Indirect Hard Modeling (IHM)
- Analysis of peak heights or areas fails in cases of strong peak overlap.
- Data-driven methods like Partial Least Squares (PLS) fail in cases of strong peak shifts and deformations, or require extraordinarily high calibration efforts.
- Frequently changing applications require fast and efficient calibration. Extrapolation into new concentration or temperature ranges should be of low effort, without the need of a full re-calibration.
What is IHM?
Indirect Hard Modeling (IHM) uses a physically motivated model - hence, Hard Model -, to predict concentrationds from a mixture spectrum. Physics tell us that a mixture spectrum is composed of the contributions of the mixture components, and that the intensity of a component is linked to its concentration.
A mixture spectrum explained by the weighted sum of the component spectra.
The spectrum of a component is composed of peak-shaped signals, expressed mathematically by (peak) functions.
Representation of a component spectrum by a group of peak functions.
This generates a spectral model in which all parameters have a physical meaning: describing at the same time the shape of spectra, with peak positions, peak width etc., and the mixture composition with Component Weights ωk.
For the analysis of a mixture, the model gets fitted to the mixture spectrum by calculating the Component Weights. Peak shifts and other shape changes are compensated automatically.
How do I apply IHM?
- a tutorial with test data
- a detailed method description linking to the underlying publications