Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber

Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to optimize saponin extraction from North Atlantic Sea cucumber (Cucumaria frondosa). Ultrasonication-assisted ethanol-based extractions were used in a second-order polynomial and 3n full factorial RSM interconnecte...

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Published in:Food Chemistry Advances
Main Authors: Oladapo F. Fagbohun, Joseph P.M. Hui, Junzeng Zhang, Guangling Jiao, H.P. Vasantha Rupasinghe
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2024
Subjects:
ANN
RSM
Online Access:https://doi.org/10.1016/j.focha.2024.100748
https://doaj.org/article/43fb2bbd17bf499dab8e09ca8954a995
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spelling ftdoajarticles:oai:doaj.org/article:43fb2bbd17bf499dab8e09ca8954a995 2024-09-15T18:03:24+00:00 Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber Oladapo F. Fagbohun Joseph P.M. Hui Junzeng Zhang Guangling Jiao H.P. Vasantha Rupasinghe 2024-12-01T00:00:00Z https://doi.org/10.1016/j.focha.2024.100748 https://doaj.org/article/43fb2bbd17bf499dab8e09ca8954a995 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2772753X24001448 https://doaj.org/toc/2772-753X 2772-753X doi:10.1016/j.focha.2024.100748 https://doaj.org/article/43fb2bbd17bf499dab8e09ca8954a995 Food Chemistry Advances, Vol 5, Iss , Pp 100748- (2024) Cucumaria frondosa Saponins ANN RSM Frondoside Food processing and manufacture TP368-456 article 2024 ftdoajarticles https://doi.org/10.1016/j.focha.2024.100748 2024-08-05T17:49:07Z Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to optimize saponin extraction from North Atlantic Sea cucumber (Cucumaria frondosa). Ultrasonication-assisted ethanol-based extractions were used in a second-order polynomial and 3n full factorial RSM interconnected with neural design ANN model. A 3-10-2 neural network architecture was constructed to predict the relationship between the independent variables and bioactive compounds adequately. The extracts with the highest frondoside A yield were characterized for different triterpene glycosides (saponins) by high-resolution mass spectrometry (HRMS). A total of ten saponins were detected and tentatively identified including fallaxoside, frondoside, cucumarioside, cercodemasoide, colochiroside, and lefevreioside, with two unknown saponins. Six of the saponins were detected in C. frondosa extracts for the first time. The extract of body walls have a higher concentration of frondoside A (0.73 mg/g DW) than internal organs and tentacles (flowers or aquapharyngeal bulb). The optimized extracts exhibit a significantly higher concentration of polyphenols and saponins when compared with extracts prepared from conventional methods. The ANN model demonstrated a low p and high f values to indicate a perfect good fit for RSM model. The advanced knowledge of saponins of C. frondosa can contribute to the development of novel functional foods and ingredients from C. frondosa and their processing byproducts. Article in Journal/Newspaper Cucumaria frondosa North Atlantic Directory of Open Access Journals: DOAJ Articles Food Chemistry Advances 5 100748
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Cucumaria frondosa
Saponins
ANN
RSM
Frondoside
Food processing and manufacture
TP368-456
spellingShingle Cucumaria frondosa
Saponins
ANN
RSM
Frondoside
Food processing and manufacture
TP368-456
Oladapo F. Fagbohun
Joseph P.M. Hui
Junzeng Zhang
Guangling Jiao
H.P. Vasantha Rupasinghe
Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
topic_facet Cucumaria frondosa
Saponins
ANN
RSM
Frondoside
Food processing and manufacture
TP368-456
description Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to optimize saponin extraction from North Atlantic Sea cucumber (Cucumaria frondosa). Ultrasonication-assisted ethanol-based extractions were used in a second-order polynomial and 3n full factorial RSM interconnected with neural design ANN model. A 3-10-2 neural network architecture was constructed to predict the relationship between the independent variables and bioactive compounds adequately. The extracts with the highest frondoside A yield were characterized for different triterpene glycosides (saponins) by high-resolution mass spectrometry (HRMS). A total of ten saponins were detected and tentatively identified including fallaxoside, frondoside, cucumarioside, cercodemasoide, colochiroside, and lefevreioside, with two unknown saponins. Six of the saponins were detected in C. frondosa extracts for the first time. The extract of body walls have a higher concentration of frondoside A (0.73 mg/g DW) than internal organs and tentacles (flowers or aquapharyngeal bulb). The optimized extracts exhibit a significantly higher concentration of polyphenols and saponins when compared with extracts prepared from conventional methods. The ANN model demonstrated a low p and high f values to indicate a perfect good fit for RSM model. The advanced knowledge of saponins of C. frondosa can contribute to the development of novel functional foods and ingredients from C. frondosa and their processing byproducts.
format Article in Journal/Newspaper
author Oladapo F. Fagbohun
Joseph P.M. Hui
Junzeng Zhang
Guangling Jiao
H.P. Vasantha Rupasinghe
author_facet Oladapo F. Fagbohun
Joseph P.M. Hui
Junzeng Zhang
Guangling Jiao
H.P. Vasantha Rupasinghe
author_sort Oladapo F. Fagbohun
title Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
title_short Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
title_full Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
title_fullStr Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
title_full_unstemmed Application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from North Atlantic sea cucumber
title_sort application of response surface methodology and artificial neural network to optimize the extraction of saponins and polyphenols from north atlantic sea cucumber
publisher Elsevier
publishDate 2024
url https://doi.org/10.1016/j.focha.2024.100748
https://doaj.org/article/43fb2bbd17bf499dab8e09ca8954a995
genre Cucumaria frondosa
North Atlantic
genre_facet Cucumaria frondosa
North Atlantic
op_source Food Chemistry Advances, Vol 5, Iss , Pp 100748- (2024)
op_relation http://www.sciencedirect.com/science/article/pii/S2772753X24001448
https://doaj.org/toc/2772-753X
2772-753X
doi:10.1016/j.focha.2024.100748
https://doaj.org/article/43fb2bbd17bf499dab8e09ca8954a995
op_doi https://doi.org/10.1016/j.focha.2024.100748
container_title Food Chemistry Advances
container_volume 5
container_start_page 100748
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