PeakFit
Find the most obscure patterns and separate overlapping peaks by statistically fitting numerous peak functions to one data set. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model. PeakFit uses three procedures to automatically place hidden peaks; while each is a strong solution, one method may work better with some data sets than the others. • The Residuals procedure initia…
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Find the most obscure patterns and separate overlapping peaks by statistically fitting numerous peak functions to one data set. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model.
PeakFit uses three procedures to automatically place hidden peaks; while each is a strong solution, one method may work better with some data sets than the others.
• The Residuals procedure initially places peaks by finding local maxima in a smoothed data stream. Hidden peaks are then optionally added where peaks in the residuals occur.
• The Second Derivative procedure searches for local minima within a smoothed second derivative data stream. These local minima often reveal hidden peaks.
• The Deconvolution procedure uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm. A successfully deconvolved spec-trum will consist of “sharpened” peaks of equivalent area. The goal is to enhance the hidden peaks so that each represents a local maximum.
