Olalere, Olusegun Abayomi and Nour, A. H. and R. M., Yunus and Alara, Oluwaseun Ruth (2017) Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum). Journal of King Saud University - Science, 31 (4). pp. 789-797. ISSN 1018-3647. (Published)
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Abstract
Black and white peppers are important oil bearing commodity crop in tropical areas. They are highly beneficial in food industries and herbal medicine, due to their amazing aroma and therapeutic activities. In this study, heat reflux technique was employed to extract medicinal oleoresin from the two peppercorns. For this purpose, various extraction parameters were considered viz: extraction time, particle size and feed-solvent ratio. These extraction parameters were employed to optimize the extraction yield and absorbed energy via Taguchi methodology. The established optimal condition values of the yield and absorbed energy from the parametric study were 13.22 mg/g and 285.60 J/min, respectively in black pepper heat refluxation. Moreover, in white pepper refluxation, the extraction yield and absorbed energy were 14.04 mg/g and 264.82 J/min, respectively. Artificial neural network (ANN) was used for prediction purposes. This was achieved by comparing two algorithms, transfer functions and neurons. A good training and better prediction of the experimental data were observed using the Levenberg Marquardt (LM) feed forward backpropagation algorithm with log sigmoid transfer function as hidden layer and 3-4-5-1 as model topology. Furthermore, a total of 19 and 25 bioactive compounds were identified in black and white pepper extracts, respectively. The results from the Scanning Electron Spectrometry (SEM) showed a remarkable morphological changes during the heat refluxation process.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial neural network; Black and white pepper; Gas Chromatography-Mass Spectrometry (GC–MS) analysis; Multi-response optimization; Taguchi method |
| Subjects: | T Technology > TP Chemical technology T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Chemical & Natural Resources Engineering Institute of Postgraduate Studies |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 28 Feb 2020 09:18 |
| Last Modified: | 28 Feb 2020 09:18 |
| URI: | http://umpir.ump.edu.my/id/eprint/26952 |
| Download Statistic: | View Download Statistics |
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