TY  - JOUR
AU  - Rajković, Katarina M.
AU  - Vasić, Marijana
AU  - Drobac, Milica
AU  - Mutić, Jelena
AU  - Jeremić, Sanja
AU  - Simić, Valentina
AU  - Stanković, Jovana
PY  - 2020
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3887
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−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.
PB  - Elsevier
T2  - Chemical Engineering Research and Design
T1  - Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves
VL  - 157
SP  - 25
EP  - 33
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, Jovana",
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−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.",
publisher = "Elsevier",
journal = "Chemical Engineering Research and Design",
title = "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves",
volume = "157",
pages = "25-33",
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 and Design
Elsevier., 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 and 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, Jovana, "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves" in Chemical Engineering Research and Design, 157 (2020):25-33,
https://doi.org/10.1016/j.cherd.2020.03.002 . .

TY  - JOUR
AU  - Rajković, Katarina M.
AU  - Vasić, Marijana
AU  - Drobac, Milica
AU  - Mutić, Jelena
AU  - Jeremić, Sanja
AU  - Simić, Valentina
AU  - Stanković, Jovana
PY  - 2020
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3897
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−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.
PB  - Elsevier
T2  - Chemical Engineering Research and Design
T1  - Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves
VL  - 157
SP  - 25
EP  - 33
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, Jovana",
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−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.",
publisher = "Elsevier",
journal = "Chemical Engineering Research and Design",
title = "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves",
volume = "157",
pages = "25-33",
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 and Design
Elsevier., 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 and 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, Jovana, "Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves" in Chemical Engineering Research and Design, 157 (2020):25-33,
https://doi.org/10.1016/j.cherd.2020.03.002 . .

TY  - JOUR
AU  - Rajković, Katarina M.
AU  - Pekmezović, Marina
AU  - Barac, Aleksandra
AU  - Nikodinović-Runić, Jasmina
AU  - Arsenijević, Valentina Arsic
PY  - 2015
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/1656
AB  - Antifungal effect of individual thyme (Thymus vulgaris L) and cinnamon (Cinnamomum cassia L.) essential oils (EOs) and mixture of thereof on Aspergillus flavus spores was investigated. In order to optimize the process variables (time of action, concentration of individual or mixture EOs and their mass ratio) for the antifungal effect of EO mixture, two models were developed: the response surface methodology (RSM) and artificial neural network (ANN) combined with genetic algorithm (GA). In RSM model, three factors were involved in Box-Behnken design that was applied for the experiment. Based on the mean relative percent deviation (MRPD), both models provided a good quality prediction for the antifungal effect in terms of all three process variables. RSM and ANN-GA techniques predicted the 0.5% as an optimum percentage concentration of EO mixture in EOs mass ratio T. vulgaris:C. cassia 1:1, ensuring the highest antifungal effect of 95.8% and 96.4% after 65 min. Both models were found useful for the optimization of the antifungal effect in vitro. ANN-GA was found more accurate in comparison to RSM due to its lower value of MRPD. Therefore, ANN-GA can be generally used for optimization and prediction of antimicrobial effects of 605 and their mixtures.
PB  - Elsevier Science Bv, Amsterdam
T2  - Industrial Crops and Products
T1  - Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development
VL  - 65
SP  - 7
EP  - 13
DO  - 10.1016/j.indcrop.2014.11.039
ER  - 

@article{
author = "Rajković, Katarina M. and Pekmezović, Marina and Barac, Aleksandra and Nikodinović-Runić, Jasmina and Arsenijević, Valentina Arsic",
year = "2015",
abstract = "Antifungal effect of individual thyme (Thymus vulgaris L) and cinnamon (Cinnamomum cassia L.) essential oils (EOs) and mixture of thereof on Aspergillus flavus spores was investigated. In order to optimize the process variables (time of action, concentration of individual or mixture EOs and their mass ratio) for the antifungal effect of EO mixture, two models were developed: the response surface methodology (RSM) and artificial neural network (ANN) combined with genetic algorithm (GA). In RSM model, three factors were involved in Box-Behnken design that was applied for the experiment. Based on the mean relative percent deviation (MRPD), both models provided a good quality prediction for the antifungal effect in terms of all three process variables. RSM and ANN-GA techniques predicted the 0.5% as an optimum percentage concentration of EO mixture in EOs mass ratio T. vulgaris:C. cassia 1:1, ensuring the highest antifungal effect of 95.8% and 96.4% after 65 min. Both models were found useful for the optimization of the antifungal effect in vitro. ANN-GA was found more accurate in comparison to RSM due to its lower value of MRPD. Therefore, ANN-GA can be generally used for optimization and prediction of antimicrobial effects of 605 and their mixtures.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Industrial Crops and Products",
title = "Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development",
volume = "65",
pages = "7-13",
doi = "10.1016/j.indcrop.2014.11.039"
}

Rajković, K. M., Pekmezović, M., Barac, A., Nikodinović-Runić, J.,& Arsenijević, V. A.. (2015). Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development. in Industrial Crops and Products
Elsevier Science Bv, Amsterdam., 65, 7-13.
https://doi.org/10.1016/j.indcrop.2014.11.039

Rajković KM, Pekmezović M, Barac A, Nikodinović-Runić J, Arsenijević VA. Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development. in Industrial Crops and Products. 2015;65:7-13.
doi:10.1016/j.indcrop.2014.11.039 .

Rajković, Katarina M., Pekmezović, Marina, Barac, Aleksandra, Nikodinović-Runić, Jasmina, Arsenijević, Valentina Arsic, "Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development" in Industrial Crops and Products, 65 (2015):7-13,
https://doi.org/10.1016/j.indcrop.2014.11.039 . .