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windows 2016 | CEval 0.5c (portable) /sites/all/res/downloader/locked.png
The program is packed in a zip file together with necessary libraries (.dll files). Simply unzip and run CEval.exe
linux32 2016 | CEval 0.5c /sites/all/res/downloader/locked.png
CEval for Linux (32bit)
linux64 2016 | CEval 0.5c /sites/all/res/downloader/locked.png
CEval for Linux (64bit)
pdf 2016 | CEval manul /sites/all/res/downloader/unlocked.png
A detailed step-by-step manual for using CEval
github 2016 | Github repository /sites/all/res/downloader/unlocked.png
CEval is a Qt-based open source project. In the source code, you can also find the nonlinear regressor class, including the statistical module. Simple but powerful enough. If you implement new functionality or make a build for a new platform, do not be shy and show off.
We are constatly looking for co-developers!
xls 2016 | Validation of the CEval regressor engine /sites/all/res/downloader/unlocked.png
CEval regressor engine has been extensively validated against the R statistical package. The results are summarised in this data sheet.
data_package 2016 | Demo data /sites/all/res/downloader/locked.png
The zip file contains - HVL-distorted peak data - Data tables for the (double) hyperbole fit - R script and Origin hyperbole fit
xls 2015 | HVL properties from USP tailing /sites/all/res/downloader/notification.png
You may be looking for this file due to our paper in Electrophoresis. This file is no longer available since CEval serves the purpose much better. If still interested in the file for curiosity, feel free to contact us.


Common software for data acquisition and evaluation in capillary (zone) electrophoresis (CE) is designed for analytical and quality control applications. Peak identification, integration and calibration; calculation of separation characteristics such as peak resolution and asymmetry; or the HPLC-equivalent measures such as the number of theoretical plates are commonly available. The next step may be to gain separation parameters necessary for modelling and thus understanding of the separation processes in CE and the subsequent optimization of the separation.  Alternatively, gaining the physical-chemical data from the measured electrophoregram may even be the prime interest of the user.  Both situations are typical for affinity capillary electrophoresis (ACE), which however has its additional particularities concerning data evaluation in CE. This method extends applicability of the traditional CE to complex mixtures of structurally related analytes, often enantiomers, including neutral compounds. The separation mechanism in ACE is based not only on the difference in the electrophoretic mobilities of the analytes of their own but also on their complexation with the so-called selector. Complexation constants and electrophoretic mobilities of the analyte-selector complexes are often determined. These parameters provide insight into the separation mechanism and serve for analytical method optimisations, as well as input parameters for computer simulations.


The CEval softaware provides an all-in-one open-source software solution for data evaluation in CE and ACE. When developing this software, the prime intention was to help the analysts with evaluating (possibly conditional) complexation constants and moblities of complexes from the ACE electropherogram under the 1:1 complexation stoichiometry. Such evaluations are often encountered in the related literature but remain a tedious and time-consuming process. Additionally, reading the electromigration time from the apex of the peak, linearization of the data, ignoring viscosity effects and making conclusions without proper statistical testing are all faults that still prevail in the literature and are addressed by the CEval software.

The entire evaluation procedure consists of several steps, each of which had to be done in a separate piece of software previously:

  • Assessment of the peak shape, reading the proper migration time from this peak shape, and calculating the effective electrophoretic mobility of an analyte
  • Plotting the effective mobilities as a function of the selector concentration and performing nonlinear regression on these data
  • Statistical evaluation of the results


All these steps are performed by CEval, which additionally provides the user with automatic estimates of regression parameters for the HVL-function peak fitting and the ACE nonlinear regression.


Financial support

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This work was supported by the Czech Science Foundation agency, grant No. 15-18424Y