Predictive models for determination of peptide fouling based on the physicochemical characteristics of filtration membranes

Abstract

Peptide fouling of fifteen membranes with different physicochemical characteristics was estimated based on statistical redundancy analysis. The zeta-potential (ZP) and the roughness (Rz) parameters were highly correlated with the total fouling quantity (TFQ, RDA explanation of 86.6%), meaning that fouling by peptides was mainly due to electrostatic interactions over rough surfaces. TFQ was predicted by a statistical model using the combined effect of both zeta-potential and Rz membrane parameters, of the form: ln(TFQ) = 7.9546 + 0.1008⋅XRz + 0.03629⋅XZP (R2 = 0.8418). Concerning individual peptides and their own physicochemical properties, redundancy analysis showed that ZP, Rz, conductivity, contact angle and percentage of hydrophilic pores contributed significantly to the peptide fouling. Using the data of all 15 membranes, global predictive multivariate regression models were also established for each of the seven main peptides allowing the prediction of their specific fouling behavior on a wide range of membrane characteristics and properties. Finally, the models were validated by comparing them with experimental data of an extra membrane and consequently, peptide fouling can be well-estimated by using these relevant predictive models. Furthermore, based on the PES membranes, total fouling and individual peptide fouling were not affected by the membrane cut-off. However, based on the PVDF and PAN results, a combined effect of cut-off and material seemed to have an impact on both types of fouling: fouling decreased for these membranes when increasing their cut-off.