Skip to content
2000
Volume 1, Issue 1
  • ISSN: 2772-6215
  • E-ISSN: 2772-6223

Abstract

Introduction

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 and Discussion

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.

Conclusion

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.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/csci/10.2174/0127726215335752250226060859
2025-03-03
2025-10-31

  • From This Site

    /content/journals/csci/10.2174/0127726215335752250226060859
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. VinyardC.J. FiszmanS. Using electromyography as a research tool in food science.Curr. Opin. Food Sci.20169505510.1016/j.cofs.2016.06.003
     [Google Scholar]
  2. MillsK.R. The basics of electromyography.J. Neurol. Neurosurg. Psychiatry200576Suppl. 2ii32ii3515961866
     [Google Scholar]
  3. DohertyT.J. StashukD.W. Decomposition-based quantitative electromyography: Methods and initial normative data in five muscles200328220421110.1002/mus.1042712872325
     [Google Scholar]
  4. IshiharaS. Electromyography during oral processing in relation to mechanical and sensory properties of soft gels.J. Texture Stud.201142425426710.1111/j.1745‑4603.2010.00272.x
     [Google Scholar]
  5. StashukD.W. Decomposition and quantitative analysis of clinical electromyographic signals.Med. Eng. Phys.19992038940410.1016/S1350‑4533(99)00064‑810624736
     [Google Scholar]
  6. NazmiN. A review of classification techniques of EMG signals during isotonic and isometric contractions.Sensors2016168130410.3390/s1608130427548165
     [Google Scholar]
  7. KakkeriR.B. BormaneD. PawarP.M. Analysis and prediction of temporomandibular joint disorder using machine learning classification algorithms.Proceedings of the 3rd International Conference on Advanced Technologies for Societal Applications2021516110.1007/978‑3‑030‑69921‑5_6
     [Google Scholar]
  8. YousefiJ. Hamilton-WrightA. Characterizing EMG data using machinelearning tools.Compu. Bio. Med.20145111310.1016/j.compbiomed.2014.04.01824857941
     [Google Scholar]
  9. ZhangY. Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network.Sensors20191914317010.3390/s1914317031323888
     [Google Scholar]
  10. NichollsB. An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating.Comput. Biol. Med.202214910606810.1016/j.compbiomed.2022.10606836067634
     [Google Scholar]
  11. KumariS.K. MathanaJ.M. Blood sugar level indication through chewing and swallowing from acoustic MEMS sensor and deep learning algorithm for diabetic management.J. Med. Syst.2018431110.1007/s10916‑018‑1115‑230456688
     [Google Scholar]
  12. SonmezocakT. KurtS. Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles.Biomed. Signal Process. Control20216910290510.1016/j.bspc.2021
     [Google Scholar]
  13. LinardonJ. Interactions between different eating patterns on recurrent binge-eating behavior: A machine learning approach.Inter J. Eat Dis.202053453354010.1002/eat.2323231998997
     [Google Scholar]
  14. SatoW. Facial EMG correlates of subjective hedonic responses during food consumption.Nutrients2020124117410.3390/nu1204117432331423
     [Google Scholar]
  15. SodhiN.S. SinghB. DhillonB. KaurT. Application of electromyography (EMG) in food texture evaluation of different Indian sweets.J. Dairy. Foods Home Sci.2019381414810.18805/ajdfr.DR‑1357
     [Google Scholar]
  16. RustagiS. SodhiN.S. DhillonB. Relationship of electromyography (EMG) masticatory variables with sensory texture and instrumental texture parameters of different textured foods. Food.Measure202216139139910.1007/s11694‑021‑01168‑2
     [Google Scholar]
  17. FunamiT. IshiharaS. KohyamaK. Use of electromyography in measuring food texture.In:Food. Texture Design and Optimization.Wiley201428330110.1002/9781118765616.ch11
