Work

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

Year: 2026

Type: article

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 = 0.9825). Concentration–sweetness intensity curves of mixed sweeteners at specific concentrations were generated using these two optimal prediction models. Results showed that at low concentrations, ternary blends of one sugar and two polyols (mannitol and sorbitol) exhibited stronger synergism than binary mixtures in the same concentration range. Specifically, blending the composite system of 1% mannitol and 2% sorbitol with 1% sucrose, 1% glucose, and 1% fructose separately increased the sweetness intensity by 39.6%, 42.8%, and 37.4%, respectively. This work confirms that machine learning can establish a quantitative relationship between multi-component sweeteners’ concentration and sweetness intensity, reveal their complex interactions, and provide a novel approach for intelligent sensory assessment and formulation design.

Source: Foods

DOI: 10.3390/foods15010167

Authors: Tiantian Du, Gang He, Xin Hou, Peiqin Shi, Zhilei Zhou, Jian Mao

Institution: Jiangnan University

Language: English

Topic: Biochemical Analysis and Sensing Techniques

Subfield: Nutrition and Dietetics

Field: Nursing

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $3,499

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China