Fast-multivariate optimization of chiral separations in capillary electrophoresis: Anticipative strategies

• Post-DOE anticipative strategies to save experimental effort in chiral EKC separations.
• Successfully anticipated Rs values close to the experimental ones.
• Rules to anticipate EKC conditions outside DOE domain.
• PLS2-prediction strategy offers more certain and accurate anticipated Rs and At estimates than MLR-steepest ascent strategy does.

The design of experiments (DOE) is a good option for rationally limiting the number of experiments required to achieve the enantioresolution (Rs) of a chiral compound in capillary electrophoresis. In some cases, the modeled Rs after DOE analysis can be unsatisfactory, maybe because the range of the explored factors (DOE domain) was not the adequate. In these cases, anticipative strategies can be an alternative to the repetition of the process (e.g. a new DOE), to save time and money. In this work, multiple linear regression (MLR)-steepest ascent and a new anticipative strategy based on a multiple response-partial least squares model (called PLS2-prediction) are examined as post-DOE strategies to anticipate new experimental conditions providing satisfactory Rs values. The new anticipative strategy allows to include the analysis time (At) and uncertainty limits into the decision making process. To demonstrate their efficiency, the chiral separation of hexaconazole and penconazole, as model compounds, is studied using highly sulfated-β-cyclodextrin (HS-β-CD) in electrokinetic chromatography (EKC). Box–Behnken DOE for three factors (background electrolyte pH, separation temperature and HS-β-CD concentration) and two responses (Rs and At) is used. Using commercially available software, the whole modeling and anticipative process is automatic, simple and requires minimal skills from the researcher. Both strategies studied have proven to successfully anticipate Rs values close to the experimental ones for EKC conditions outside the DOE domain for the two model compounds. The results in this work suggest that PLS2-prediction approach could be the strategy of choice to obtain secure anticipations in EKC.