Utilize three different procedures to automatically identify hidden peaks in data sets. Each procedure offers a strong solution, but their effectiveness may vary depending on the specific dataset. The first procedure, known as Residuals, initially identifies peaks by identifying local maxima in a smoothed data stream. It also has the ability to add hidden peaks based on the presence of peaks in the residuals. The second procedure, called Second Derivative, searches for local minima in a smoothed second derivative data stream. These local minima often indicate the presence of hidden peaks. The third procedure, known as Deconvolution, employs a Gaussian response function and Fourier deconvolution/filtering techniques to detect hidden peaks.
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.
Main Features:
- Sophisticated Data Manipulation
- Highly Advanced Baseline Subtraction
- Publication-Quality Graphs and Data Output
- Saves You Precious Research Time
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