Spectral peak detection algorithms are often difficult to automate because they either rely on somewhat arbitrary rules, or are tuned to specific spectral peak properties. One popular approach detects peaks where signal intensities exceed some threshold. This threshold is typically set arbitrarily above the noise level or manually by the user. Intensity threshold-based methods can be sensitive to baseline variations and signal intensity. Another popular peak detection approach relies on matching the spectral intensities to a reference peak shape. This approach can be very sensitive to baseline changes and deviations from the reference peak shape. Such methods can be significantly challenged by modern analytical instrumentation where the baseline tends to drift, peaks of interest may have a low signal to noise (S/N) ratio, and no well-defined reference peak shape is available.
We present a new approach for spectral peak detection that is designed to be generic and easily automated. Employing a histogram-based model for spectral intensity, peaks are detected by comparing the estimated variance of observations (the xaxis of the spectrum) to the expected variance when no peak is present inside some window of interest. We compare an implementation of this approach to two existing peak detection algorithms using a series of simulated spectra.