Rapid Non-Invasive Capacitive Assessment of Extra Virgin Olive Oil Authenticity

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Rapid Non-Invasive Capacitive Assessment of Extra Virgin Olive Oil Authenticity
Title:
Rapid Non-Invasive Capacitive Assessment of Extra Virgin Olive Oil Authenticity
Journal Title:
Electronics
Publication Date:
11 January 2023
Citation:
Salila Vijayalal Mohan, H. K., Aung, P. P., Ng, C. F., Wong, Z. Z., & Alexander Malcolm, A. (2023). Rapid Non-Invasive Capacitive Assessment of Extra Virgin Olive Oil Authenticity. Electronics, 12(2), 359. https://doi.org/10.3390/electronics12020359
Abstract:
Economically motivated adulteration (EMA) and/or cross-contamination are the two major factors resulting in the substandard quality of premium edible oil like extra virgin olive oil (EVOO) produced in food and beverage (F&B) fast-moving consumer goods (FMCG) industries. Current quality assurance methods (e.g., spectroscopy and chromatography) in FMCG involve intrusive sample extraction and ex situ analysis in a laboratory using expensive bulky instrumentation, which is neither integrable inline nor scalable to match the production throughput. Such techniques do not meet the industrial requirements of in situ testing, non-intrusive analysis, and high throughput inspection (100% product verification) leading to food loss and package waste from unwanted batch rejects. Herein, a low-cost electrical approach based on capacitance is proposed to show the proof of concept for screening EVOO-filled containers non-invasively for adulteration without any sample extraction by capturing the differences in the dielectric properties of mixed oils. The sensor system displayed a fast response (100 ms) and low detection limits for different adulterants (olive oil (32.8%), canola oil (19.4%), soy oil (10.3%) and castor oil (1.7%)), which is suitable for high-throughput (>60 sample/min) screening. Furthermore, a low-cost automated system prototype was realized to showcase the possibility of translating the proof of concept for possible scaling up and inline integration.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: Advanced Remanufacturing and Technology Centre (ARTC)
Grant Reference no. : n.a
Description:
ISSN:
2079-9292
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