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The heat treatment of leaves was superior to both methods for the heat treatment of extracts in terms of process-scale implementation, energy consumption and speed.
Our data have validated that the mean phenolic content (PC) in olive leaf extracts in terms of mg dispense from 16.9 to 25.6 mg and flavonoid content (FC) from 9.5 to 24.1 respectively (Table 2).
The comparative study results, which has been done between optimized UAE, PLS-UAE and conventional Soxhlet extraction (CSE) revealed that higher CEY (81.11 ± 0.11 mg g−1) could be achieved using CSE even through, the quality of extracts in terms of phenolic compounds and RSA was significantly (p-value < 0.05) higher using optimized UAE and PLS-UAE.
The flavonoid contents of extracts in terms of quercetin equivalents were between 6.20 ± 0.41 and 17.16 ± 1.04 mg/g dw.
The degree of discoloration indicated the scavenging potential of the extracts in terms of hydrogen donating ability.
Our research group is currently focusing on the characterization of these plants extracts in terms of chemical composition and synergistic effects.
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A pattern recognition tool of principle component analysis was used to determine the similarity of the andrographolide enriched extracts, in term of their chemical composition produced from the column chromatography at different ethanol concentrations (20 100%) as eluent.
In Section 4, we present key results of the experiments and we show what useful information can be extracted in terms of design and configuration of the contextual risk management system.
For some categorical data like land use type, the category of interest (e.g. coniferous forest) was extracted in terms of a binary variable (existent = 1 or non-existent = 0).
The ANSYS stress results were extracted in terms of inhomogeneities along the σ kl (z) (assuming a laser beam path along the middle of the component), and numerically approximated as a set of homogeneous layers [5] with different corresponding stress tensors ( {sigma}_{kl}^{(p)} ) as seen in Fig. 6 [4].
The modules that contribute to the I/O behavior can be easily extracted in terms of T-invariants, and as a consequence insight can be gained about the contribution of each module to the robustness of the network.
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