A Rapid Screening Approach for Authentication of Olive Oil and Classification of Binary Blends of Olive Oils Using Low-F
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A Rapid Screening Approach for Authentication of Olive Oil and Classification of Binary Blends of Olive Oils Using Low-Field Nuclear Magnetic Resonance Spectra and Support Vector Machine Xin Wang 1
&
Guangli Wang 1 & Xuewen Hou 1 & Shengdong Nie 1
Received: 22 November 2019 / Accepted: 14 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Due to the quality differentiation and commercial concerns, rapid authentication and addressing the adulterants in olive oil is of great importance. The feasibility of identifying pure olive oil as well as classifying the binary blends of olive oils according to the adulterants in olive oils using low-field NMR spectroscopy and support vector machine (SVM) have been investigated. Based on the characterization of low-field NMR profiles of six types of vegetable oil and the binary blends of olive oils with three types of seeds oils (corn, soybean, and sunflower seed oils), SVM was employed to build the authentication and classification models. The result indicated that the difference of oils and the type of blends can be monitored by low-field NMR profiles. SVM classification models for identifying pure olive oils from blended ones were developed and an 84.92% classification accuracy was acquired when the adulteration ratio is above 10%. For the classification of binary blends of olive oils according to the seed oils, two SVM classification strategies have been developed and compared, and the SVM model with a suspected range of 10%–30% could provide an acceptable classification result. LF-NMR could be a novel screening method for the authentication of olive oil. Keywords Olive oil . Authentication . LF-NMR . Support vector machine . Classification
Introduction Originated from the Mediterranean region, olive oil has been praised as liquid gold in the Western and loved by people of all countries for its unique taste and excellent nutritional value
(Cicerale et al. 2009). In recent years, the proportion of olive oil in the world-wide edible oil market has also increased significantly. However, olive oil has also become the target of unscrupulous merchants seeking benefits (Maggio et al. 2010) (Zhou et al. 2015). For example, the common
Highlights • LF-NMR measurements were obtained for pure and binary blends of olive oils. • Support vector machine was employed to build the classification models. • An 84.92% authentication accuracy was acquired for pure olive oil. • SVM classification model with suspected range was suitable for blended olive oils. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12161-020-01799-z) contains supplementary material, which is available to authorized users. * Xin Wang [email protected] Guangli Wang [email protected] Xuewen Hou [email protected]
Shengdong Nie [email protected]
1
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Food Anal. Methods
fraudulent phenomena of olive oil in China ar
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