An Investigation into the Impact of Impedance Measurement Parameters on the Limit of Detection of QCM-D using Machine Learning Model Chaining

Impedance spectroscopy is an appropriate measurement method for Quartz crystal microbalance with energy dissipation (QCM-D) monitoring, especially for machine learning applications, given the vast amount of information it can provide. When QCM is used in a liquid medium for biosensing, it responds t...

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Bibliographic Details
Published in:IEEE sensors journal 2024-12, p.1-1
Main Authors: Kirimli, Ceyhun E., Elgun, Elcim, Yuksel, Mehmet Mert, Tugtag, Selin Yagmur
Format: Article
Language:English
Subjects:
Biosensors
Experimental Design
Frequency measurement
Harmonic analysis
Impedance
Impedance measurement
Impedance Spectroscopy
Machine learning
Optimization
Quartz Crystal Microbalance with Energy Dissipation
Resonance
Resonant frequency
Sensors
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Summary:Impedance spectroscopy is an appropriate measurement method for Quartz crystal microbalance with energy dissipation (QCM-D) monitoring, especially for machine learning applications, given the vast amount of information it can provide. When QCM is used in a liquid medium for biosensing, it responds to mass change, and the viscoelastic properties of both the medium and the film deposited on the electrode surface. It has been previously observed that the limit of detection (LOD) experiments employing QCM may be increased by at least 12 folds, by an application of machine learning (ML) assisted optimization of impedance measurement parameters while enabling a reduction of the number of experiments by 10 fold. In this study, ML methodologies are employed to quantify how a selection of such measurement parameters is possible and affects the calculated viscoelastic parameters of the bulk fluid and thickness along with the viscosity of Bovine Serum Albumin (BSA) thin films adsorbed on gold electrodes. Results indicate that the limit of detection (LOD) for bulk fluid viscosity and thickness of BSA thin films can vary up to 6 and 3 fold respectively, depending on the chosen measurement parameters. By implementing this machine learning framework, viscoelastic modeling accuracy in complex media and thin film applications can be significantly improved through impedance spectroscopy, thus resulting in an increased overall sensitivity in QCM biosensing.
ISSN:1530-437X
DOI:10.1109/JSEN.2024.3511274