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Related Concept Videos

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Olfaction

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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
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Related Experiment Video

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Fruit Volatile Analysis Using an Electronic Nose
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Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning.

Mingming Zhao1,2, Zhiheng You1,2, Huayun Chen1,2

  • 1School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Foods (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

This study introduces a portable fruit ripeness detection system using colorimetric sensors and deep convolutional neural networks (DCNN). The system accurately identifies ripeness by analyzing unique scent fingerprints, overcoming limitations of previous artificial olfactory systems.

Keywords:
artificial olfactory sensorcolorimetric sensing combinatoricsdeep convolutional neural networksfruit ripeness detectionvolatile organic compounds

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Area of Science:

  • Agricultural Science
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Artificial scent screening systems show potential for fruit ripeness detection but face challenges with sensitivity and pattern recognition accuracy.
  • Existing methods often lack the precision required for reliable commercial application in fruit quality assessment.

Purpose of the Study:

  • To develop and validate a portable system for accurate fruit ripeness prediction.
  • To overcome the limitations of low sensitivity and inaccurate pattern recognition in artificial olfactory systems.

Main Methods:

  • Utilized gas chromatography-mass spectrometry (GC-MS) to identify volatile gases emitted by fruits at different ripening stages.
  • Employed colorimetric sensing combinatorics with 25 dyes to generate unique scent fingerprints based on gas interactions.
  • Applied deep convolutional neural networks (DCNN), specifically DenseNet, for pattern recognition of the generated scent fingerprints.

Main Results:

  • The DCNN model achieved high accuracy in fruit ripeness assessment, reaching 97.39% on the validation set and 82.20% on the test set.
  • The system successfully generated distinct scent fingerprints for various fruit ripening stages through cross-reactivity of colorimetric dyes.
  • The developed system demonstrated effective pattern recognition for complex volatile gas mixtures.

Conclusions:

  • The portable fruit ripeness prediction system, integrating colorimetric sensing and DCNN, offers a highly accurate and non-destructive method for ripeness detection.
  • This innovative approach addresses key challenges in artificial scent screening, paving the way for practical and cost-effective fruit quality monitoring.
  • The system's accuracy, convenience, and low cost make it a promising tool for commercial development in the agricultural sector.