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Taste Buds and Receptors01:20

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Gustation, or the sense of taste, is intrinsically linked to the anatomical structures located on the tongue. This organ's surface, along with the entirety of the oral cavity, is adorned with stratified squamous epithelium. Evident on the tongue are elevated structures known as papillae (singular = papilla), which house the mechanisms for the transduction of gustatory stimuli. Four distinct types of papillae exist, each identified by their unique morphological attributes: the circumvallate,...
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Gustation is a chemical sense that, along with olfaction (smell), contributes to our perception of taste. It starts with the activation of receptors by chemical compounds (tastants) dissolved in the saliva. The saliva and filiform papillae on the tongue distribute the tastants and increase their exposure to the taste receptors.
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Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
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Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.

Moonjeong Jang1,2, Garam Bae1,3, Yeong Min Kwon1

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Summary
This summary is machine-generated.

This study developed an AI-powered artificial nose and tongue using zinc oxide (ZnO) sensors to analyze coffee bean flavor and origin. The system achieved high accuracy in identifying flavors and distinguishing coffee types, paving the way for advanced food quality monitoring.

Keywords:
gas sensorliquid sensormachine learningsurface engineeringzinc oxide

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Area of Science:

  • Materials Science
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Portable, personalized AI-driven sensors mimic human senses for Internet of Things (IoT) applications.
  • Zinc oxide (ZnO) thin films are engineered for surface modifications, enabling sensor development.

Purpose of the Study:

  • To develop an artificial Q-grader (artificial nose and tongue) using ZnO thin films and AI for coffee bean analysis.
  • To identify aroma and flavor chemicals in coffee beans and classify their origin.

Main Methods:

  • Fabrication of a poly(vinylidene fluoride-co-hexafluoropropylene)/ZnO thin film transistor (TFT) as an artificial tongue.
  • Development of Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensors as an artificial nose.
  • Utilizing TFT transfer and dynamic response curves for electrical transport behavior analysis.
  • Implementing Principal Component Analysis (PCA)-assisted machine learning (ML) for classification and regression.

Main Results:

  • A PCA-assisted ML model achieved >92% accuracy in distinguishing four target coffee flavors.
  • An ML-based regression model predicted flavor chemical concentrations with >99% accuracy.
  • The classification model successfully distinguished four different coffee bean types with 100% accuracy.

Conclusions:

  • The developed artificial Q-grader demonstrates high efficacy in analyzing coffee bean flavor profiles and origin.
  • AI-driven sensors show significant potential for autonomous monitoring and quality control in the food industry.
  • This technology enables precise and efficient classification of coffee characteristics, advancing food science applications.