Scientific Paper
Automating data classification for label-free point-of-care biosensing in real complex samples
Summary
A step-by-step procedure that automatically decides whether a pathogen is present from the raw signal of a simple point-of-care QCM biosensor in unprocessed samples. Filtering, data selection, baseline de-drifting and result calculation are tuned on E. coli O157:H7 and SARS-CoV-2 data in food and cell culture media, reaching 80.8% and 90.9% correct detection without heavy machine learning.
Summary written by the openQCM team. The original abstract is on the publisher’s page.
Related work.
- An approach to compare performance of surgical masks for fighting against the COVID-19 pandemic 2022
- Rapid SARS-CoV-2 Detection Using High-Sensitivity Thickness Shear Mode Sensors 2024
- Evaluation of Dynamic Viscosity in the 10 MHz Range Using a General-Purpose Quartz Crystal Microbalance 2022
- Solvent effects of N,N-dimethylformamide and methanol on mass spectrometry imaging by tapping-mode scanning probe electrospray ionization 2023
- Rheology of the Electric Double Layer in Electrolyte Solutions 2020
- QCM sensörlerinin partikül madde konsantrasyonunu ölçmek için geliştirilmesi 2024
New openQCM papers, by email.
A selection of new publications measured with openQCM, with software releases and application notes. A few emails a year.
Browse all papers.
Hundreds of peer-reviewed publications cite openQCM. Search full-text and filter by instrument, year, journal or topic.