Texture prediction and influencing factor analysis of gel-type foods using machine learning based on compression and penetration data

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Key Points

This study aims to connect the mechanical properties of gel foods with sensory perceptions using machine learning.
Constructed a machine learning model based on sensory evaluation and compression/penetration test data
Analyzed specific onomatopoeic expressions related to texture characteristics
Investigated the relationship between mechanical properties and sensory impressions
Identified correlation between specific onomatopoeia and compression/penetration characteristics
Demonstrated that texture perceptions can be linked to mechanical measurements
Established a foundation for using sensory evaluations as scientific indicators in food development

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Research analyzes texture and sensory qualities of gel foods using machine learning, indicating new design perspectives.

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Ishihara et al. (2026) studied this question.

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