RAPID OPTIMIZATION OF DISINFECTANTS BY PHOTOGRAPHIC IMAGE-BASED ANALYSIS AND DESIGN OF EXPERIMENTS

Abstract

The current pandemic of coronavirus disease 2019 (COVID-19) introduced the need for the development and optimization of new alcohol-water based disinfectant formulations. Moreover, the limited supply chain of traditional active ingredients such as ethanol (EtOH) has excessively increased the base formula cost, thus, novel platforms are needed to design new strategies for disinfectant development. In this work, we devise a novel quantification method of disinfectant turbidity and foam thickness based on photographic image analysis of disinfectant to optimize formula preparation, which improves critical physicochemical parameters related to colloidal stability. Next, the numerical data obtained from the pixel photograph's values were ordered by applying a 24 factorial design, considering each disinfectant ingredient, followed by an analysis of variance (ANOVA) and counter with contour and surface plots, respectively. Furthermore, our novel method was validated using a linear regression test, consequently outlining the method's error value. Our results suggest that the photographic image analysis supported by the statistical model correlated satisfactorily with the real physicochemical behavior of disinfectant, showing that the EtOH-H2O system plays a crucial role in turbidity and foam height control. In addition, we predicted by Minitab Optimizer Tool the physicochemical and aesthetic conditions of the disinfectant, having an error of 5%. Our current approach opens up a novel path to incorporate novel active ingredients for a rapid formulation and potentially scalable method to fabricate disinfectants. Keywords: image analysis, factorial design, disinfectant, foaming, active ingredients, optimization