IDEAS home Printed from https://ideas.repec.org/a/epw/ejeng0/v5y2020i8id62069.html

Optimization of Extraction Parameters of Polyphenols from Mango Seed Kernel through Response Surface Methodology

Author

Listed:
  • Junior Franck Ekorong Akouan Anta
  • Dorothée Marcelle Biloa
  • K.S.M.S. Raghavarao

Abstract

Phenolic compounds constitute an essential part of the human diet, and are of considerable interest due to their antioxidant properties. The traditional maceration method has been used for the extraction of polyphenols from mango seed kernel (Mangifera indica L.). Highlight the effects of different extraction parameters is useful to optimize the process, as well as to predict the extraction yield within the experimental domain with enough precision and confidence. The purpose of this work is to probe the influence of extraction time, extraction temperature and agitation speed on the extraction yield of phenolic compounds, and the total reducing power of the extract. The Surface Methodology (RSM), using the Doehlert design, have been applied. Optimal values of extraction yield and total reducing power was 36.99 mg Gallic acid equivalent/g and 61.08 mg Ascorbic Acid Equivalent/g respectively. The optimal conditions are 60 minutes of extraction time, 68.7°C extraction temperature and 424 rpm for agitation speed. Under optimized conditions the experimental values well agreed with the values predicted by the model equations proposed.

Suggested Citation

  • Junior Franck Ekorong Akouan Anta & Dorothée Marcelle Biloa & K.S.M.S. Raghavarao, 2020. "Optimization of Extraction Parameters of Polyphenols from Mango Seed Kernel through Response Surface Methodology," European Journal of Engineering and Technology Research, European Open Science, vol. 5(8), pages 877-883, August.
    • Handle: RePEc:epw:ejeng0:v:5:y:2020:i:8:id:62069
    DOI: 10.24018/ejeng.2020.5.8.2069
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejeng/article/view/62069
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejeng/article/download/62069/12453
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejeng.2020.5.8.2069?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kouteu Nanssou, Paul Alain & Jiokap Nono, Yvette & Kapseu, César, 2016. "Pretreatment of cassava stems and peelings by thermohydrolysis to enhance hydrolysis yield of cellulose in bioethanol production process," Renewable Energy, Elsevier, vol. 97(C), pages 252-265.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Zhanglin & Wan, Xue & Wang, Qing & Tian, Dong & Hu, Jinguang & Huang, Mei & Shen, Fei & Zeng, Yongmei, 2021. "Performances of a multi-product strategy for bioethanol, lignin, and ultra-high surface area carbon from lignocellulose by PHP (phosphoric acid plus hydrogen peroxide) pretreatment platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Sánchez, Antonio Santos & Silva, Yuri Lopes & Kalid, Ricardo Araújo & Cohim, Eduardo & Torres, Ednildo Andrade, 2017. "Waste bio-refineries for the cassava starch industry: New trends and review of alternatives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1265-1275.
    3. Gabriel S. Aruwajoye & Alaika Kassim & Akshay K. Saha & Evariste B. Gueguim Kana, 2020. "Prospects for the Improvement of Bioethanol and Biohydrogen Production from Mixed Starch-Based Agricultural Wastes," Energies, MDPI, vol. 13(24), pages 1-22, December.
    4. Cardona, Eliana & Llano, Biviana & Peñuela, Mariana & Peña, Juan & Rios, Luis Alberto, 2018. "Liquid-hot-water pretreatment of palm-oil residues for ethanol production: An economic approach to the selection of the processing conditions," Energy, Elsevier, vol. 160(C), pages 441-451.
    5. Zaafouri, Kaouther & Ziadi, Manel & ben Hassen-Trabelsi, Aida & Mekni, Sabrine & Aïssi, Balkiss & Alaya, Marwen & Hamdi, Moktar, 2017. "Enzymatic saccharification and liquid state fermentation of hydrothermal pretreated Tunisian Luffa cylindrica (L.) fibers for cellulosic bioethanol production," Renewable Energy, Elsevier, vol. 114(PB), pages 1209-1213.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejeng0:v:5:y:2020:i:8:id:62069. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejeng .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.