Signal enhancement of potentiometric stripping analysis using digital signal processing

Abstract

Four digital signal processing methods, moving average smoothing, polynomial smoothing, rectangular low pass filtering and exponential low pass filtering have been compared in this study of potentiometric stripping analysis (PSA) data. For the filtering methods, a new procedure is introduced based on treating the potential coordinate as mathematically equivalent to a time coordinate to transform data to the frequency domain. After Fourier transformation, each method has yielded signal-to-noise ( S N ) improvement over the original data, for example exponential low pass filtering providing a 2.3 times S N enhancement. Peak area measurement was unaffected by the two smoothing methods, while an undesirable increase in peak area was observed in the two filtering methods. A linear peak area-concentration relationship was retained in all cases. The original peak height calibration is poorly correlated linearly, although the complex curve is reproducible. However, after digital signal processing, peak height as the measuring parameter gave a good linear relationship to concentration. The filtering approach which allows optimum filtering and best peak resolution was readily established. The resolution of overlapping potential peaks, using copper and bismuth peaks as the example, was best improved using the rectangular low pass filtering method.