A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Microorganisms
2.2. OTA Standard
2.3. Yeast Cell and Sample Preparation
2.4. Ultra-High-Performance Liquid Chromatography (UHPLC) for Determination of Ethanol and Residual Glucose Concentrations
2.5. Spectrophotometric Method of Determining GSH and MDA Concentrations
2.6. Statistical Analysis
Artificial Neural Network (ANN) Modelling
3. Results and Discussion
3.1. Effect of OTA on Ethanol Production and Residual Glucose in Medium
3.2. Effect of OTA on Oxidative Stress Parameters, GSH and MDA Concentrations
3.3. Predictive Assessment of ANNs
3.4. Predictive Assessment of GSH, MDA, Ethanol and Glucose Concentrations Based on Hypothetical OTA Concentrations and Hours of Incubation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Compound | Retention Time, tR (min) | The Equation of the Perpendicular Direction | R2 |
---|---|---|---|
Ethanol | 11.072 | y = 55,421x + 1343 | 1.0000 |
Glucose | 4.821 | y = 135,278x − 3377 | 0.9997 |
Sample | γ OTA (µg mL−1) | γ Ethanol (mg mL−1) | |
---|---|---|---|
12 h | 24 h | ||
S. bayanus | 0 | 4.415 ± 0.092 a | 7.050 ± 0.028 a |
2 | 4.525 ± 0.035 a | 7.020 ± 0.028 a | |
4 | 4.375 ± 0.007 a | 6.870 ± 0.057 a * | |
K. marxianus | 0 | 1.603 ± 0.007 b | 1.952 ± 0.007 b |
2 | 1.529 ± 0.009 b | 1.948 ± 0.008 b | |
4 | 1.320 ± 0.030 b | 1.940 ± 0.010 b | |
H. uvarum | 0 | 1.390 ± 0.113 b | 7.365 ± 0.134 a |
2 | 1.305 ± 0.035 b | 7.490 ± 0.071 a | |
4 | 1.205 ± 0.064 b | 7.410 ± 0.042 a | |
P. guilliermondii | 0 | 0.620 ± 0.113 c | 2.605 ± 0.021 c * |
2 | 0.430 ± 0.071 c | 3.845 ± 0.049 c | |
4 | 0.545 ± 0.064 c | 3.295 ± 0.120 c |
Sample | γ OTA (µg mL−1) | γ Glucose (mg mL−1) | |
---|---|---|---|
12 h | 24 h | ||
S. bayanus | 0 | 9.545 ± 0.021 a | n.d. a |
2 | 9.035 ± 0.078 a * | n.d. a | |
4 | 9.400 ± 0.085 a | 0.060 ± 0.057 a | |
K. marxianus | 0 | n.d. b | n.d. a |
2 | n.d. b | n.d. a | |
4 | n.d. b | n.d. a | |
H. uvrum | 0 | 16.67 ± 0.127 c | 0.045 ± 0.021 a |
2 | 16.25 ± 0.064 c * | 0.050 ± 0.014 a | |
4 | 16.79 ± 0.071 c | 0.065 ± 0.007 a | |
P. guilliermondii | 0 | 17.20 ± 0.198 c | 8.145 ± 0.502 c * |
2 | 16.95 ± 0.240 c * | 5.890 ± 0.127 c | |
4 | 17.16 ± 0.283 c | 6.050 ± 0.113 c |
Sample | γ OTA (µg mL−1) | GSH Concentration (µM) | |
---|---|---|---|
12 h | 24 h | ||
S. bayanus | 0 | 11.98 ± 0.04 a | 12.26 ± 0.11 a * |
2 | 16.15 ± 0.11 a * | 14.84 ± 0.28 a | |
4 | 13.36 ± 0.00 a | 15.41 ± 1.30 a | |
K. marxianus | 0 | 11.84 ± 0.04 a * | 11.98 ± 0.04 a * |
2 | 15.19 ± 0.14 a | 13.60 ± 0.46 a | |
4 | 14.10 ± 0.11 a | 13.57 ± 0.14 a | |
H. uvarum | 0 | 12.05 ± 0.04 a * | 12.69 ± 0.04 a |
2 | 16.43 ± 0.32 a | 16.54 ± 0.64 a * | |
4 | 14.31 ± 0.04 a | 13.78 ± 0.07 a | |
