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Peakfit's state - of -the - art nonlinear curve fitting is essential for accurate peak analysis and conclusive findings. PeakFit separates and analyzes nonlinear peak data better, more accurately and more conveniently than your lab instrument. Nonlinear curve fitting is by far the most accurate way to reduce noise and quantify 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 three Procedures are...
Residuals - initially places peaks by finding local maxima in a smoothed data stream
Second Derivative - searches for local minima within a smoothed second derivative data stream
Deconvolution - uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm.
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.
PeakFit is the automatic choice for Spectroscopy, Chromatography or Electrophoresis. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments.PeakFit can even deconvolve your spectral instrument response so that you can analyze your data without the smearing that your instrument introduces. By Using 82 nonlinear peak models to choose from, you're almost guaranteed to find the best equation for your data.
PeakFit includes 18 different nonlinear spectral application line shapes, including the Gaussian, the Lorentzian, and the Voigt, and even a Gaussian plus Compton Edge model for fitting Gamma Ray peaks. As a product of the curve fitting process, PeakFit reports amplitude (intensity), area, center and width data for each peak. Overall area is determined by integrating the peak equations in the entire model.
The Voigt function is a convolution of both the Gaussian and Lorentzian functions. Most analysis packages that offer a Voigt function use an approximation with very limited precision. PeakFit actually uses a closed-form solution to precisely calculate the function analytically. PeakFit has four different Voigt functions, so you can fit the parameters you're most interested in, including the individual widths of both the Gaussian and Lorentzian components, and also the amplitude and area of the Voigt function. PeakFit's precise calculation of the Voigt function is crucial to the accuracy of your analysis.

Features
Nonlinear Curve Fitting
Full Graphical Placement of Peaks
Highly Advanced Baseline Subtraction
PeakFit Saves You Precious Research Time
Publication-Quality Graphs and Data Output
PeakFit Offers Sophisticated Data Manipulation
PeakFit Automatically Places Peaks in Three Ways
Data Input
Data Preparation
Peak Autoplacement
Non - Linear Curve Fitting
Output and Export Options
Fitting Multiple Simultaneous Gaussian Functions
SYSTEM REQUIREMENTS
486 Processor or higher
Windows 95 and above
8 MB RAM required (12 MB or more recommended)
5MB hard disk space

TOPICS


Why Should You Use Nonlinear Curve Fitting?
PeakFit Offers Sophisticated Data Manipulation
Highly Advanced Baseline Subtraction
Full Graphical Placement of Peaks and...
Publication-Quality Graphs and Data Output
PeakFit Saves You Precious Research Time
PeakFit Automatically Places Peaks in Three Ways


Why Should You Use Nonlinear Curve Fitting?

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.



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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.


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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.
Built-In Nonlinear Functions (83 total)

Spectroscopy (18): Gauss Amp, Gauss Area, Lorentz Amp, Lorentz Area, Voigt Amp, Voigt Area, Voigt Amp Approx, Voigt Amp G/L, Voigt Area G/l, Gauss Cnstr Amp, Gauss Cnstr Area, Pearson VII Amp, Pearson VII Area, Gauss+Lor Area, Gauss*Lor, Gamma Ray, Compton Edge.
Chromatography (8): HVL, NLC, Giddings, EMG, GMG, EMG+GMG, GEMG, GEMG 5-parm.
Statistical (31): Log Normal Amp, Log Normal Area, Logistic Amp, Logistic Area, Laplace Amp, Laplace Area, Extr Value Amp, Extr Value Area, Log Normal-4 Amp, Log Normal-4 Area, Eval4 Amp Tailed, Eval4 Area Tailed, Eval4 Amp Frtd, Eval4 Area Frtd, Gamma Amp, Gamma Area, Inv Gamma Amp, Inv Gamma Area, Weibull Amp, Weibull Area, Error Amp, Error Area, Chi-Sq Amp, Chi-Sq Area, Student t Amp, Student t Area, Beta Amp, Beta Area, F Variance Amp, F Variance Area, Pearson IV.

General Peak (12): Erfc Pk, Pulse Pk, LDR Pk, Asym Lgstc Pk, Lgstc pow Pk, Pulse pow Pk, Pulse Wid2 Pk, Intermediate Pk, Sym Dbl Sigmoid, Sym Dbl GaussCum, Asym Dbl Sigmoid, Asym Dbl GaussCum.

Transition (14): Sigmoid Asc, Sigmoid Desc, GaussCum Asc, GaussCum Desc, LorentzCum Asc, LorentzCum Desc, LgstcDose Rsp Asc, LgstcDoseRsp Desc, LogNormCum Asc, LogNormCum Desc, ExtrValCum Asc, ExtrValCum Desc, PulseCum Asc, PulseCum Desc.

User Defined Functions

Up to 10 Parameters per function.
Up to 15 UDF's active during fit.
Estimates can contain formulas and constraints.
Extensive mathematical, statistical, Bessel, and logic functions.

Baseline Fit and Subtract

Automatic detection of baseline points by constant second derivatives.
Real-time Fitting in conjunction with data point selection, deselection.
Background Functions (10): Constant, Linear, Progressive Linear, Quadratic, Cubic, Logarithmic, Exponential, Power, Hyperbolic and Non-Parametric.

Graphical Review


Component and Sum Curve graphs.
Residuals Graphs, including Distribution and Stabilized Normal Probability Plots.
Confidence and Prediction Intervals.
Peak Labels (Amplitude, Center, or Area).

Numerical Review

Peak Characterization data: Center, Amplitude, Integrated Area, Analytical Area, FWHM, FW10, FWBASE, Asymmetry at HM, Asymmetry at 10 percent, First and Second Moments, Column Efficiency and Resolution, Percentage Areas, Overlap Areas.

Parameter Statistics: Parameter Values, Confidence Limits (90, 95, 99 percent), t-values, Standard Errors.

Fit Statistics: Analysis of Variance, F-statistic, Overall Standard Error, r2 value.
Data Statistics: Residual Values, Predicted Y-values, Confidence/Prediction Intervals.

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