Rethinking Data Collection and Signal Processing. 1. Real-Time Oversampling Filter for Chemical Measurements

Abstract

Minimizing noise in chemical measurements is critical to achieve low limits of detection and accurate measurements. We describe a real-time oversampling filter that offers a method to reduce stochastic noise in a time-dependent chemical measurement. The power of this technique is demonstrated in its application to the separation of dopamine and serotonin by micellar electrokinetic chromatography with amperometric detection. Signal-to-noise ratios were increased by almost an order of magnitude, allowing for limits of detection of 100 and 120 amol, respectively. Real-time oversampling filters can be implemented using simple software algorithms and require no change to existing experimental apparatus. The application is not limited to analytical separations, and this technique can be used to improve the signal-to-noise ratio in any experiment where the necessary sampling rate is less than the maximum sampling rate of the analog-to-digital converter. Theory, implementation, and the performance of this filter are described. We propose that this technique should be the default mode of operation for an analog-to-digital converter