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 sys- tems 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.
This work was developed within the scope of the project All- Nat - POCI-01–0145-FEDER-030463 (PTDC/EQU-EPQ/30463/2017), funded by FEDER funds through COMPETE2020 –Prog. Operacional Competitividade e Internacionalização (POCI), and by national funds through the Foundation for Science and Technology (FCT/MCTES). Support was also provided by CIMO-Mountain Research Center, UIDB/00690/2020 and CICECO-Aveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, both financed by national funds through FCT/MCTES. S. M. Vilas-Boas thanks FCT and the European Social Fund (ESF) for his Ph.D. grant (SFRH/BD/138149/2018). I. W. Cordova and H. Almeida also thank project AllNat - POCI-01–0145-FEDER-030463 for their contracts.
