Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces

Predicting the degree to which proteins adhere to a polymeric surface is an ongoing challenge in the scientific community to prevent non-specific protein adhesion and drive favourable protein-surface interactions. This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion. ATR-FTIR spectra were collected on dry and wetted polymeric surfaces, followed by evaluation of adhered fibrinogen on surfaces using the micro bicinchoninic (BCA) protein assay as a reference method. Partial Least Squares Regression (PLSR) models were built using IR spectra as the predictor variable. Overall the models built with 'wetted polymer' IR spectra performed better as compared to the models built using 'dry polymer' IR spectra (average coefficient of determination, R 2P 0.998, 0.996 respectively), with the lowest error in prediction (4 ± 0.6 μg) for ultra-high molecular weight polyethylene (UHMPE) as a test surface. This indicates the potential of this method to predict the degree to which protein adhesion occurs on polymeric surfaces using experimentally determined surface chemistry.