Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves
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AuthorsRajković, Katarina M.
Article (Accepted Version)
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The extraction yield of Juglans nigra L. leaves was assessed at different ethanol concentrations (0–96% (v/v)) and solvent-to-solid ratios (5–20 kg kg−1). The response surface methodology (RSM) and artificial neural network with genetic algorithms (ANN-GA) were developed to optimize the extraction variables. The RSM and ANN-GA models determined 50% (v/v) ethanol concentration and 20 kg kg−1 solvent-to-solid ratio as optimal conditions, ensuring an extraction yield of 27.69 and 27.19 g 100 g−1 of dry leaves. The phenolic compounds in optimal extract were quantified: 3-O-caffeoylquinic acid (2.27 mg g−1of dry leaves), quercetin-3-O-galactoside (10.99 mg g−1 of dry leaves) and quercetin-3-O-rhamnoside (15.07 mg g−1of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K > Mg > Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and micro-elements ...(the relative order by content was: Zn > Rb > Mn > I>Sr > Ni > Cu > Co > V > Ag > Se) using inductively coupled plasma mass spectrometry (ICP-MS). The extraction coefficients for minerals were determined and were highest for K (64.3%) and I (53.5%). Optimization of extraction process resulted in high extraction yield from J. nigra leaves and optimal extract containing different phytochemical compounds.
Keywords:Artificial neural network / Juglans nigra / Minerals / Phenolic constituents / Response surface methodology
Source:Chemical Engineering Research and Design, 2020, 157, 25-33
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- This is the peer-reviewed version of the article: Rajković, K. M.; Vasić, M.; Drobac, M.; Mutić, J.; Jeremić, S.; Simić, V.; Stanković, J. Optimization of Extraction Yield and Chemical Characterization of Optimal Extract from Juglans Nigra L. Leaves. Chemical Engineering Research and Design 2020, 157, 25–33. https://doi.org/10.1016/j.cherd.2020.03.002