Sensory Prediction and Ingredient Interaction Modeling for Functional Donuts Containing Riceberry Flour

The increasing demand for functional foods has stimulated the creation of healthier bakery items. Riceberry flour, sourced from Thai purple rice, is abundant in antioxidants and dietary fiber, rendering it a viable substitute for wheat flour in donut recipes. Nonetheless, its incorporation can profoundly modify sensory characteristics including texture, hue, and general palatability. Hydrocolloids such as Hydroxypropyl Methylcellulose (HPMC) and Methylcellulose (MC) are frequently included to enhance moisture retention and diminish oil absorption; nevertheless, their interactions with Riceberry flour are intricate and nonlinear. In this study, the use of machine learning approaches for predicting the sensory qualities of Riceberry flour-formulated functional donuts is investigated. Three regression models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest Regression—were assessed for their efficacy in predicting Overall Liking, Taste, and Texture scores based on constituent composition. The findings indicated that GPR consistently surpassed the other models, attaining the lowest MAE and RMSE values for all sensory targets. SVR offered competitive performance, however Random Forest displayed elevated prediction errors, especially for Texture. These findings underscore the efficacy of kernel-based models in elucidating nonlinear ingredient-sensory connections within tiny, noisy datasets. This study illustrates the viability of using machine learning into nascent product development, facilitating enhanced precision and efficiency in sensory optimization for functional food design.