Frequency-Amplitude Dual-Parameter-Modulated on a Single WE-QCM-D for VOCs Discrimination and Analysis

The complexity of in situ volatile organic compounds (VOCs) detection demands high selectivity and anti-interference capability of electronic noses (E-noses). Multivariant virtual sensor array (VSA) is a new generation of E-noses under research to overcome the insufficient VOCs selectivity limitation of existing electronic noses and alleviate the issue of sensor drifts. In this paper, a frequency-amplitude dual-parameter modulation strategy is presented on a wireless electrodeless quartz crystal microbalance with dissipation (WE-QCM-D) to realize the recognition and analysis of VOC mixtures and complex real-world analytes with a single multivariant VSA E-nose, by probing the gas dynamic sorption in a sensitive film at various scales of oscillating shear displacements and obtaining multiple partially independent responses to VOCs. Ten VOC analytes from alcohols, esters, and aromatic hydrocarbons were classified with accuracies of above 95% for both interclass and intraclass discriminations. Additionally, a discriminating accuracy of 95% has been achieved on VOC mixtures, and their component concentrations were predicted with coefficients of determination above 0.9. For practical testing, the system was exposed to the headspace VOCs of banana, pineapple, and mango under different concentrations. It recognized different fruits and identified the ripen state of bananas based on the detection of their volatiles. The dual-parameter modulated WE-QCM-D paves a promising multivariant way to realize online real-time VOCs monitoring with high performance on selectivity and quantitative analysis.

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Publication Details

Journal
ACS Sensors
Published
2025-12-22
DOI
https://doi.org/10.1021/acssensors.5c03113
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
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article

Frequency-Amplitude Dual-Parameter-Modulated on a Single WE-QCM-D for VOCs Discrimination and Analysis

Y. Wang, Jun Fu, Ruifen Hu, Guang Li et al.
ACS Sensors
Advanced Chemical Sensor Technologies
article

Frequency-Amplitude Dual-Parameter-Modulated on a Single WE-QCM-D for VOCs Discrimination and Analysis

Y. Wang, Jun Fu, Ruifen Hu, Guang Li, Nan Li, Changle Pei, Adrian Carl Stevenson, Muhammad Hamza Nadeem, Nan Xu
article en

Abstract

The complexity of in situ volatile organic compounds (VOCs) detection demands high selectivity and anti-interference capability of electronic noses (E-noses). Multivariant virtual sensor array (VSA) is a new generation of E-noses under research to overcome the insufficient VOCs selectivity limitation of existing electronic noses and alleviate the issue of sensor drifts. In this paper, a frequency-amplitude dual-parameter modulation strategy is presented on a wireless electrodeless quartz crystal microbalance with dissipation (WE-QCM-D) to realize the recognition and analysis of VOC mixtures and complex real-world analytes with a single multivariant VSA E-nose, by probing the gas dynamic sorption in a sensitive film at various scales of oscillating shear displacements and obtaining multiple partially independent responses to VOCs. Ten VOC analytes from alcohols, esters, and aromatic hydrocarbons were classified with accuracies of above 95% for both interclass and intraclass discriminations. Additionally, a discriminating accuracy of 95% has been achieved on VOC mixtures, and their component concentrations were predicted with coefficients of determination above 0.9. For practical testing, the system was exposed to the headspace VOCs of banana, pineapple, and mango under different concentrations. It recognized different fruits and identified the ripen state of bananas based on the detection of their volatiles. The dual-parameter modulated WE-QCM-D paves a promising multivariant way to realize online real-time VOCs monitoring with high performance on selectivity and quantitative analysis.

ACS SensorsVol. 11(1)
University of Cambridge (GB), Systems Control (United States) (US), Quzhou University (CN), PRG S&Tech (South Korea), Zhejiang Gongshang University (CN), Zhejiang University (CN)
Reduced inequalities
Openalex Percentile: Top 43%
Advanced Chemical Sensor Technologies
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