Morlet Wavelet Filtering and Phase Analysis to Reduce the Limit of Detection for Thin Film Optical Biosensors

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–Pérot interference fringes characteristic of thin film reflectometric biosensors effectively filters out white noise and low-frequency reflectance variations. Subsequent calculation of the average difference in extracted phase between the filtered analyte and reference signals enables a significant reduction in the limit of detection (LOD). 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 the 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.

Porous Silicon-Based Aptasensors: Toward Cancer Protein Biomarker Detection

“…Thus, we apply a more accurate and robust approach by centering the noise floor around the intercept and calculating the LOD as the concentration at which the signal exceeds its intercept by 3.3*σ in the linear fit of the data. 47 It should be noted that AGR2 is present in a dimer−monomer equilibrium; 56 thus, we utilized the molecular mass of the dimer to convert between micrograms per liter and molar units. The apparent dissociation constant (K D ) was calculated based on a nonlinear regression of the obtained data utilizing the model for specific binding with a hill slope according to…”

“…The acquired reflectance spectra were processed using the recently introduced Morlet wavelet phase method. 47 The requisite steps are depicted in Figure S-3 and include the application of complex Morlet wavelet band-pass filtering to the reflectance versus wavenumber spectrum. The Morlet wavelet parameters were determined based on the width and center frequency of the dominant peak in the FFT, which were obtained using a rectangular window.…”

“…To reduce the latter, we apply a different signal processing technique instead of RIFTS, named Morlet wavelet phase. 47 In this method, Morlet wavelet convolution is applied to the PSi reflectance spectra to filter out typical noise signatures. This results in enhanced noise immunity and consequently lower LOD values.…”

“…31,45 Furthermore, we have recently presented a novel signal processing technique, which reduces system noise by applying Morlet wavelet convolution to filter spectra, resulting in an improved limit of detection. 47 Herein, we investigate different avenues for enhancing the sensitivity of PSi-based biosensors and address the three key benchmarks for developing a successful biosensor. 2 The selectivity of the biosensor is achieved by the immobilization of an anti-AGR2 aptamer 48 within the PSi and is analyzed by exposing the biosensor to nontarget proteins in a buffer solution and to a simulated pancreatic juice.…”

Development of Gas Sensor Based on Fractal Substrate Structures

“…AS sensors have been widely used in many fields, including environmental monitoring [1], food safety [2] and medical diagnostics [3]. Commonly used gas sensors are based on semiconducting [4], electrochemical [5] and biosensing principles [6]. However, the sensitivity and/or LoD (Limit of Detection) of many kinds of gas sensors is not sufficient for some applications, requiring more sophisticated analytical techniques to be used [7,8].…”