  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.
