A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks

The aim of this paper was to examine the effect of different OTA concentrations on the parameters of oxidative stress (glutathione (GSH) and malondialdehyde (MDA) concentrations) and glucose utilization in ethanol production by wine yeasts. In addition to the above, artificial neural networks (ANN)...

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Main Authors: Željko Jakopović, Davor Valinger, Karla Hanousek Čiča, Jasna Mrvčić, Ana-Marija Domijan, Iva Čanak, Deni Kostelac, Jadranka Frece, Ksenija Markov
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Foods
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Online Access:https://www.mdpi.com/2304-8158/13/3/408
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author Željko Jakopović
Davor Valinger
Karla Hanousek Čiča
Jasna Mrvčić
Ana-Marija Domijan
Iva Čanak
Deni Kostelac
Jadranka Frece
Ksenija Markov
author_facet Željko Jakopović
Davor Valinger
Karla Hanousek Čiča
Jasna Mrvčić
Ana-Marija Domijan
Iva Čanak
Deni Kostelac
Jadranka Frece
Ksenija Markov
author_sort Željko Jakopović
collection DOAJ
description The aim of this paper was to examine the effect of different OTA concentrations on the parameters of oxidative stress (glutathione (GSH) and malondialdehyde (MDA) concentrations) and glucose utilization in ethanol production by wine yeasts. In addition to the above, artificial neural networks (ANN) were used to predict the effects of different OTA concentrations on the fermentation ability of yeasts and oxidative stress parameters. The obtained results indicate a negative influence of OTA (4 µg mL<sup>−1</sup>) on ethanol production after 12 h. For example, <i>K. marxianus</i> produced 1.320 mg mL<sup>−1</sup> of ethanol, while in the control sample 1.603 µg mL<sup>−1</sup> of ethanol was detected. However, after 24 h, OTA had no negative effect on ethanol production, since it was higher (7.490 and 3.845 mg mL<sup>−1</sup>) in comparison to control samples. Even low concentrations of OTA affect GSH concentrations, with the highest being detected after 12 and 24 h (up to 16.54 µM), while MDA concentrations are affected by higher OTA concentrations, with the highest being detected at 24 h (1.19 µM). The obtained results with the use of ANNs showed their potential for quantification purposes based on experimental data, while the results of ANN prediction models have shown to be useful for predictions of what outcomes different concentrations of OTA that were not part of experiment will have on the fermentation capacity and oxidative stress parameters of yeasts.
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spelling doaj.art-41b2338d6b5d46b9a088a3d691ec1d4f2024-02-09T15:12:06ZengMDPI AGFoods2304-81582024-01-0113340810.3390/foods13030408A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural NetworksŽeljko Jakopović0Davor Valinger1Karla Hanousek Čiča2Jasna Mrvčić3Ana-Marija Domijan4Iva Čanak5Deni Kostelac6Jadranka Frece7Ksenija Markov8Laboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for Measurement, Control and Automatisation, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for Fermentation and Yeast Technology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for Fermentation and Yeast Technology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaDepartment of Pharmaceutical Botany, Faculty of Pharmacy and Biochemistry, University of Zagreb, Schrottova 39, 10000 Zagreb, CroatiaLaboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaLaboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaThe aim of this paper was to examine the effect of different OTA concentrations on the parameters of oxidative stress (glutathione (GSH) and malondialdehyde (MDA) concentrations) and glucose utilization in ethanol production by wine yeasts. In addition to the above, artificial neural networks (ANN) were used to predict the effects of different OTA concentrations on the fermentation ability of yeasts and oxidative stress parameters. The obtained results indicate a negative influence of OTA (4 µg mL<sup>−1</sup>) on ethanol production after 12 h. For example, <i>K. marxianus</i> produced 1.320 mg mL<sup>−1</sup> of ethanol, while in the control sample 1.603 µg mL<sup>−1</sup> of ethanol was detected. However, after 24 h, OTA had no negative effect on ethanol production, since it was higher (7.490 and 3.845 mg mL<sup>−1</sup>) in comparison to control samples. Even low concentrations of OTA affect GSH concentrations, with the highest being detected after 12 and 24 h (up to 16.54 µM), while MDA concentrations are affected by higher OTA concentrations, with the highest being detected at 24 h (1.19 µM). The obtained results with the use of ANNs showed their potential for quantification purposes based on experimental data, while the results of ANN prediction models have shown to be useful for predictions of what outcomes different concentrations of OTA that were not part of experiment will have on the fermentation capacity and oxidative stress parameters of yeasts.https://www.mdpi.com/2304-8158/13/3/408artificial neural networksfermentation abilityoxidative stressochratoxin Awine yeasts
spellingShingle Željko Jakopović
Davor Valinger
Karla Hanousek Čiča
Jasna Mrvčić
Ana-Marija Domijan
Iva Čanak
Deni Kostelac
Jadranka Frece
Ksenija Markov
A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
Foods
artificial neural networks
fermentation ability
oxidative stress
ochratoxin A
wine yeasts
title A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
title_full A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
title_fullStr A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
title_full_unstemmed A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
title_short A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
title_sort predictive assessment of ochratoxin a s effects on oxidative stress parameters and the fermentation ability of yeasts using neural networks
topic artificial neural networks
fermentation ability
oxidative stress
ochratoxin A
wine yeasts
url https://www.mdpi.com/2304-8158/13/3/408
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