Abstract: The noise addition method (NAM) is presented as a tool for determining the number of significant factors in a data set. The NAM is compared to residual standard deviation (RSD), the factor indicator function (IND), chi-squared (χ2) and cross-validation (CV) for establishing the number of significant factors in three data sets. The comparison and validation of the NAM are performed through Monte Carlo simulations with noise distributions of varying standard deviation, HPLC/UV-vis chromatographs of a mixture of aromatic hydrocarbons, and FIA of methyl orange. The NAM succeeds in correctly identifying the proper number of significant factors 98% of the time with the simulated data, 99% in the HPLC data sets and 98% with the FIA data. RSD and χ2 fail to choose the proper number of factors in all three data sets. IND identifies the correct number of factors in the simulated data sets but fails with the HPLC and FIA data sets. Both CV methods fail in the HPLC and FIA data sets. CV. also fails for the simulated data sets, while the modified CV correctly chooses the proper number of factors an average of 80% of the time.

Abstract: A three-dimensional stochastic computer simulation has been developed in order to provide a detailed understanding of chromatographic separations. In this simulation, the migration of individual molecules is established through diffusion and convection within a fluid phase that is in contact with a surface, Molecular interaction and, hence, retention may arise by partitioning into permeable surfaces or by adsorption at solid surfaces. The molecular distribution and the corresponding zone profile may be examined and characterized by means of statistical moments at any specified time or spatial position during the simulation. This simulation provides a powerful and versatile model with which to characterize transport phenomena in complex chromatographic separation systems.