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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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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1810440890872758272 |