Automating Data Analysis for Point-of-Care Label-Free Surface-Based Affinity Biosensors Dealing with Complex Biological Samples: Escherichia Coli O157: H7 and Sars-Cov-2 Case Studies

Surface-based affinity biosensors offer a promising avenue for point-of-care (POC) detection of pathogens in real-world samples. While laboratory-based devices commonly employ various techniques to compensate for noise, signal drifts, fluidic artifacts, and other system imperfections, their POC counterparts aiming at providing simple cost-effective detection platforms for field use, often lack these qualities. This paper addresses this gap by introducing a procedure for automatic classification of pathogen presence in unprocessed liquids from direct detection data measured by a simple POC-relevant quartz crystal microbalance sensor device. By considering the nature of the sensor signal and the sources of its imperfections in real-world media, a straightforward procedure integrates “classical” analytical tools (filtering, data selection, baseline de-drifting, and result calculation) in successive steps to automate sample classification without the need for extensive machine learning. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively. Furthermore, we analyse the sensitivity of the routine to variations of input parameters and with examples discuss the key factors influencing the accuracy of the overall procedure. The results show that the developed method exhibits exceptional robustness across different biosensing assays and complex real-world media, highlighting its promising broader applicability in point-of-care diagnostics.