Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens
The problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/12/3/627 |
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| author | Jovana Vunduk Anita Klaus Vesna Lazić Maja Kozarski Danka Radić Olja Šovljanski Lato Pezo |
| author_facet | Jovana Vunduk Anita Klaus Vesna Lazić Maja Kozarski Danka Radić Olja Šovljanski Lato Pezo |
| author_sort | Jovana Vunduk |
| collection | DOAJ |
| description | The problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of effective compounds of natural origin, also known as “green” agents. Mushrooms appear to be a possible new source of antibiofilm compounds, as has been demonstrated recently. The existing modeling methods are directed toward predicting bacterial biofilm formation, not in the presence of antibiofilm materials. Moreover, the modeling is almost exclusively targeted at biofilms in healthcare, while modeling related to the food industry remains under-researched. The present study applied an Artificial Neural Network (ANN) model to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two very important food-borne bacterial species for food and food-related industries—<i>Listeria monocytogenes</i> and <i>Salmonella enteritidis</i>. The models developed in this study exhibited high prediction quality, as indicated by high r<sup>2</sup> values during the training cycle. The best fit between the modeled and measured values was observed for the inhibition of adhesion. This study provides a valuable contribution to the field, supporting industrial settings during the initial stage of biofilm formation, when these communities are the most vulnerable, and promoting innovative and improved safety management. |
| first_indexed | 2024-03-11T07:00:35Z |
| format | Article |
| id | doaj.art-7402ab2b04ca4c09a0d990e110b682fe |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-11T07:00:35Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-7402ab2b04ca4c09a0d990e110b682fe2023-11-17T09:15:46ZengMDPI AGAntibiotics2079-63822023-03-0112362710.3390/antibiotics12030627Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne PathogensJovana Vunduk0Anita Klaus1Vesna Lazić2Maja Kozarski3Danka Radić4Olja Šovljanski5Lato Pezo6Institute of General and Physical Chemistry, Studenski trg 10-12, 11 158 Belgrade, SerbiaInstitute for Food Technology and Biochemistry, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11 080 Belgrade, SerbiaInstitute for Food Technology and Biochemistry, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11 080 Belgrade, SerbiaInstitute for Food Technology and Biochemistry, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11 080 Belgrade, SerbiaInstitute of General and Physical Chemistry, Studenski trg 10-12, 11 158 Belgrade, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21 000 Novi Sad, SerbiaInstitute of General and Physical Chemistry, Studenski trg 10-12, 11 158 Belgrade, SerbiaThe problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of effective compounds of natural origin, also known as “green” agents. Mushrooms appear to be a possible new source of antibiofilm compounds, as has been demonstrated recently. The existing modeling methods are directed toward predicting bacterial biofilm formation, not in the presence of antibiofilm materials. Moreover, the modeling is almost exclusively targeted at biofilms in healthcare, while modeling related to the food industry remains under-researched. The present study applied an Artificial Neural Network (ANN) model to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two very important food-borne bacterial species for food and food-related industries—<i>Listeria monocytogenes</i> and <i>Salmonella enteritidis</i>. The models developed in this study exhibited high prediction quality, as indicated by high r<sup>2</sup> values during the training cycle. The best fit between the modeled and measured values was observed for the inhibition of adhesion. This study provides a valuable contribution to the field, supporting industrial settings during the initial stage of biofilm formation, when these communities are the most vulnerable, and promoting innovative and improved safety management.https://www.mdpi.com/2079-6382/12/3/627antiadhesionantibiofilmfood-borne pathogensmushroom extractsartificial neural networkmodel |
| spellingShingle | Jovana Vunduk Anita Klaus Vesna Lazić Maja Kozarski Danka Radić Olja Šovljanski Lato Pezo Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens Antibiotics antiadhesion antibiofilm food-borne pathogens mushroom extracts artificial neural network model |
| title | Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens |
| title_full | Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens |
| title_fullStr | Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens |
| title_full_unstemmed | Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens |
| title_short | Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens |
| title_sort | artificial neural network prediction of antiadhesion and antibiofilm forming effects of antimicrobial active mushroom extracts on food borne pathogens |
| topic | antiadhesion antibiofilm food-borne pathogens mushroom extracts artificial neural network model |
| url | https://www.mdpi.com/2079-6382/12/3/627 |
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