Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)

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...

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Main Authors:	Olalere, Olusegun Abayomi, Nour, A. H., R. M., Yunus, Alara, Oluwaseun Ruth
Format:	Article
Language:	English
Published:	Elsevier Ltd 2017
Subjects:	TP Chemical technology TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/1/Multi-response%20optimization%20and%20neural%20network%20modeling%20for%20parameter.pdf

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spelling	ump-269522020-02-28T09:18:20Z http://umpir.ump.edu.my/id/eprint/26952/ Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum) Olalere, Olusegun Abayomi Nour, A. H. R. M., Yunus Alara, Oluwaseun Ruth TP Chemical technology TS Manufactures 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. Elsevier Ltd 2017-09-18 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/26952/1/Multi-response%20optimization%20and%20neural%20network%20modeling%20for%20parameter.pdf 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 https://doi.org/10.1016/j.jksus.2017.09.010 https://doi.org/10.1016/j.jksus.2017.09.010
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TP Chemical technology TS Manufactures
spellingShingle	TP Chemical technology TS Manufactures Olalere, Olusegun Abayomi Nour, A. H. R. M., Yunus Alara, Oluwaseun Ruth Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
description	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.
format	Article
author	Olalere, Olusegun Abayomi Nour, A. H. R. M., Yunus Alara, Oluwaseun Ruth
author_facet	Olalere, Olusegun Abayomi Nour, A. H. R. M., Yunus Alara, Oluwaseun Ruth
author_sort	Olalere, Olusegun Abayomi
title	Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
title_short	Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
title_full	Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
title_fullStr	Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
title_full_unstemmed	Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)
title_sort	multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (piper nigrum)
publisher	Elsevier Ltd
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/ http://umpir.ump.edu.my/id/eprint/26952/1/Multi-response%20optimization%20and%20neural%20network%20modeling%20for%20parameter.pdf
first_indexed	2023-09-18T22:42:17Z
last_indexed	2023-09-18T22:42:17Z
_version_	1777416994941829120

Multi-response optimization and neural network modeling for parameter precision in heat reflux extraction of spice oleoresins from two pepper cultivars (Piper nigrum)

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