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Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots

Authorized Users Only
2020
Authors
Mudrić, Jelena
Janković, Teodora
Šavikin, Katarina
Bigović, Dubravka
Đukić-Ćosić, Danijela
Ibrić, Svetlana
Đuriš, Jelena
Article (Published version)
Metadata
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Abstract
Perennial plant Gentiana lutea L. is used worldwide for the preparation of pharmaceutical and food products. Health benefits of G. lutea roots are associated with the presence of major bitter-tasting secoiridoid gentiopicroside and xanthone isogentisin. The aim was to optimize the heat-assisted extraction of gentipicroside (GP), isogentisin (ISG) and total phenolics (TP) from G. lutea roots and develop models with high accuracy and prediction capacity by response surface methodology (RSM) and artificial neural networks (ANN). Extracts were prepared according to central composite design. Significant independent variables which were previously identified by Plackett-Burman screening design were varied at five levels - temperature (20−80 °C), time (8−180 min), solid-to-solvent ratio (1:10−1:50) and ethanol concentration (10–70 %). Contents of GP, ISG (by HPLC-DAD) and TP (by Folin–Ciocalteu method) were analyzed. The optimal conditions for the extraction were temperature of 65 °C, time of... 129.08 min, solid-to-solvent ratio of 1:40, and ethanol concentration 49.33 %. Under these conditions, experimentally obtained results for GP (18.03 mg/g dw), ISG (8.15 mg/g dw) and TP (17.46 mg of gallic acid equivalents/g dw) content were in agreement with the values predicted by RSM and ANN. Comparison of models through the coefficient of determination (R2) and the root mean square error (RMSE) showed that ANN approach was superior to RSM in predicting and modelling GP, ISG and TP content, simultaneously. Effective heat-assisted extraction method for the extraction of GP, ISG and TP from the roots of G. lutea was designed, and models with high accuracy and good prediction capacity were developed.

Keywords:
Artificial neural networks (ANN) / Gentiopicrin / Heat-assisted extraction / Isogentisin / Response surface methodology (RSM) / Yellow gentian
Source:
Industrial Crops and Products, 2020, 155
Publisher:
  • Elsevier B.V.
Funding / projects:
  • Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200003 (Institute for Medicinal Plant Research 'Dr. Josif Pančić ', Belgrade) (RS-200003)
  • Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200161 (University of Belgrade, Faculty of Pharmacy) (RS-200161)

DOI: 10.1016/j.indcrop.2020.112767

ISSN: 0926-6690

WoS: 000571868400001

Scopus: 2-s2.0-85088026455
[ Google Scholar ]
10
5
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3636
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Mudrić, Jelena
AU  - Janković, Teodora
AU  - Šavikin, Katarina
AU  - Bigović, Dubravka
AU  - Đukić-Ćosić, Danijela
AU  - Ibrić, Svetlana
AU  - Đuriš, Jelena
PY  - 2020
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3636
AB  - Perennial plant Gentiana lutea L. is used worldwide for the preparation of pharmaceutical and food products. Health benefits of G. lutea roots are associated with the presence of major bitter-tasting secoiridoid gentiopicroside and xanthone isogentisin. The aim was to optimize the heat-assisted extraction of gentipicroside (GP), isogentisin (ISG) and total phenolics (TP) from G. lutea roots and develop models with high accuracy and prediction capacity by response surface methodology (RSM) and artificial neural networks (ANN). Extracts were prepared according to central composite design. Significant independent variables which were previously identified by Plackett-Burman screening design were varied at five levels - temperature (20−80 °C), time (8−180 min), solid-to-solvent ratio (1:10−1:50) and ethanol concentration (10–70 %). Contents of GP, ISG (by HPLC-DAD) and TP (by Folin–Ciocalteu method) were analyzed. The optimal conditions for the extraction were temperature of 65 °C, time of 129.08 min, solid-to-solvent ratio of 1:40, and ethanol concentration 49.33 %. Under these conditions, experimentally obtained results for GP (18.03 mg/g dw), ISG (8.15 mg/g dw) and TP (17.46 mg of gallic acid equivalents/g dw) content were in agreement with the values predicted by RSM and ANN. Comparison of models through the coefficient of determination (R2) and the root mean square error (RMSE) showed that ANN approach was superior to RSM in predicting and modelling GP, ISG and TP content, simultaneously. Effective heat-assisted extraction method for the extraction of GP, ISG and TP from the roots of G. lutea was designed, and models with high accuracy and good prediction capacity were developed.
PB  - Elsevier B.V.
T2  - Industrial Crops and Products
T1  - Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots
VL  - 155
DO  - 10.1016/j.indcrop.2020.112767
ER  - 
@article{
author = "Mudrić, Jelena and Janković, Teodora and Šavikin, Katarina and Bigović, Dubravka and Đukić-Ćosić, Danijela and Ibrić, Svetlana and Đuriš, Jelena",
year = "2020",
abstract = "Perennial plant Gentiana lutea L. is used worldwide for the preparation of pharmaceutical and food products. Health benefits of G. lutea roots are associated with the presence of major bitter-tasting secoiridoid gentiopicroside and xanthone isogentisin. The aim was to optimize the heat-assisted extraction of gentipicroside (GP), isogentisin (ISG) and total phenolics (TP) from G. lutea roots and develop models with high accuracy and prediction capacity by response surface methodology (RSM) and artificial neural networks (ANN). Extracts were prepared according to central composite design. Significant independent variables which were previously identified by Plackett-Burman screening design were varied at five levels - temperature (20−80 °C), time (8−180 min), solid-to-solvent ratio (1:10−1:50) and ethanol concentration (10–70 %). Contents of GP, ISG (by HPLC-DAD) and TP (by Folin–Ciocalteu method) were analyzed. The optimal conditions for the extraction were temperature of 65 °C, time of 129.08 min, solid-to-solvent ratio of 1:40, and ethanol concentration 49.33 %. Under these conditions, experimentally obtained results for GP (18.03 mg/g dw), ISG (8.15 mg/g dw) and TP (17.46 mg of gallic acid equivalents/g dw) content were in agreement with the values predicted by RSM and ANN. Comparison of models through the coefficient of determination (R2) and the root mean square error (RMSE) showed that ANN approach was superior to RSM in predicting and modelling GP, ISG and TP content, simultaneously. Effective heat-assisted extraction method for the extraction of GP, ISG and TP from the roots of G. lutea was designed, and models with high accuracy and good prediction capacity were developed.",
publisher = "Elsevier B.V.",
journal = "Industrial Crops and Products",
title = "Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots",
volume = "155",
doi = "10.1016/j.indcrop.2020.112767"
}
Mudrić, J., Janković, T., Šavikin, K., Bigović, D., Đukić-Ćosić, D., Ibrić, S.,& Đuriš, J.. (2020). Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots. in Industrial Crops and Products
Elsevier B.V.., 155.
https://doi.org/10.1016/j.indcrop.2020.112767
Mudrić J, Janković T, Šavikin K, Bigović D, Đukić-Ćosić D, Ibrić S, Đuriš J. Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots. in Industrial Crops and Products. 2020;155.
doi:10.1016/j.indcrop.2020.112767 .
Mudrić, Jelena, Janković, Teodora, Šavikin, Katarina, Bigović, Dubravka, Đukić-Ćosić, Danijela, Ibrić, Svetlana, Đuriš, Jelena, "Optimization and modelling of gentiopicroside, isogentisin and total phenolics extraction from Gentiana lutea L. roots" in Industrial Crops and Products, 155 (2020),
https://doi.org/10.1016/j.indcrop.2020.112767 . .

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