Locating Peaks in Proteomic Mass Spectral Data Using the Morel-Helmholtz Principle

Proteomics is a rapidly emerging field of research that will help identify and characterize the complex proteins that are responsible for the function of complex biological systems. For detection and identification of separated components, mass spectrometry is evolving to be the method-of-choice because of its high sensitivity and its ability to characterize the individual components. Analysis of biological sample will typically generate a protein mass fingerprint of the various constitutive components, with the component mass expressed as mass-to-charge (m/z) ratios and the relative abundance of each component as the peak height. However, reliably finding protein peaks with small relative abundance has been a difficult signal processing task, and many of the currently used techniques require many arbitrary parameters. This paper investigates the application of the Morel-Helmholtz principle, a single parameter method, to mass spectrometry signal processing. A comparison of the Morel-Helmholtz peak finding method with a thresholding method demonstrates that using the false alarm rate of one per interval will detect peaks that can optimally classify mass spectrometry data equally well as a well chosen threshold