Self-modeling mixture analysis

Self-modeling mixture analysis is a class of data analysis techniques that are also termed as Blind signal separation or Blind source separation which are used to separate pure data components from additive mixture data.

Examples

Examples include the separation of pure spectra and concentration profiles from a matrix of spectra made from mixtures of components with varying concentrations.

Multivariate curve resolution

Some classes of applications are also termed as multivariate curve resolution. Well known techniques include SIMPLISMA.^[1]

See also

• Mixture model

References

1. ^ Willem Windig; Jean Guilment (July 1991). "Interactive Self-Modeling Mixture Analysis". Analytical Chemistry 63 (14): 1425–1432. doi:10.1021/ac00014a016. 

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