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Characterization and Differentiation between Olive Varieties through Electrical Impedance Spectroscopy, Neural

José Miguel Madueño Luna1, Antonio Madueño Luna2, Rafael E Hidalgo Fernández3

  • 1Graphics Engineering Department, University of Seville, 41013 Seville, Spain.

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|October 23, 2020
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Summary

Electrical impedance measurements can assess agri-food quality, including olives. This study developed a system to measure olive electrical impedance, demonstrating its sensitivity to temperature and potential for cultivar characterization using neural networks and IoT.

Keywords:
SoC AD5933artificial neural networks (ANNs)electrical impedanceinternet of things (IoT)temperature

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Area of Science:

  • Agricultural Science
  • Electrical Engineering
  • Food Science

Background:

  • Electrical impedance is a valuable, non-destructive technique for analyzing agri-food product properties.
  • Previous applications include assessing fruit quality, moisture, seed germination, and frost resistance.
  • In olives, electrical impedance has been used for fat content determination and optimal harvest timing.

Purpose of the Study:

  • To develop and validate a novel system for measuring electrical impedance in agri-food products, specifically olives.
  • To investigate the effect of temperature on the electrical impedance of fresh and brined olives.
  • To explore the potential of using electrical impedance, neural networks (NN), and the Internet of Things (IoT) for olive cultivar characterization.

Main Methods:

  • A custom system utilizing the AD5933 System on Chip (SoC) for impedance measurement with a 1024-point discrete Fourier transform (DFT).
  • Integration of ADG706 analog multiplexers and a Field-Programmable Gate Array (FPGA) based DDS for clock synthesis.
  • Frequency sweep from 1 Hz to 100 kHz to capture impedance magnitude and phase across a range of conditions.

Main Results:

  • The developed system successfully measured electrical impedance in agri-food products across the specified frequency range.
  • Electrical impedance was shown to be significantly affected by temperature in both fresh and brined olives.
  • The study demonstrated the feasibility of characterizing olive cultivars using electrical impedance data combined with neural networks and IoT.

Conclusions:

  • The developed electrical impedance measurement system is effective for analyzing agri-food products like olives.
  • Temperature is a critical factor influencing olive electrical impedance, requiring consideration in measurements.
  • Combining electrical impedance, neural networks, and IoT offers a promising approach for automated olive cultivar characterization and quality monitoring.