Bulgarian Journal of Agricultural Science
Array ( [session_started] => 1713437058 [LANGUAGE] => EN [LEPTON_SESSION] => 1 )
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Assessment of extracts from dry fruits of Vaccinium myrtillus L. and Ribes nigrum by regression analysis
Antoaneta Georgieva, Radostina Stefanova and Krasimir Krastev
Abstract: The aim of the study is to develop a technology for obtaining extracts of dried fruits of currants and bilberries. The basic extraction parameters have been established. The influence of the technological parameters of the extraction process on the content of the total phenol compounds in the extracts is analyzed. Mathematical data processing was performed by one-dimensional and multi-dimensional regression analysis. Estimates were made on the degree of influence of the factors as well as on their level of significance. Fischer’s criterion is assessed, as well as its probability. Residue assessment and analysis was performed by normal probability plot of residues, the scatter plot of the predicted residual values and the residual histogram. The resulting extracts determine the amount of total phenolic compounds for the purpose of enriching fruit juices with BAV. The effect of the extractant type, the duration and temperature of the extraction and the hydromodule on the color parameters were investigated. The results of the planned experiment are statistically processed with the Statistica program. Residue assessment and analysis was performed by normal probability plot of residues, the scatter plot of the predicted residual values and the residual histogram. The results obtained suggest that 70% ethyl alcohol, temperature 65°C, duration 3–4 h and 1:30 hydromodule are technologically reasonable choices for obtaining extracts with a maximum content of common phenolic compounds. Adequate mathematical models were described describing the dependencies of the individual parameters in the extraction of the common phenols. Technology for obtaining extracts with maximum content of common phenols has been developed.
Keywords: bilberry fruits; blackcurrant berries; extracts; phenolic compounds; regression analysis
Date published: 2020-10-20
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