Nonlinear curve fitting
is by far the most accurate way to reduce noise and quantify peaks.
Many instruments come with software that only approximates the fitting
process by simply integrating the raw data numerically. When there
are shouldered or hidden peaks, a lot of noise or a significant background
signal, this can lead to the wrong results. (For example, a spectroscopy
data set may appear to have a peak with a 'raw' amplitude of 4,000
units -- but may have a shoulder peak that distorts the amplitude
by 1,500 units! This would be a significant error.)
PeakFit helps you separate overlapping peaks by statistically fitting
numerous peak functions to one data set, which can help you find even
the most obscure patterns in your data. The background can be fit
as a separate polynomial, exponential, logarithmic, hyperbolic or
power model. This fitted baseline is then subtracted before peak characterization
data (such as areas) is calculated, which gives much more accurate
results. And any noise (like you get with electrophoretic gels or
Raman spectra) that might bias raw data calculations is filtered simply
by the nonlinear curve fitting process. Nonlinear curve fitting is
essential for accurate peak analysis and accurate research.
PeakFit Offers Sophisticated
Data Manipulation
With PeakFit's visual
FFT filter, you can inspect your data stream in the Fourier domain
and zero higher frequency points -- and see your results immediately
in the time-domain. This smoothing technique allows for superb noise
reduction while maintaining the integrity of the original data stream.
PeakFit also includes an automated FFT method as well as Gaussian
convolution, the Savitzky-Golay method and the Loess algorithm for
smoothing. AI Experts throughout the smoothing options and other parts
of the program automatically help you to set many adjustments. And,
PeakFit even has a digital data enhancer, which helps to analyze your
sparse data. Only PeakFit offers so many different methods of data
manipulation.
Highly Advanced Baseline
Subtraction
PeakFit's non-parametric baseline fitting
routine easily removes the complex background of a DNA electrophoresis
sample. PeakFit can also subtract eight other built-in baseline equations
or it can subtract any baseline you've developed and stored in a file.
Full Graphical
Placement of Peaks
If PeakFit's
auto-placement features fail on extremely complicated or noisy data,
you can place and fit peaks graphically with only a few mouse clicks.
Each placed function has "anchors" that adjust even the most highly
complex functions, automatically changing that function's specific
numeric parameters. PeakFit's graphical placement options handle even
the most complex peaks as smoothly as Gaussians.
Publication-Quality
Graphs and Data Output
Every publication-quality
graph (see above) was created using PeakFit's built-in graphic engine
-- which now includes print preview and extensive file and clipboard
export options. The numerical output is customizable so that you see
only the content you want.
PeakFit Saves
You Precious Research Time
For most data
sets, PeakFit does all the work for you. What once took hours now
takes minutes – with only a few clicks of the mouse! It’s so easy
that novices can learn how to use PeakFit in no time. And if you have
extremely complex or noisy data sets, the sophistication and depth
of PeakFit’s data manipulation techniques is unequaled.
PeakFit Automatically
Places Peaks in Three Ways
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.
