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Related Experiment Video

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Fruit Volatile Analysis Using an Electronic Nose
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A Novel Approach for Apple Freshness Prediction Based on Gas Sensor Array and Optimized Neural Network.

Wei Wang1, Weizhen Yang1, Maozhen Li1,2

  • 1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces an electronic nose system for predicting apple freshness by analyzing emitted gases. The novel approach utilizes a chaotic sequence-optimized neural network for accurate, non-destructive freshness assessment.

Keywords:
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Area of Science:

  • Agricultural Science
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Apple freshness prediction is crucial for reducing storage risks and economic losses in China's significant apple industry.
  • Changes in volatile organic compounds (VOCs) like ethylene, carbon dioxide, and ethanol are key indicators of apple degradation.
  • Non-destructive methods are needed to assess apple quality without causing physical damage.

Purpose of the Study:

  • To develop an accurate and non-destructive method for predicting apple freshness.
  • To design an electronic nose system capable of capturing apple-emitted odor information.
  • To create an optimized neural network model for enhanced prediction accuracy.

Main Methods:

  • An electronic nose system was designed, incorporating a gas sensor array and wireless transmission.
  • A Back Propagation (BP) neural network was optimized using an improved Sparrow Search Algorithm (SSA) with a chaotic Tent sequence.
  • The relationship between prediction coefficients and input vectors was fitted to establish an accuracy benchmark.

Main Results:

  • The developed system achieved accurate apple freshness prediction based on odor information.
  • The SSA-optimized BP model demonstrated improved prediction accuracy compared to traditional methods.
  • The system proved to be simple to operate, cost-effective, reliable, and mobile.

Conclusions:

  • The electronic nose system offers a viable non-destructive solution for apple freshness prediction.
  • The optimized neural network model significantly enhances the accuracy of odor-based freshness assessment.
  • This technology can effectively reduce storage risks and economic losses in the apple supply chain.