AIST Software PeakLab 1.07.01 (x64)

[SKIKDA]

File size: 65 MB

State-of-the-Art Scientific Software for Spectroscopy and Chromatography Peak Fitting and Analysis.

Spectroscopic Peak Fitting
PeakLab's fitting of spectroscopic peaks is unsurpassed for accuracy and ease of use. From a true Voigt function with a full precision only matched by Mathematica™, to modeling Gaussian, Lorentzian, and Voigt models with asymmetric instrument response functions as seen in XPS and Raman spectra, to fitting the full theoretical shapes in Gamma Ray spectra, PeakLab will easily manage every difficult spectral modeling problem. PeakLab uses a number of new patentable peak fitting algorithmic innovations to effortlessly get you the correct solution, an important consideration in any iterative nonlinear optimization. PeakLab dramatically augments and complements your spectroscopy instrumentation software.

Chromatographic Peak Fitting and System Suitability
PeakLab's new analytic modeling technologies make in possible to fit every chromatographic peak, baseline resolved or overlapping, to an unprecedented level of accuracy. This results in noise-free and instrumental distortion-free separation parameters for system suitability. Columns and instruments can now be measured separately. New fitting and deconvolution methods for estimating instrument response functions are invaluable for detecting flow system irregularities and column aging. With respect to data system AI applications, PeakLab can replace the large number of raw data values for each peak with a handful of theoretically meaningful parameters that dramatically enhances the capability of AI engines to process peak information. PeakLab augments your instrumentation software by taking you into the world of state-of-the-art 2024 analytics for chromatography.

UHPLC Gradient and Preparative Peak Modeling
If you are in an R&D setting where the quantification of coeluting peaks is important, PeakLab's new chromatographic models can fit real-world chromatographic peaks to less than 1 ppm unaccounted variance, three orders of magnitude better than any other existing peak modeling software. This incudes new models and technology for high accuracy fitting of HPLC and UHPLC gradient peaks as well as the complex shapes seen in preparative chromatography. PeakLab offers a first, the fitting of Fourier convolution and deconvolution models in a nonlinear peak-fitting procedure. This allows both instrumental distortions and gradient compactions to be estimated directly within the peak fits.

Chemometric Modeling
A major innovation in PeakLab in support of spectroscopy is a new chemometric modeling system that algorithmically redefines the state of the art in the science. If you wish to create a predictive model for a chromatographic or other elaborate laboratory measurement that you wish to quickly estimate using a fast spectroscopic analysis, PeakLab offers predictive models that consistently outperform all current PLS and PCR 'unscramble' algorithms. The PeakLab predictive models are faster, more robust, more accurate, much easier to understand, and child's play to implement in automated or web-based systems. PeakLab will even write the C++ or VB code for you.

Fourier Deconvolution and IRF Estimation
PeakLab offers state of the art deconvolution of instrument response functions, including IRF models which have proven to accurately describe real-world instrument system distortions as well as the asymmetry observed in XPS and Raman peaks. The technology includes our own advanced genetic algorithm that directly estimates an IRF from a optimization of the Fourier deconvolution. PeakLab's interface allows up to 25 data sets to be simultaneously fit or preprocessed in a single visual step.

Statistical Probability Density Modeling
For statisticians, PeakLab furnishes a library of nearly one hundred continuous univariate probability densities for fitting and quantifying the histograms and kernel densities generated from statistical data. PeakLab includes a peak discovery feature where the most likely models are identified with only a single button click. The new statistical models include many of the generalizations being widely used in current statistical modeling..

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