P. guilliermondii | 0 | 12.12 ± 0.04 a * | 12.83 ± 0.04 a * |
2 | 13.57 ± 0.71 a | 15.65 ± 0.18 a | |
4 | 13.99 ± 0.07 a | 15.41 ± 0.05 a |
Sample | γ OTA (µg mL−1) | MDA Concentration (µM) | |
---|---|---|---|
12 h | 24 h | ||
S. bayanus | 0 | 0.92 ± 0.01 a | 0.95 ± 0.07 a |
2 | 0.83 ± 0.03 a | 0.92 ± 0.03 a | |
4 | 0.88 ± 0.01 a | 1.19 ± 0.01 a | |
K. marxianus | 0 | 0.92 ± 0.00 a | 0.78 ± 0.01 a |
2 | 0.97 ± 0.02 a | 0.79 ± 0.01 a | |
4 | 1.05 ± 0.01 a | 0.89 ± 0.07 a | |
H. uvarum | 0 | 0.93 ± 0.01 a | 0.79 ± 0.01 a |
2 | 0.83 ± 0.00 a | 1.09 ± 0.02 a | |
4 | 1.07 ± 0.01 a | 0.80 ± 0.04 a | |
P. guilliermondii | 0 | 0.92 ± 0.01 a | 0.90 ± 0.08 a |
2 | 0.73 ± 0.01 a | 0.84 ± 0.06 a | |
4 | 1.01 ± 0.01 a | 1.08 ± 0.03 a |
Number | Network Configuration | Training | Training Error | Test | Test Error | Validation | Validation Error | Hidden Activation | Output Activation |
---|---|---|---|---|---|---|---|---|---|
1 | 3-10-4 | 0.9457 | 0.6132 | 0.9373 | 0.7263 | 0.9249 | 1.1029 | Tanh | Tanh |
2 | 3-10-4 | 0.9536 | 0.8801 | 0.9273 | 0.9729 | 0.9133 | 1.9684 | Tanh | Tanh |
Yeast Strain | t (h) | c OTA (ug/mL) | ANN1 | ANN2 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
c GSH (µM) | c MDA (µM) | (mg/mL) | c Glucose (mg/mL) | c GSH (µM) | c MDA (µM) | c Ethanol (mg/mL) | c Glukose (mg/mL) | |||
S. bayanus | 6 | 1.00 | 15.59 | 0.85 | 0 * | 11.28 | 15.50 | 0.85 | 0 * | 10.66 |
S. bayanus | 6 | 3.00 | 14.40 | 0.85 | 0.06 | 11.87 | 15.05 | 0.94 | 0 * | 9.96 |
S. bayanus | 6 | 10.00 | 10.00 | 1.09 | 1.88 | 10.83 | 16.17 | 1.14 | 0 * | 0 * |
S. bayanus | 18 | 1.00 | 15.56 | 0.80 | 6.57 | 4.24 | 15.91 | 0.84 | 7.34 | 5.08 |
S. bayanus | 18 | 3.00 | 14.90 | 0.99 | 6.63 | 4.56 | 14.39 | 0.94 | 7.35 | 5.94 |
S. bayanus | 18 | 10.00 | 9.53 | 1.09 | 6.10 | 7.13 | 17.11 | 0.26 | 0.00 | 0 * |
S. bayanus | 30 | 1.00 | 15.96 | 0.88 | 7.05 | 0 * | 14.07 | 0.80 | 0.18 | 0 * |
S. bayanus | 30 | 3.00 | 15.62 | 1.09 | 7.07 | 0 * | 14.98 | 1.06 | 0 * | 0 * |
S. bayanus | 30 | 10.00 | 13.06 | 1.19 | 7.07 | 0 * | 17.09 | 0.26 | 0 * | 0 * |
K. marxianus | 6 | 1.00 | 15.39 | 0.92 | 0 * | 17.42 | 14.69 | 0.85 | 0 * | 20.00 |
K. marxianus | 6 | 3.00 | 15.12 | 1.05 | 0 * | 16.46 | 14.86 | 1.01 | 0.35 | 20.00 |
K. marxianus | 6 | 10.00 | 13.37 | 1.17 | 1.22 | 10.39 | 15.44 | 0.96 | 0.00 | 19.99 |
K. marxianus | 18 | 1.00 | 15.44 | 0.94 | 2.63 | 0 * | 15.49 | 0.91 | 4.17 | 0 * |
K. marxianus | 18 | 3.00 | 14.24 | 0.89 | 2.59 | 0.01 | 14.30 | 0.94 | 4.78 | 0 * |
K. marxianus | 18 | 10.00 | 9.30 | 0.94 | 1.32 | 0 * | 17.03 | 0.26 | 0 * | 0 * |
K. marxianus | 30 | 1.00 | 14.62 | 0.62 | 1.04 | 0 * | 14.28 | 0.54 | 0 * | 0 * |
K. marxianus | 30 | 3.00 | 13.82 | 0.85 | 0.72 | 0 * | 13.85 | 0.66 | 0 * | 0 * |
