Enzymatic Extraction of Copper Complexes with Phenolic Compounds from Açaí (Euterpe oleracea Mart.) and Bilberry (Vaccinium myrtillus L.) Fruits
Justyna Wojcieszek , Lena Ruzik
AbstractIn this study, an analytical procedure for the extraction of phenolic copper complexes from two species of berries — Brazilian açaí (Euterpe oleracea Mart.) and Polish bilberry (Vaccinium myrtillus L.) — has been performed. Berries have proven to be a good source of vitamins and various nutrients, especially compounds with antioxidant capacity such as polyphenols, tocopherols, and ascorbic acid. Polyphenols from plant/foods interact strongly with cations (copper), and the experimental evidence presented in literature shows that the antioxidant activity is modified by the presence of metal ions. To obtain the information about phenolic copper complex attendance in berries, the enzymatic treatment with pectinase and hemicellulase was performed. These enzymes cause degradation of the cell wall matrix, causing enhancing to the extraction yield. Berries were treated with buffer (ammonium acetate) and enzymatic extraction (pectinase and hemicellulase) media and analyzed by size exclusion chromatography coupled to a mass spectrometer with inductively coupled plasma (SEC-ICP-MS) and an on-capillary liquid chromatograph coupled to an electrospray tandem mass spectrometer (μ-HPLC-ESI-MS/MS) techniques. The enzymatic treatment process significantly increases the extraction and helps extraction of other groups of copper complexes with polyphenols — such as those known from the literature and new one. In berries, copper is complexed by flavonols, for example, quercetin; flavanone, for example luteolin; and hydroxybenzoic acid, for example, syringic acid. We could indicate that in açaí and bilberry copper is complexed by non-cell- wall phenols and, additionally, in bilberry by cell wall phenols.
|Journal series||Food Analytical Methods, ISSN 1936-9751 [1936-976X]|
|Keywords in English||Açaí, Bilberry, Copper, Enzymatic treatment, HPLC, ICP-MS, ESI-MS/MS|
|Score|| = 30.0, 28-11-2017, ArticleFromJournal|
= 30.0, 28-11-2017, ArticleFromJournal
|Publication indicators||: 2016 = 2.038 (2) - 2016=1.982 (5)|
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