Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves
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2020
Authors
Rajković, Katarina M.Vasić, Marijana
Drobac, Milica
Mutić, Jelena
Jeremić, Sanja
Simić, Valentina
Stanković, Jovan
Article (Published 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(-1)of dry leaves), quercetin-3-O-galactoside (10.99 mg g(-1) of dry leaves) and quercetin 3 0 rhamnoside (15.07 mg g(-1)of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K gt Mg gt Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and mic...ro-elements (the relative order by content was: Zn gt Rb gt Mn gt I gt Sr gt Ni gt Cu gt Co gt V gt Ag gt 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:
Response surface methodology / Phenolic constituents / Minerals / Juglans nigra / Artificial neural networkSource:
Chemical Engineering Research & Design, 2020, 157, 25-33Publisher:
- Elsevier, Amsterdam
Funding / projects:
- Detection of early laboratory fungal biomarkers and it's importance for outcome of invasive fungal infections in Serbia (RS-MESTD-Basic Research (BR or ON)-175034)
- Signaling molecules in diabetes: search for potential targets in intrinsic pathways for prediction and intervention in diabetes (RS-MESTD-Basic Research (BR or ON)-173020)
- Investigation on the medicinal plants: morphological, chemical and pharmacological characterisation (RS-MESTD-Basic Research (BR or ON)-173021)
DOI: 10.1016/j.cherd.2020.03.002
ISSN: 0263-8762
WoS: 000528193200003
Scopus: 2-s2.0-85081647717
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Institut za molekularnu genetiku i genetičko inženjerstvoTY - JOUR AU - Rajković, Katarina M. AU - Vasić, Marijana AU - Drobac, Milica AU - Mutić, Jelena AU - Jeremić, Sanja AU - Simić, Valentina AU - Stanković, Jovan PY - 2020 UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1378 AB - 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(-1)of dry leaves), quercetin-3-O-galactoside (10.99 mg g(-1) of dry leaves) and quercetin 3 0 rhamnoside (15.07 mg g(-1)of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K gt Mg gt Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and micro-elements (the relative order by content was: Zn gt Rb gt Mn gt I gt Sr gt Ni gt Cu gt Co gt V gt Ag gt 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. PB - Elsevier, Amsterdam T2 - Chemical Engineering Research & Design T1 - Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves EP - 33 SP - 25 VL - 157 DO - 10.1016/j.cherd.2020.03.002 ER -
@article{ author = "Rajković, Katarina M. and Vasić, Marijana and Drobac, Milica and Mutić, Jelena and Jeremić, Sanja and Simić, Valentina and Stanković, Jovan", year = "2020", abstract = "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(-1)of dry leaves), quercetin-3-O-galactoside (10.99 mg g(-1) of dry leaves) and quercetin 3 0 rhamnoside (15.07 mg g(-1)of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K gt Mg gt Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and micro-elements (the relative order by content was: Zn gt Rb gt Mn gt I gt Sr gt Ni gt Cu gt Co gt V gt Ag gt 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.", publisher = "Elsevier, Amsterdam", journal = "Chemical Engineering Research & Design", title = "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves", pages = "33-25", volume = "157", doi = "10.1016/j.cherd.2020.03.002" }
Rajković, K. M., Vasić, M., Drobac, M., Mutić, J., Jeremić, S., Simić, V.,& Stanković, J.. (2020). Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves. in Chemical Engineering Research & Design Elsevier, Amsterdam., 157, 25-33. https://doi.org/10.1016/j.cherd.2020.03.002
Rajković KM, 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. in Chemical Engineering Research & Design. 2020;157:25-33. doi:10.1016/j.cherd.2020.03.002 .
Rajković, Katarina M., Vasić, Marijana, Drobac, Milica, Mutić, Jelena, Jeremić, Sanja, Simić, Valentina, Stanković, Jovan, "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves" in Chemical Engineering Research & Design, 157 (2020):25-33, https://doi.org/10.1016/j.cherd.2020.03.002 . .