Low-Resource Microwave Sensing Platform for Oil Quality Control Applications

Votes: 5
Views: 149

A growing demand has been established over the recent years for quick and inexpensive oil adulteration detection testing to convoy the automated processes in the industry. Monitoring the oil's quality is paramount not only during their production, transportation, and storage phases but also, more importantly, for controlling their authenticity by the regulatory authorities. Extra virgin olive oil (EVOO), for instance, is massively prone to frequent fraudulent activities in the oil industry. To lower the cost of production, fake olive oils are currently produced from mixing cheap and low-quality edible or botanical oils like soybean, sunflower, and canola types with EVOO of high quality and cost. The adulteration results into modulating the composition of the fatty acids and compounds while removing many of the EVOO flavour-related features of much desirability. That would also reduce the associated health benefits and raise health issues for customers who are allergic to some supplements such as peanut proteins.

Towards implementing a smart IoT monitoring system that impedes the frequent fraudulent practices, a low-resource microwave sensor of small size (~ 6 cm3) and high sensitivity is developed for rapid non-destructive detection of oil types, brands, and/or quality without having to open any bottles off-shelf. The Whispering-Gallery-Mode (WGM) technique is used to implement the sensing platform in the millimeter-wave range 22 – 32 GHz where the microwave power from a microstrip-line is coupled into a ring resonator made of ferrite material of high resistivity and low-loss. Its magnetic anisotropy is exploited to engender a non-reciprocal effect on the induced modal fields in the presence of a bias magnetic field. The acquired nonreciprocity feature would allow for checking the oil products, when placed nearby the sensor, at multiple instances of highly sensitive WGM modes at distinct frequencies in both S12 and S21 transmission signals. Illustration 1 depicts the sensor implementation at one hot spot a few millimeters underneath the production line where all oil bottles pass through. The interaction of the sensor E-field with the oil material on top of the ring resonator, allows for recording its scattering response in few seconds by the connected Vector Network Analyzer (VNA), analyzing, and comparing it against a reference response using an AI-based software running in the attached processing machine to report any differences in quality between oil products. Measured scattering results on a fabricated prototype (illustration 2) have demonstrated its detection functionality with significant changes in resonance frequencies, phases, and dips of the induced WGM modes when testing various edible oils.

The microwave sensor enjoys many features of compact size, low power consumption, affordable cost, and high sensitivity, thereby making it attractive not only for development as an independent quality detection platform in the oil processing industry but also as a complete autonomous system based on IoT (Illustration 3) for online, rapid, non-invasive, and cost-efficient testing that substantially reduce the product processing losses, labour shifts, operational costs, and effectively improve the product quality and supply-chain. A portable version could be developed to rapidly detect any fraudulent oil brands and/or producers.

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  • ABOUT THE ENTRANT

  • Name:
    Ala Eldin Omer
  • Type of entry:
    team
    Team members:
    Ala Eldin Omer, University of Waterloo
    Suren Gigoyan, University of Waterloo
    George Shaker, University of Waterloo
    Safieddin Safavi-Naeini, University of Waterloo
  • Profession:
    Student
  • Number of times previously entering contest:
    1
  • Ala Eldin is inspired by:
    Food safety has grabbed great attention from research institutes and industrial communities over the recent years. The boundless demand of food urges the industry for massive production that satisfies the consumer’s quality standards and meets the health safety standards. Towards smart industries, new methods and practices were adopted to effectively apply the quality control procedures exploiting the recent advances on the Internet-of-Things (IoT) technologies. Among different foods, the oil products that contain composite blend of saturated, polyunsaturated, and monounsaturated fats where various carbon chain lengths are incorporated. Monitoring the quality of oils is paramount not only during their production, transportation, and storage phases but also, more importantly, for controlling their authenticity by the regulatory authorities. Extra virgin olive oil (EVOO), for instance, is massively prone to frequent fraudulent activities in the oil industry. To lower the cost of production, fake olive oils are currently produced from mixing cheap and low-quality edible or botanical oils like soybean, sunflower, and canola types with the virgin olive oil of high quality and cost. The adulteration results into modulating the composition of the fatty acids and compounds while removing many of the EVOO flavour-related features of much desirability. In addition, such adulteration would reduce the associated health benefits and may raise health issues for customers who are allergic to some supplements such as peanut proteins. Due to the high similarity in physical characteristics of the EVOO product with those been significantly or moderately modified, the detection of its authenticity by typical consumers or retailers may prove impractical. Such a hurdle has instigated the EVOO adulteration more dominantly in the industry to commercialize fraudulent products that are not compatible with the quality requirements of original EVOO. Many counterfeit activities have been reported in the olive oil industry over the recent years despite the quality standards and legal requirements issued by the IOC (International Olive Council) for different grades of olive oil.
    With non-invasive, non-destructive, uncontaminated, and rapid responses for simple measurement routines, the narrow-band microwave resonant-based technology looks more apropos for industrial applications of automated and computerized processes. Such techniques could be effectively used in oil fraud detection to rapidly identify different oil types through their electromagnetic (EM) fingerprints in the microwave spectral range of frequency domain. They could also be used to detect any impurity present in oils through capturing the tiny modifications in their EM properties over a narrow band as implemented in our proposed device.
  • Software used for this entry:
    ANSYS HFSS
  • Patent status:
    pending