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Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation

Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content...

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Autores principales: Fourati, Mariam, Smaoui, Slim, Ennouri, Karim, Ben Hlima, Hajer, Elhadef, Khaoula, Chakchouk-Mtibaa, Ahlem, Sellem, Imen, Mellouli, Lotfi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815538/
https://www.ncbi.nlm.nih.gov/pubmed/31737081
http://dx.doi.org/10.1155/2019/1542615
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author Fourati, Mariam
Smaoui, Slim
Ennouri, Karim
Ben Hlima, Hajer
Elhadef, Khaoula
Chakchouk-Mtibaa, Ahlem
Sellem, Imen
Mellouli, Lotfi
author_facet Fourati, Mariam
Smaoui, Slim
Ennouri, Karim
Ben Hlima, Hajer
Elhadef, Khaoula
Chakchouk-Mtibaa, Ahlem
Sellem, Imen
Mellouli, Lotfi
author_sort Fourati, Mariam
collection PubMed
description Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R(2)) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 10(4) CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth.
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spelling pubmed-68155382019-11-17 Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation Fourati, Mariam Smaoui, Slim Ennouri, Karim Ben Hlima, Hajer Elhadef, Khaoula Chakchouk-Mtibaa, Ahlem Sellem, Imen Mellouli, Lotfi Evid Based Complement Alternat Med Research Article Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R(2)) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 10(4) CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth. Hindawi 2019-10-13 /pmc/articles/PMC6815538/ /pubmed/31737081 http://dx.doi.org/10.1155/2019/1542615 Text en Copyright © 2019 Mariam Fourati et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Fourati, Mariam
Smaoui, Slim
Ennouri, Karim
Ben Hlima, Hajer
Elhadef, Khaoula
Chakchouk-Mtibaa, Ahlem
Sellem, Imen
Mellouli, Lotfi
Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title_full Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title_fullStr Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title_full_unstemmed Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title_short Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation
title_sort multiresponse optimization of pomegranate peel extraction by statistical versus artificial intelligence: predictive approach for foodborne bacterial pathogen inactivation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815538/
https://www.ncbi.nlm.nih.gov/pubmed/31737081
http://dx.doi.org/10.1155/2019/1542615
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