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Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Artificial Neural Network Classifiers
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  • Julio C. P. Alarcon,
  • Mateus I. O. Souza,
  • Vinicius Marrara Pepino,
  • Ben-Hur Viana Borges
Julio C. P. Alarcon
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Mateus I. O. Souza
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Vinicius Marrara Pepino

Corresponding Author:[email protected]

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Ben-Hur Viana Borges
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(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)
In this work, we combine microwave dielectric spectroscopy and machine learning techniques to assess the authenticity of extra virgin olive oil (EVOO). We investigate four common adulterants of EVOO, namely soybean oil, corn oil, sunflower oil, and canola oil. We use a low-cost spiral shaped microwave resonator-based sensor operating at 546.8 MHz to detect changes in the complex permittivity of oil samples. A vector network analyzer (VNA) is used to extract complex scattering parameters S11 and S21 that serves as inputs for two artificial neural network models. The first model, using only the real and imaginary parts of S21, achieves an overall accuracy of 95.8% in predicting the applied adulterant in test samples. In contrast, the second model, incorporating the real and imaginary parts of both S11 and S21, attains a 100% accuracy for test samples. Additionally, we investigate the relationship between the measured |S21| (in dB) and the adulteration level, expressed as the percentile value of the volume of adulterant per volume of the sample (mL/mL). For each adulterant, a calibration equation is developed using partial least squares regression (PLSR) to predict adulteration levels up to 50%. The maximum root mean square error (RMSE) is 2.1% for canola oil adulteration prediction. PLSR yields RMSE values of 0.9% for soybean oil, 1.1% for corn oil, and 1.0% for sunflower oil adulteration. This methodology offers both qualitative and quantitative analyses of EVOO, capable of identifying adulterations as low as 5% with a simple, portable, and practical system.
19 Mar 2024Submitted to TechRxiv
29 Mar 2024Published in TechRxiv