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Novel monkey-wrench-shaped microstrip patch sensor for food evaluation and analysis.

Nitika¹, Jaswinder Kaur¹, Rajesh Khanna¹

¹Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India.

Journal of the Science of Food and Agriculture

|August 14, 2021

Summary

A novel monkey-wrench-shaped microwave sensor effectively detects food adulteration in milk. This non-destructive sensor offers high sensitivity and accuracy for identifying contaminants like water, synthetic powder, caustic soda, and vegetable oil.

Keywords:

dielectric properties microstrip sensor monkey wrench reflection coefficient

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Area of Science:

Electrical Engineering
Food Science
Materials Science

Background:

Microwave sensor technology offers a non-destructive and hygienic approach to food analysis.
Advancements in microwave sensors enable novel applications in food quality evaluation.
A monkey-wrench-shaped microstrip patch sensor is proposed for evaluating food quality.

Purpose of the Study:

To develop and validate a novel monkey-wrench-shaped microstrip patch sensor for detecting food adulteration.
To investigate the sensor's performance in identifying various adulterants in different milk types.
To establish the relationship between concentration, resonant frequency shift, and reflection coefficient variation.

Main Methods:

A compact monkey-wrench-shaped microstrip patch sensor (17×14 mm²) was designed and fabricated on an FR4 substrate (1.57 mm thickness).
The sensor was utilized as a liquid sensor to analyze milk samples adulterated with water, synthetic milk powder, caustic soda, and vegetable oil.
The variation in reflection coefficient and resonant frequency was measured to assess the dielectric properties of the liquid samples.

Main Results:

The sensor demonstrated good numerical sensitivity (13.11%) and accuracy (88.5%) for detecting adulteration.
A high Q-factor (209) and sufficient resolution allowed for statistically accurate distinction between adulterated and non-adulterated milk.
Significant variations in reflection coefficient and resonant frequency were observed due to changes in dielectric properties with different adulterants and concentrations.

Conclusions:

Simulation and measured results confirm the proposed sensor's effectiveness for food adulteration detection with high sensitivity.
The sensor exhibits high accuracy, spatial resolution, and reduced penetration depth for adulteration detection in buffalo, goat, and cow milk.
The developed microwave sensor system is attractive for monitoring milk quality and detecting adulteration.