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An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion.

Daniel Enériz1, Nicolas Medrano1, Belen Calvo1

  • 1Faculty of Science, University of Zaragoza, 50009 Zaragoza, Spain.

Biosensors
|October 22, 2021
PubMed
Summary

This study demonstrates machine learning on a Field Programmable Gate Array (FPGA) for food quality assessment. This low-cost, portable solution enables in-situ analysis, unlike bulky traditional systems.

Keywords:
FPGATVCe-nosefood qualityneural networkssensor fusion

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Area of Science:

  • Sensor technology and data analysis
  • Machine learning applications in food science

Background:

  • Sensor arrays are increasingly used in health, bio-signals, and food quality tracking.
  • Current machine learning solutions for sensor data analysis are often costly and bulky, limiting in-situ applications.

Purpose of the Study:

  • To develop a low-cost, portable machine learning system for food quality assessment.
  • To demonstrate the feasibility of implementing sensor fusion algorithms on a single Field Programmable Gate Array (FPGA) chip.

Main Methods:

  • Application of machine learning techniques for sensor fusion.
  • Implementation of algorithms on a single Field Programmable Gate Array (FPGA) chip.
  • Testing the system on an electronic-nose (e-nose) for beef classification and microbial population prediction.

Main Results:

  • Successful implementation of machine learning for food quality assessment on an FPGA.
  • Demonstrated low-cost, low-power, and compact solution suitable for in-situ deployment.
  • Validated the system's capability in beef classification and microbial population prediction.

Conclusions:

  • FPGA-based machine learning offers a viable and cost-effective alternative for in-situ food quality analysis.
  • The developed system addresses the limitations of traditional bulky computing solutions.
  • The approach is applicable to various food manufacturing and quality control scenarios.