Predicting the wicking rate of nitrocellulose membranes from recipe data: a case study using ANN at a membrane manufacturing in South Korea

Abstract

AbstractLateral flow assays have been widely used for detecting coronavirus disease 2019 (COVID-19). A lateral flow assay consists of a Nitrocellulose (NC) membrane, which must have a specific lateral flow rate for the proteins to react. The wicking rate is conventionally used as a method to assess the lateral flow in membranes. We used multiple regression and artificial neural networks (ANN) to predict the wicking rate of NC membranes based on membrane recipe data. The developed ANN predicted the wicking rate with a mean square error of 0.059, whereas the multiple regression had a square error of 0.503. This research also highlighted the significant impact of the water content on the wicking rate through images obtained from scanning electron microscopy. The findings of this research can cut down the research and development costs of novel NC membranes with a specific wicking rate significantly, as the algorithm can predict the wicking rate based on the membrane recipe. Graphical abstract Correlation matrix displaying the distribution of the variables and linear correlations