Signal Processing Techniques to Reduce the Limit of Detection for Thin Film Biosensors

TL;DR

This paper demonstrates that applying complex Morlet wavelet convolution to thin film biosensor signals effectively reduces noise, significantly lowering the detection limit and enhancing the sensor's clinical and environmental diagnostic capabilities.

Contribution

The study introduces a novel signal processing approach using wavelet convolution and phase difference analysis to improve detection limits of optical biosensors.

Findings

Wavelet convolution filters out white noise and low frequency variations.

Significant reduction in limit of detection achieved.

Method validated on sensors from two laboratories.

Abstract

The ultimate detection limit of optical biosensors is often limited by various noise sources, including those introduced by the optical measurement setup. While sophisticated modifications to instrumentation may reduce noise, a simpler approach that can benefit all sensor platforms is the application of signal processing to minimize the deleterious effects of noise. In this work, we show that applying complex Morlet wavelet convolution to Fabry-P\'erot interference fringes characteristic of thin film reflectometric biosensors effectively filters out white noise and low frequency reflectance variations. Subsequent calculation of an average difference in phase between the filtered analyte and reference signals enables a significant reduction in the limit of detection (LOD) enabling closer competition with current state-of-the-art techniques. This method is applied on experimental data sets of thin film porous silicon sensors (PSi) in buffered solution and complex media obtained from two different laboratories. The demonstrated improvement in LOD achieved using wavelet convolution and average phase difference paves the way for PSi optical biosensors to operate with clinically relevant detection limits for medical diagnostics, environmental monitoring, and food safety.