abberior instruments
2021
Food Hydrocolloids
Super-resolution microscopy to visualize and quantify protein microstructural organization in food materials and its relation to rheology: Egg white proteins
Authors:
Bonilla, J. C., & Clausen, M. P.
Keywords:
Quantitative image analysis, Food proteins, Rheology, Egg-white proteins
Abstract:
Nowadays, food materials can be well characterized at their molecular level (food chemistry) and macroscopic level (texture and rheology) thanks to the advances in these areas in the last decades. However, there is a gap in the understanding of how food materials organize at microscopic level. This article shows how an optical super-resolution microscopy technique can be used to gain more information about food microstructure compared to conventional confocal microscopy. Egg-white is used as a protein aggregation model system, it was mixed with a fluorescent dye and was cooked for 40 min at temperatures with low, intermediate, or high albumin denaturation (69 °C, 72 °C, or 75 °C). Samples were observed using confocal and super-resolution stimulated emission depletion (STED) microscopy, and their elastic modulus was recorded with rheological amplitude sweeps. The different cooking temperatures generated materials with very different textural attributes. STED microscopy was able to resolve protein microstructures with a 5-fold increase in resolution compared to the confocal system. This gain in resolution allowed a more precise quantification of the structure, with quantification of 7–13 times more particles in the solid area of the materials compared to confocal images. The increase of particle count and particle density measured in the STED images shows a possible correlation with the logarithmic increase of the elastic modulus and the brittleness of the egg-white cooked at the three different temperatures. This shows how this extra information from STED microscopy can be correlated with macroscopic rheological measurements for a more complete understanding of protein aggregation in food matrices.