J Sci Food Agric. 2021 Aug 14. doi: 10.1002/jsfa.11478. Online ahead of print.
ABSTRACT
BACKGROUND: Microwave sensor technology is considered to be a non-destructive and hygienic means for food evaluation and analysis. The rapid progress in microwave sensor technologies motivate to present a novel monkey-wrench shaped Microstrip Patch Sensor (MPS) for evaluating food quality. The proposed antenna is considered as a liquid sensor to detect adulteration in liquids by examining the relationship between concentration, shift in resonant frequency and variation in reflection coefficient.
RESULTS: The sensor has a compact size of 17×14 mm2 imprinted on FR4 substrate of thickness 1.57 mm. This microwave system is proposed for monitoring the overall milk quality using microstrip sensor and has considerably good numerical sensitivity and accuracy i.e. 13.11% and 88.5% respectively which makes the system attractive for detecting adulteration. Further, the Q-factor for proposed sensor is 209 and has a standard deviation less than the difference between non-adulterated and adulterated values, giving resolution high enough to distinguish adulteration with an acceptable statistical accuracy. In this study, different milk categories such as buffalo, goat and cow milk are considered as liquid samples to detect adulteration by demonstrating the variation in reflection coefficient due to change in dielectric properties for different cases viz.: (i) adulteration of milk with water, (ii) adulteration of milk with synthetic milk powder, (iii) adulteration of milk with caustic soda & (iv) adulteration of milk with vegetable oil. The referred variation in reflection coefficient and resonant frequency is due to change in the dielectric properties of a liquid when a varying concentration of an adulterant/solute is added to the liquid sample.
CONCLUSION: The simulated and measured results show good agreement that validates the proposed sensor for food adulteration detection with high sensitivity. In terms of performance, the proposed sensor shows accuracy with high spatial resolution and reduced penetration depth to detect the adulteration in various milk samples i.e. buffalo, goat and cow milk. This article is protected by copyright. All rights reserved.
PMID:34390496 | DOI:10.1002/jsfa.11478
