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

Area of Science:

• Chemical Sensors
• Analytical Chemistry
• Materials Science

Background:

• Detecting volatile organic compounds (VOCs) in real-time requires high selectivity and anti-interference capabilities, which current electronic noses (E-noses) struggle to provide.
• Sensor drift and limited selectivity are significant challenges in existing E-nose technologies.
• Multivariant virtual sensor arrays (VSAs) represent a next-generation E-nose approach to address these limitations.

Purpose of the Study:

• To develop a novel frequency-amplitude dual-parameter modulation strategy for a wireless electrodeless quartz crystal microbalance with dissipation (WE-QCM-D).
• To enhance the selectivity and anti-interference capabilities of a single multivariant VSA E-nose for VOC detection.
• To demonstrate the system's effectiveness in recognizing VOC mixtures, analyzing complex real-world samples, and predicting component concentrations.

Main Methods:

• Implementation of a frequency-amplitude dual-parameter modulation on a WE-QCM-D device.
• Probing gas dynamic sorption within a sensitive film across various oscillating shear displacements.
• Acquisition of multiple, partially independent responses to VOCs for multivariant analysis.

Main Results:

• Classification accuracy exceeding 95% for ten different VOC analytes (alcohols, esters, aromatic hydrocarbons) in both interclass and intraclass discrimination.
• Discrimination accuracy of 95% for VOC mixtures, with component concentration prediction achieving coefficients of determination above 0.9.
• Successful identification of different fruits (banana, pineapple, mango) and determination of banana ripeness based on headspace VOC analysis.

Conclusions:

• The dual-parameter modulated WE-QCM-D offers a promising multivariant approach for online, real-time VOC monitoring.
• This method significantly improves selectivity and quantitative analysis performance for complex VOC detection.
• The developed system demonstrates high potential for practical applications in environmental monitoring and food quality assessment.