Detecting Isotachophoresis Zones Hidden in Noise Using Signal Processing

Lowering the limit of detection in chemical or biochemical analysis is key to extending the application scope of sensing schemes. Usually, this is related to an increased instrumentation effort, which in turn precludes many commercial applications. We demonstrate that the signal-to-noise ratio of isotachophoresis-based microfluidic sensing schemes can be substantially increased merely by postprocessing of recorded signals. This becomes possible by exploiting knowledge about the physics of the underlying measurement process. The implementation of our method is based on microfluidic isotachophoresis and fluorescence detection, for which we take advantage of the physics of electrophoretic sample transport and the structure of noise in the imaging process. We demonstrate that by processing only 200 images, the detectable concentration, compared to the detection from a single image, is already lowered by 2 orders of magnitude without any additional instrumentation effort. Furthermore, we show that the signal-to-noise ratio is proportional to the square root of the number of fluorescence images, which leaves room for further lowering of the detection limit. In the future, our results could be relevant for various applications where the detection of minute sample amounts plays a role.