Phenolic and Antioxidant Analysis of Olive Leaves Extracts (Olea europaea L.) Obtained by High Voltage Electrical Discharges (HVED)

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ID: 3691
2019
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Abstract
Background: The aim of this study was to evaluate high voltage electrical discharges (HVED) as a green technology, in order to establish the effectiveness of phenolic extraction from olive leaves against conventional extraction (CE). HVED parameters included different green solvents (water, ethanol), treatment times (3 and 9 min), gases (nitrogen, argon), and voltages (15, 20, 25 kV). Methods: Phenolic compounds were characterized by ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS), while antioxidant potency (total phenolic content and antioxidant capacity) were monitored spectrophotometrically. Data for Near infrared spectroscopy (NIR) spectroscopy, colorimetry, zeta potential, particle size, and conductivity were also reported. Results: The highest yield of phenolic compounds was obtained for the sample treated with argon/9 min/20 kV/50% (3.2 times higher as compared to CE). Obtained results suggested the usage of HVED technology in simultaneous extraction and nanoformulation, and production of stable emulsion systems. Antioxidant capacity (AOC) of obtained extracts showed no significant difference upon the HVED treatment. Conclusions: Ethanol with HVED destroys the linkage between phenolic compounds and components of the plant material to which they are bound. All extracts were compliant with legal requirements regarding content of contaminants, pesticide residues and toxic metals. In conclusion, HVED presents an excellent potential for phenolic compounds extraction for further use in functional food manufacturing.
Reference Key
untar2019phenolicfoods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Žuntar, Irena;Putnik, Predrag;Kovačević, Danijela Bursać;Nutrizio, Marinela;Šupljika, Filip;Poljanec, Andreja;Dubrović, Igor;Barba, Francisco J.;Jambrak, Anet Režek;
Journal foods
Year 2019
DOI
E248
URL
https://www.mdpi.com/2304-8158/8/7/248
Keywords
artificial intelligence pneumonia

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