Key-set factor analysis

There are many chemometric methods to build initial estimates some are particularly suitable when the data consists of the evolutionary profiles of a process, such as evolving factor analysis (see Figure 11.4b in Section 11.3) [27, 28, 51], whereas some others mathematically select the purest rows or the purest columns of the data matrix as initial profiles. Of the latter approach, key-set factor analysis (KSFA) [52] works in the FA abstract domain, and other procedures, such as the simple-to-use interactive self-modeling analysis (SIMPLISMA) [53] and the orthogonal projection approach (OPA) [54], work with the real variables in the data set to select rows of purest variables or columns of purest spectra, that are most dissimilar to each other. In these latter two methods, the profiles are selected sequentially so that any new profile included in the estimate is the most uncorrelated to all of the previously selected ones. 

ACD/AutoChrom uses the mutual automated peak matching [33] or UV-MAP approach based on extraction of pure variables from diode array data. The UV-MAP algorithm applies abstract factor analysis (AFA) followed by iterative key set factor analysis to the augmented data matrix in order to extract retention times for each of the selected experiments. 

We now consider a type of analysis in which the data (which may consist of solvent properties or of solvent effects on rates, equilibria, and spectra) again are expressed as a linear combination of products as in Eq. (8-81), but now the statistical treatment yields estimates of both a, and jc,. This method is called principal component analysis or factor analysis. A key difference between multiple linear regression analysis and principal component analysis (in the chemical setting) is that regression analysis adopts chemical models a priori, whereas in factor analysis the chemical significance of the factors emerges (if desired) as a result of the analysis. We will not explore the statistical procedure, but will cite some results. We have already encountered examples in Section 8.2 on the classification of solvents and in the present section in the form of the Swain et al. treatment leading to Eq. (8-74). 

E.R. Malinowski, Obtaining the key set of typical vectors by factor analysis and subsequent isolation of component spectra. Anal. Chim. Acta, 134 (1982) 129-137. 

If improving human resource management is a key factor for success in a company s business model, all questions and policies that relate to this area (tags 2, 3,4, and 5 above) can be sorted from the whole and grouped together for analysis. Both strengths and opportunities in this query area can be studied and utilized in order to set action plans into motion. 

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