Back close

Planar Resonator based Sensor for Adulteration Detection

Dept/Center/Lab: Amrita Center for Wireless Networks and Applications (AWNA)

Project Incharge:Dr. Aiswarya S.
Planar Resonator based Sensor for Adulteration Detection

The development of microwave sensors for adulteration detection involves a comprehensive literature survey to identify various adulterants present. A microwave resonator is designed using simulation software, leveraging the variation in material properties to detect adulteration. Following design optimization, the sensor is fabricated and experimentally tested to validate simulation results, ensuring accuracy and reliability. Successful prototypes are then refined for productization, aiming to offer a practical solution for real-time adulteration detection in the food industry, ensuring consumer safety and maintaining oil quality standards.

Name of Staff and Students from Amrita : Prof. K A Unnikrishana Menon, Ms Meenu L, Dr Sreedevi K Menon

Publication Details

  1. Aiswarya, S., Sreedevi K. Menon, Massimo Donelli, and L. Meenu. “Development of a Microwave Sensor for Solid and Liquid Substances Based on Closed Loop Resonator.” Sensors 21 (2021): 8506.
  2. Aiswarya, S., L. Meenu, K. A. Menon, Massimo Donelli, and Sreedevi K. Menon. “A Novel Microstrip Sensor Based on Closed Loop Antenna for Adulteration Detection of Liquid Samples.” IEEE Sensors Journal 24, no. 2 (2024): 1405-1414.

Related Projects

AI-Powered Algorithm for Detecting Micronutrient Deficiency in Coconut Trees  
AI-Powered Algorithm for Detecting Micronutrient Deficiency in Coconut Trees  
Technology inputs in promoting indigenous food recipes of Irulas and Kurumbas tribes and empowering disadvantaged youth of Masinagudi and Ebbanad village of The Nilgiri District
Technology inputs in promoting indigenous food recipes of Irulas and Kurumbas tribes and empowering disadvantaged youth of Masinagudi and Ebbanad village of The Nilgiri District
Computational Neuroscience and Cognitive Modeling Lab
Computational Neuroscience and Cognitive Modeling Lab
Fault diagnosis of dynamic mechanical systems (gearbox) based on signal processing using machine learning techniques
Fault diagnosis of dynamic mechanical systems (gearbox) based on signal processing using machine learning techniques
Proteomics and Antivenomics of Snake Venom
Proteomics and Antivenomics of Snake Venom
Admissions Apply Now