Utilizing the physics of microfluidic sensing schemes in signal processing to lower the detection limit

Lowering the limit of detection in chemical or biochemical analysis is the key to extending the application scope of many detection methods. Using the example of a detection scheme based on microfluidic isotachophoresis, we present an approach for lowering the limit of detection via signal processing by utilizing knowledge about the physics of the electrophoretic sample transport and of the imaging process. By cross-correlating pairs of noisy fluorescence images of an analyte focused by isotachophoresis, the electrophoretic velocity of the sample can be extracted even at low signal-to-noise ratios in a first step. Based on this velocity, a Galilean transformation is then performed on the whole set of images to align the fluorescence distributions of the sample and create a series of quasi-replicate measurements. Averaging over the transformed data leads to a significant reduction of Gaussian White Noise superposing the raw images, where the signal-to-noise ratio after processing scales with the number N of frames considered as √N. In this way, the limit of detection is lowered by about two orders of magnitude without any additional instrumentation. Bayesian inference is used to include the uncertainties of the measurements and signal processing in the final detection decision.