Plant-based egg washes for use in baked goods: Machine learning and visual parameter analysis

Pea protein is one potential environmentally sustainable way of recreating the functionality of eggs in coatings for baked goods. These coatings are commonly applied to enhance visual properties of baked goods that consumers desire, especially color and gloss. This project used a simplified pie crust formulation (flour, shortening, and water) as a model baked good system to investigate color and gloss properties of pea protein coatings comprising three pea protein glycerol ratios, prepared at 2 pH values, and applied at three different weights, baked for either 15 or 30 min. Pictures were also taken and used to estimate L*, a*, and b* colorimeter values from images using random forest and artificial neural network models developed using SciKit-Learn. Image acquisition was also conducted at different lighting conditions and the results are corrected utilizing a principal component analysis (PCA) weighting approach. Results show that pea protein glycerol solutions are all glossier than egg washes, and the 80%/20% ratio gives the highest glossiness. Furthermore, there are not significant differences in any of the colorimeter values at any concentration when applying 1 g of protein. The artificial neural network model coupled with PCA weighting led to reasonable estimates of color at different lighting conditions. Together, these results offer an alternative for the use of pea protein/glycerol in lieu of egg washes.