Predicting Sweetness Intensity and Uncovering Quantitative Interactions of Mixed Sweeteners: A Machine Learning Approach.

ABSTRACT: Sweeteners are commonly blended to exploit synergistic effects, enabling the desired sweetness to be attained while reducing total usage. However, establishing a quantitative relationship between mixed sweeteners' concentration and sweetness intensity remains a key challenge. This study developed a sensory evaluation-machine learning approach to construct prediction models for binary/ternary mixtures of five sweeteners (sucrose, glucose, fructose, mannitol, and sorbitol). After feature selection of molecular descriptors and comparison of seven machine learning regression models, the Multilayer Perceptron achieved superior performance for the binary mixtures (R² = 0.9828), while the Support Vector Regression exhibited optimal performance for the ternary mixtures (R² =

SUBMITTER: Du T 

PROVIDER: S-EPMC12785889 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature