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/Vacatures/Student project: Microwave probing for fruit ripeness

Student project: Microwave probing for fruit ripeness

Research & development - Wageningen | More than two weeks ago

The goal of this project is to combine microwave near-field probing in the 1-4 GHz range with data analysis/machine learning to determine fruit ripeness based on broadband dielectric spectroscopy.

Student project: Microwave probing for fruit ripeness

What you will do

In this project we would like to set up near-field microwave probing for the non-destructive assessment of fruit ripeness. Fruit ripeness is correlated to moisture content, pH and sugar content, which are typically measured using destructive techniques. For the case of e.g. mango’s, literature shows that the complex permittivity of the fruit flesh in the 1-4 GHz range is strongly correlated to moisture content and can be used as a nondestructive indicator of ripeness. It also has been shown that this correlation can be improved by excluding the contribution of the fruit skin to the measured permittivity by using a layered permittivity model. The student will be responsible for performing broadband permittivity measurements in the 1-4 GHz range using an open-ended coaxial probe and the implementation of the layered model. Depending on the duration of the assignment, the student will additionally investigate the feasibility of sensor data fusion to determine fruit freshness, combining data from both microwave and acoustic probing using e.g. machine learning techniques.

Your tasks:

  • Realize a setup for microwave probing of fruit, including controlled probe-fruit contact.
  • Performing broadband measurements and destructive reference measurements.
  • Understand and implement a procedure to derive complex permittivity from the measurement data.
  • Implement a dual layer model for improving of derived permittivity data.
  • Extended task: Setup sensor data fusion approach to combine microwave and acoustic probe data. 

Who you are

  • Bsc or Msc in Applied Physics/Electrical Engineering.
  • Available for 6-12 months.
  • Affinity for hands-on approach.
  • Experience with Matlab or python is required.
  • Understanding of basic concepts in electrical engineering (e.g. equivalent circuit) is required.
  • Basic knowledge of or experience with dielectric spectroscopy is an advantage.
  • Experience with Comsol or other finite element software is an advantage.
  • Experience with advanced data analysis or machine learning approaches is an advantage.
  • Good written and verbal English skills, ability to perform literature study.

Interested

Click on ‘apply’ to submit your application. You will then be redirected to e-recruiting.

Please be advised that non-EU/EEA country students that are studying outside of the Netherlands, need to have a work-permit to be able to do an internship in the Netherlands.

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