Simulation-based tools are designed to model chromatographic behavior based on a limited number of experimental runs.
These approaches are helpful when:
- peaks are already separated and identified
- reliable experimental data is available for modeling
However, in practical situations:
- simulation accuracy depends on the quality of initial data
- strongly overlapping or unresolved peaks are difficult to model
- unknown or complex samples limit predictive capability
ChromSword takes a different approach. It performs real experimental screening and optimization, allowing it to:
- work even when peaks are not separated
- improve resolution step by step
- handle complex and poorly understood samples
A simple way to look at it:
- Simulation tools predict behavior based on existing separation
- ChromSword builds the separation from the beginning