Comparison of two computational methods for solvent screening in countercurrent and centrifugal partition chromatography

Abstract

Countercurrent and centrifugal partition chromatography are techniques applied in the separation and isolation of compounds from natural extracts. One of the key design parameters of these processes is the selection of the biphasic solvent system that provides for the adequate partitioning of the solutes. To address this challenging task, the fully predictive Conductor-like Screening Model for Real Solvents (COSMO-RS) and the semi-predictive Non-Random Two-Liquid Segment Activity Coefficient (NRTL-SAC) model were applied to estimate the partition coefficients (K) of four model phenolic compounds (vanillin, ferulic acid, (S)-hesperetin and quercetin) in different solvent systems. Complementing the experimental data collected in the literature, partition coefficients of each solute in binary, or quaternary, solvent systems were measured at 298.2 K.Higher deviations from the experimental data were obtained using the predictive COSMO-RS model, with an average RMSD (root-mean-square deviation) in log(K) of 1.17 of all four solutes (61 data points), providing a satisfactory quantitative description only for the systems containing vanillin (RSMD = 0.57). For the NRTL-SAC model, the molecular parameters of the solutes were initially calculated by correlating a set of K and solubility (x, in mole fraction) data (16 partition coefficients and 44 solubility data points), for which average RMSD values of 0.07 and 0.41 were obtained in log(K) and log(x), respectively. The predictions of the remaining log(K) data (45 partition coefficients) resulted in an average RMSD of 0.43, suggesting that the NRTL-SAC model was a more reliable quantitative solvent screening tool. Depending on the amount of available solubility and partition data, both models can be valuable alternatives in the preliminary stages of solvent screening destined to select the optimal mobile and stationary phases for a given separation.