Toward a better interpretation of the partial least squares regression models for fluoropolymers treated by dielectric barrier discharges at atmospheric pressure

In this article, partial least squares regression was applied to a continuous dielectric discharge process aiming to modify the surface of a fluoropolymer. Cross‐validation was used to find the optimal number of latent variables that minimize the error from the model. Then, the key parameters affecting the process were highlighted with the variable importance on the projection (VIP) and the biplot exploratory graph produced from the algorithm. Finally, the model was used to predict additional data not included in the training set. The new predictions were used to assess the ability of the model to predict data outside of the training range. The applicability domain for this model was also discussed. The results showed that less prediction errors occurred when the surface modification remained close to the untreated fluoropolymer surface characteristics.