Evaluating Adulteration of Commercial Extra Virgin Olive Oil with Canola and Sunflower Oils Through Electrochemical Impedance Spectroscopy

de Magalhães, Jassana Bernicker; Simon, Karoline Fontana; Veiga, Emiliano Amarante; Galvão, Alessandro Cazonatto; da Silva Robazza, Weber

doi:10.1007/s11947-023-03295-8

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…There are several procedures for researching edible oil in the field of food safety, such as flash gas chromatography [9][10][11], TaqMan real-time quantitative polymerase chain reaction [12], gas chromatography ion mobility spectrometry [13,14], electrochemical impedance spectroscopy [15], non-destructive ultrasonic method [16], and the use of chemometric tools [17]. Combined with chemometrics, flash gas chromatography had been used to assess volatile profiles of berry seed oils for authenticity and deterioration.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

Sun,

Hu,

Liu

et al. 2024

Foods

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Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

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