     [Google Scholar]
  18. MiocheL. MartinJ-F. Training and sensory judgment effects on mastication as studied by electromyography.J. Food Sci.19986311510.1111/j.1365‑2621.1998.tb15661.x
     [Google Scholar]
  19. KohyamaK. MiocheL. BourdioP. Influence of age and dental status on chewing behaviour studied by EMG recordings during consumption of various food samples.Gerodontology2003201152310.1111/j.1741‑2358.2003.00015.x12926747
     [Google Scholar]
  20. KohyamaK. Natural eating behavior of two types of hydrocolloid gels as measured by electromyography: Quantitative analysis of mouthful size effects.Food Hydrocolloids.20165224325210.1016/j.foodhyd.2015.07.004
     [Google Scholar]
  21. SantosA.C. SilvaC.A. Surface electromyography of masseter and temporal muscles with use percentage while chewing on candidates for gastroplasty.Arq. Bras. Cir. Dig.201629Suppl. 1485210.1590/0102‑6720201600S1001327683776
     [Google Scholar]
  22. Flexible Tool for Life Science Research & Teaching2024Available from: https://www.biopac.com/product/mp36r-systems/?attribute
  23. PedregosaF. Scikit-learn: Machine learning in python.J. Mach. Learn. Res.20111228252830
     [Google Scholar]
  24. VirtanenP. SciPy 1.0: Fundamental algorithms for scientific computing in Python.Nat. Methods202017326127210.1038/s41592‑019‑0686‑2
     [Google Scholar]
  25. PearsonK. Determination of the coefficient of correlation.Science190930757232510.1126/science.30.757.2317838275
     [Google Scholar]
  26. FredricksG.A. NelsenR.B. On the relationship between Spearman’s Rho and Kendall’s Tau for pairs of continuous random variables.J. Stat. Plan. Inference200713772143215010.1016/j.jspi.2006.06.045
     [Google Scholar]
  27. BaakM. KoopmanR. SnoekH. KlousS. A new correlation coefficient between categorical, ordinal and interval variables with pearson characteristics.Comput. Stat. Data Anal.202015210704310.1016/j.csda.2020.107043
     [Google Scholar]
  28. CramerH. Mathematical Methods of Statistics (PMS-9); Princeton University Press.1999Available from: http://www.jstor.org/stable/j.ctt1bpm9r4
    [Google Scholar]
  29. MullerA.C. GuidoS. Introduction to machine learning with Python: A guide for data scientists.O’Reilly Media, Inc.2016
     [Google Scholar]
  30. McKinneyW. Data Structures for Statistical Computing in Python.Proceedings of the 9th Python in Science Conference20104455156
     [Google Scholar]
  31. WangL. Research and implementation of machine learning classifier based on KNN.IOP Conf Ser: Mater. Sci. Eng2019677(5)05203810.1088/1757‑899X/677/5/052038
     [Google Scholar]
  32. SinghA. ThakurN. SharmaA. A Review of Supervised Machine learning Algorithms.3rd International Conference on Computing for Sustainable Global DevelopmentIEEE, New Delhi, India201613101315
     [Google Scholar]
  33. ClydesdaleF.M. GoverR. FugardiC. The effect of color on thirst quenching, sweetness, acceptability and flavor intensity in fruit punch flavored beverages.J. Food Qual.1992151193810.1111/j.1745‑4557.1992.tb00973.x
     [Google Scholar]
/content/journals/csci/10.2174/0127726215335752250226060859
Loading
Quantitative Analysis of Mastication Electromyography Data. Application in Chewing Behavior of Three Types of Gels.
Curr Cell Sci 1, E27726215335752 (2025); https://doi.org/10.2174/0127726215335752250226060859
/content/journals/csci/10.2174/0127726215335752250226060859
/content/journals/csci/10.2174/0127726215335752250226060859
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Electromyography; machine learning; mastication; python; sensory evaluation; signal processing
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test