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

Željko Jakopović; Davor Valinger; Karla Hanousek Čiča; Jasna Mrvčić; Ana-Marija Domijan; Iva Čanak; Deni Kostelac; Jadranka Frece; Ksenija Markov

doi:10.3390/foods13030408

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

Foods. 2024 Jan 26;13(3):408. doi: 10.3390/foods13030408.

Authors

Željko Jakopović¹, Davor Valinger², Karla Hanousek Čiča³, Jasna Mrvčić³, Ana-Marija Domijan⁴, Iva Čanak¹, Deni Kostelac¹, Jadranka Frece¹, Ksenija Markov¹

Affiliations

¹ Laboratory for General Microbiology and Food Microbiology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
² Laboratory for Measurement, Control and Automatisation, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
³ Laboratory for Fermentation and Yeast Technology, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
⁴ Department of Pharmaceutical Botany, Faculty of Pharmacy and Biochemistry, University of Zagreb, Schrottova 39, 10000 Zagreb, Croatia.

Abstract

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^-1) on ethanol production after 12 h. For example, K. marxianus produced 1.320 mg mL^-1 of ethanol, while in the control sample 1.603 µg mL^-1 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^-1) 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.

Keywords: artificial neural networks; fermentation ability; ochratoxin A; oxidative stress; wine yeasts.

Grants and funding

This work was supported by funding from the Faculty of Food Technology and Biotechnology, University of Zagreb, Department of Biochemical Engineering, Laboratory for General Microbiology and Food Microbiology.