Multiset data analysis: extended multivariate curve resolution

An important achievement in the analysis of complex data matrices lacking the appropriate conditions for unique resolution resulted when multivariate curve resolution (MCR) methods were applied to several data matrices simultaneously, to the so-called three-way data and multiset data. Resolution ambiguities and rank deficiency problems in the analysis of two-way data sets can be reduced significantly if it is possible to analyze data structures with richer information (multiway and multiset data). Data fusion and multiset data analysis are names given now to the merged measurements coming from one or more experiments monitored by different techniques or under different conditions but, well before these names were coined, MCR had already been applied to these and to other kinds of merged data arrangements. Data fusion often responds to the hyphenated or multiresponse nature of modern instruments (coupling several detection systems or acquiring several responses at a time), but other augmented data arrangements are equally interesting, such as multibatch and multiprocess data sets or multiple data sets coming from the analysis of the same system under different conditions or stimuli