getPeaksCov - Probabilistic peak detection of first order chromatographic or time-series data. For a provided pair of vectors (time and intensity) the function uses probabilistic reasoning to evaluate an exhaustive set of (realistic) peak models in a windowed region. Prior probabilities are evaluated by the method published by Davis and Giddings 1983. Probabilistic reasoning is used to estimate the posterior probability that a given point is affected by a chromatographic peak.

Input: p = getPeaksConv(x, y, bandWidth, ResSigma, ALPHA, MAX_PEAKS_PER_ROW, verbosity_flag, external_models)

x : Time index of each measurement y : Intensity value of each measurement bandWidth: The standard deviation of a prototypical Gaussian peak for the chromatographic system expressed in terms of retention time ResSigma: Standard deviation of a blank measurement (baseline noise) MAX_PEAKS_PER_ROW (optional): The boound on number of peaks allowed to overlap in a chromatographic space define by 2 plates in the chromatographic system. Default = 2 ALPHA (optional): The saturation of the chromatogram [1 > ALPHA > 0] Default = 0.25 external_models (optional): The contents of modelHolder normally generated per parameter set. For batch processing similar chormatograms, it can save a lot of time to generate these once and pass the models in. Default = generated from other parameters verbosity_flag: 0 for no output and no waitbars 1 for waitbars only 2 verbose mode

Output: p : Estiamted posterior probibility that the point at p is affected by a chromatographic peak

Example usage (simplest case): p = getPeaksConv(retention_time_vector, intenstiy_vector, 2.5, 1.2e-5); Example usage (fully specified case): p = getPeaksConv(retention_time_vector, intenstiy_vector, 2.5, 1.2e-5, 0.3, 3, 2, external_models);

If this software is useful to your academic work, please cite our publication in lieu of thanks:

%% Lopatka M., Vivó-Truyols G., Sjerps M.J., "Probabilistic peak detection %% for first-order chromatographic data." Analitica Chimica Acta. 2014. %% DOI: 10.1016/j.aca.2014.02.015

Author: Martin Lopatka [email protected] Created: 29th August, 2013 Gabriel Vivó-Truyols [email protected] Revised: 23rd April, 2015 Maintained by Martin Lopatka