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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.