On the inherent data fitting problems encountered in modelingretention behavior of analytes with dual retention mechanism

Some valuable insights have been obtained in the inherent fitting problems when trying to predict theretention time of complex, multi-modal retention modes such as encountered in HILIC and SFC. In thisstudy, we used mathematical models with known input parameters to generate different sets of numericaltest curves representative for systems exhibiting a complex, non-LSS dual retention behavior. Subse-quently, we tried to fit these data sets using some popular (non-linear) literature models. Even in caseswhere a physical fitting model exists (e.g., the mixed model in case of pure additive adsorptive andpartitioning retention), the fitting quality can only be expected to be relatively good (prediction errorsexpressed in terms of a normalized resolution error εRs) when carefully selecting the scouting runs andthe appropriate starting values for the fitting algorithm. The latter can best be done using a comprehen-sive grid search scanning a wide range of different starting values. This becomes even more importantwhen no good physical model is available and one has to use a non-physical fitting model, such as theempirical Neue-model. The use of higher-order models is found to be quasi indispensable to keep theprediction errors on the order of some ΔRs= 0.05. Also, the choice of the scouting runs becomes evenmore important using these higher-order models. For highly retained compounds we recommend usingscouting runs with long tG/t0-values or to include a run with a higher fraction of eluting solvent at thestart of the gradient. When trying to predict gradient retention, errors with which the isocratic retentionbehavior is fitted are much less important for high retention factors k than errors made in the range of knear the one at the point of elution. The results obtained with a so-called segmented Neue-model (con-taining 7 parameters) were less good and thus practically not interesting (because of the high number ofinitial runs). © 2015 Elsevier B.V.