Automated Gaussian Smoothing and Peak Detection Based on Repeated Averaging and Properties of a Spectrum's Curvature

Abstract

In this paper, we address the two problems of automated smoothing and peak detection in spectral data analysis. We introduce the concept of triplet significance, and propose a repeated averaging approach, which is able to find a balance between noise reduction and signal preservation based on properties of a spectrum's curvature. For evaluation purposes, multiple spectra are simulated at different levels of resolution and different distances between peaks for varying amplitudes of uniformly distributed noise. The results empirically show that the proposed methodology outperforms existing approaches based on local maximum detection or the lag-one autocorrelation coefficient. © Springer-Verlag Berlin Heidelberg 2010.