Sensory and Physicochemical Evaluation of Dairy-Like Plant-Based Milk Formulations Predicted by Machine Learning.

Plant-based milk alternatives (PBMAs) are now increasingly used by some consumers in similar ways to cow milk. However, they differ in their physicochemical and sensory properties. Improving PBMAs to better mimic dairy is limited by the time and cost of development trials. Machine learning offers a promising approach to predict optimal formulations without the need for extensive trial and error. In this study, Random Forest regression (RFR) and gradient boosting regression (GBR) models were used to formulate PBMAs (Formulation 1 and 2, respectively) that mimic cow milk. Sensory analysis, viscosity, stability, color, particle size, and headspace volatiles were compared to cow milk to assess model accuracy. Both formulations were sensorially distinct from coconut, oat, soy, almond, pasteurized, and ultra-high temperature (UHT) cow milk, associated with vanilla, caramel, astringent, bitter, metallic, and nutty notes, compared to the sweet, dairy, and coconut notes of dairy. The formulations contained aldehydes, including hexanal and pentanal from soy, although compounds including vanillin, octanal, and 1-pentanol were shared with cow milk. UHT cow (0.68 µm) had the smallest average particle size (D