: 9798535550435Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Biomarker

Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis.
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245 1 0 $a Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 141 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Morgera, Salvatore D.
502 $a Thesis (Ph.D.)--University of South Florida, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Detection of specific target biomarkers is a critical step for electronic nose technologies in breath analysis. Electronic nose technologies have been successfully demonstrated in the air quality, meat processing, and the citrus industries, as well as cancer clinical trials. The novel design, assembly, and implementation of a test bed prototype of an electronic nose system is introduced in this Dissertation. To verify the feasibility of a test bed prototype design, a Corona Virus Disease 2019 (COVID-19) breath simulation mixture of alcohol, acetone, and carbon monoxide was presented to the test bed prototype in the laboratory. The COVID-19 breath simulation mixture was developed from a literature review and from preliminary gas chromatography ion mobility spectrometry data obtained from parallel COVID-19 breath studies based in Edinburgh and Dortmund. As a result, the test bed prototype was fit with metal oxide semiconductor (MOS) MQ-2 and MQ-135 gas sensors due to their availability, affordability, high sensitivity, high selectivity, and low recovery time properties related to their respective target gasses. The sensors were configured to show a detection range for alcohol, acetone, and carbon monoxide in part per million (ppm) level.The problem with the use of MOS based sensors in electronic nose technologies is system instability, due to a result of gradual drifts in the output responses of the MQ-2 and MQ-135 sensors. This instability directly pertains to the baseline output response of the MQ-2 and MQ-135 sensors not being accurately obtained. A main focus of this Dissertation analyzes a signal processing approach involving the manipulation of the gas sensor output response baseline using Orthogonal Signal Correction (OSC) in an attempt to decrease the negative influence of gradual drifts associated with the output response of the MQ-2 and MQ-135 sensors. The proposed signal processing approach of applying baseline manipulation with OSC was performed to eliminate and/or minimize the drift effects of the MQ-2 and MQ-135 gas sensors responding to varying concentrations of the COVID-19 breath simulated mixture of methyl alcohol, acetone, and carbon monoxide (CO). A Partial Least Squares (PLS) regression model is then employed to determine the approximate concentration of gas detected in ppm after the corrected datasheet undergoes baseline manipulation. This Dissertation will detail how the proposed signal processing approaches increase the reproducibility of the sensor output responses by enhancing the regression model stability while maintaining an accuracy suitable for breath analysis applications.
590 $a School code: 0206.
650 4 $a Electrical engineering. $3 649834
650 4 $a Influenza. $3 924085
650 4 $a Biomarkers. $3 2205735
650 4 $a Viral infections. $3 3540521
650 4 $a Coronaviruses. $3 894828
650 4 $a Tuberculosis. $3 808530
650 4 $a Chronic obstructive pulmonary disease. $3 3558078
650 4 $a Odors. $3 676225
650 4 $a Pandemics. $3 3464080
650 4 $a Sensors. $3 3549539
650 4 $a COVID-19. $3 3554449
653 $a Biomarker
653 $a COVID-19
653 $a Diagnostic
653 $a ENose
653 $a Gas sensor
653 $a SARS-CoV-2
690 $a 0544
710 2 $a University of South Florida. $b Electrical Engineering. $3 1678952
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0206
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28644812