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image of Quantitative Analysis of Mastication Electromyography Data. Application in Chewing Behavior of three types of Gels.

Abstract

Background

Jaw muscles are essential for chewing and swallowing, generating electrical signals measurable through electromyography (EMG). In food science, EMG is increasingly being used to link food texture with consumption

Objective

This study aimed to analyze chewing EMG data using signal processing and machine learning, to explore its relationship with sensory evaluation.

Methods

Participants tasted three gels with identical flavors but different colors (green, red, yellow) while EMG data were recorded. Three machine-learning classification algorithms analyzed the EMG patterns to detect potential color-based preference differences.

Results

No strong relationship was found between EMG data and gel preferences, although the approach shows promise for investigating muscle function in food choice. Challenges arose from limited taste variability and data set size.

Conclusión

This research underscores EMG’s potential in studying muscle function and food-related behavior, despite limitations in using EMG data with machine learning for preference prediction.

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/content/journals/csci/10.2174/0127726215335752250226060859
2025-03-03
2025-04-12

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/content/journals/csci/10.2174/0127726215335752250226060859
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Quantitative Analysis of Mastication Electromyography Data. Application in Chewing Behavior of three types of Gels.
Bentham Science Publishers ; https://doi.org/10.2174/0127726215335752250226060859
/content/journals/csci/10.2174/0127726215335752250226060859
/content/journals/csci/10.2174/0127726215335752250226060859
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  • Article Type:     Research Article
Keywords: Electromyography, mastication ; sensory, signal processing, machine learning, python
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