Resolution of rotational ambiguity for three-component systems.

Soft modeling of multivariate data is a powerful method for the analysis of processes that cannot be described quantitatively by a chemical model. Soft modeling usually does not result in unique solutions. Thus, the determination of the range of feasible solutions is important. For two-component systems the determination of that range is well-understood; for three-component systems the task is remarkably more complex. We present a novel method that can be applied to any multivariate data set, irrespective of overlap or realistic noise level. The expansion to four components is indicated.