Interlaboratory Comparisons using common (reference) materials of known composition are an established means for assessing overall measurement precision and accuracy. Intercomparisons based on common data sets are equally important and informative, when one is dealing with complex chemical patterns or spectra requiring significant numerical modeling and manipulation for component identification and quantification. Two case studies of "Chemometric Intercomparison" using Simulation Test Data (STD) are presented, the one comprising STD vectors as applied to nuclear spectrometry, and the other, STD data matrices as applied to aerosol source apportionment. Generic information gained from these two exercises includes: a) the requisites for a successful STD intercomparison (including the nature and preparation of the simulation test patterns); b) surprising degrees of bias and imprecision associated with the data evaluation process, per se; c) the need for increased attention to implicit assumptions and adequate statements of uncertainty; and d) the importance of STD beyond the Intercomparison-i.e., their value as a chemometric research tool. Open research questions developed from the STD exercises are highlighted, especially the opportunity to explore "Scientific Intuition" which is essential for the solution of the underdetermined, multicollinear inverse problems that characterize modern Analytical Chemistry.
Keywords: aerosol source apportionment; chemometric intercomparison; gamma-ray spectra; interlaboratory comparison; inverse problem; linear regression; multivariate data analysis; pattern recognition; reference materials; scientific intuition; scientific judgment; simulation test data.