Simple Method to Assess Foam Structure and Stability using Hydrophobin and BSA as Model Systems

The properties and arrangement of surface-active molecules at air-water interfaces influence foam stability and bubble shape. Such multiscale-relationships necessitate a well-conducted analysis of mesoscopic foam properties. We introduce a novel automated and precise method to characterize bubble growth, size distribution and shape based on image analysis and using the machine learning algorithm Cellpose. Studying the temporal evolution of bubble size and shape facilitates conclusions on foam stability. The addition of two sets of masks, for tiny bubbles and large bubbles, provides for a high precision of analysis. A python script for analysis of the evolution of bubble diameter, circularity and dispersity is provided in the Supporting Information. Using foams stabilized by bovine serum albumin (BSA), hydrophobin (HP), and blends thereof, we show how this technique can be used to precisely characterize foam structures. Foams stabilized by HP show a significantly increased foam stability and rounder bubble shape than BSA-stabilized foams. These differences are induced by the different molecular structure of the two proteins. Our study shows that the proposed method provides an efficient way to analyze relevant foam properties in detail and at low cost, with higher precision than conventional methods of image analysis.