K. marxianus | 30 | 10.00 | 9.23 | 1.10 | 0 * | 0 * | 17.06 | 0.26 | 0 * | 0 * |
H. uvarum | 6 | 1.00 | 14.62 | 0.67 | 1.03 | 20.00 | 14.23 | 0.80 | 0.15 | 20.00 |
H. uvarum | 6 | 3.00 | 14.64 | 0.88 | 1.00 | 20.00 | 14.41 | 0.89 | 0.61 | 20.00 |
H. uvarum | 6 | 10.00 | 13.70 | 1.02 | 2.16 | 20.00 | 14.98 | 0.33 | 0.00 | 20.00 |
H. uvarum | 18 | 1.00 | 15.08 | 0.78 | 5.57 | 2.55 | 15.25 | 0.86 | 2.83 | 3.10 |
H. uvarum | 18 | 3.00 | 15.07 | 1.00 | 5.65 | 2.95 | 15.42 | 1.08 | 4.08 | 2.71 |
H. uvarum | 18 | 10.00 | 12.96 | 1.02 | 6.99 | 2.48 | 16.98 | 0.26 | 0 * | 0 * |
H. uvarum | 30 | 1.00 | 15.33 | 0.80 | 7.45 | 0 * | 16.20 | 0.91 | 7.53 | 2.88 |
H. uvarum | 30 | 3.00 | 14.43 | 0.75 | 7.46 | 0 * | 15.19 | 0.56 | 7.53 | 3.48 |
H. uvarum | 30 | 10.00 | 9.05 | 0.42 | 7.46 | 2.86 | 17.10 | 0.26 | 0 * | 0 * |
P. guilliermondii | 6 | 1.00 | 14.43 | 0.82 | 0.41 | 20.00 | 14.26 | 0.81 | 0 * | 20.00 |
P. guilliermondii | 6 | 3.00 | 14.22 | 0.85 | 0.61 | 20.00 | 14.46 | 0.86 | 0 * | 20.00 |
P. guilliermondii | 6 | 10.00 | 13.43 | 0.92 | 1.11 | 20.00 | 15.02 | 0.27 | 0 * | 20.00 |
P. guilliermondii | 18 | 1.00 | 14.59 | 0.83 | 0 * | 16.17 | 14.46 | 0.78 | 1.16 | 20.00 |
P. guilliermondii | 18 | 3.00 | 14.70 | 1.00 | 0 * | 14.63 | 14.61 | 0.92 | 1.88 | 20.00 |
P. guilliermondii | 18 | 10.00 | 14.33 | 1.11 | 1.11 | 12.71 | 16.83 | 0.26 | 0.00 | 20.00 |
P. guilliermondii | 30 | 1.00 | 15.05 | 0.83 | 6.98 | 2.73 | 15.73 | 0.76 | 7.20 | 0.45 |
P. guilliermondii | 30 | 3.00 | 15.19 | 1.05 | 6.98 | 3.33 | 16.16 | 0.97 | 7.09 | 0 * |
P. guilliermondii | 30 | 10.00 | 12.70 | 0.86 | 7.40 | 4.48 | 17.06 | 0.26 | 0 * | 0 * |
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Jakopović, Ž.; Valinger, D.; Hanousek Čiča, K.; Mrvčić, J.; Domijan, A.-M.; Čanak, I.; Kostelac, D.; Frece, J.; Markov, K. A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks. Foods 2024, 13, 408. https://doi.org/10.3390/foods13030408
Jakopović Ž, Valinger D, Hanousek Čiča K, Mrvčić J, Domijan A-M, Čanak I, Kostelac D, Frece J, Markov K. A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks. Foods. 2024; 13(3):408. https://doi.org/10.3390/foods13030408
Chicago/Turabian StyleJakopović, Željko, Davor Valinger, Karla Hanousek Čiča, Jasna Mrvčić, Ana-Marija Domijan, Iva Čanak, Deni Kostelac, Jadranka Frece, and Ksenija Markov. 2024. "A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks" Foods 13, no. 3: 408. https://doi.org/10.3390/foods13030408
APA StyleJakopović, Ž., Valinger, D., Hanousek Čiča, K., Mrvčić, J., Domijan, A.-M., Čanak, I., Kostelac, D., Frece, J., & Markov, K. (2024). A Predictive Assessment of Ochratoxin A’s Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks. Foods, 13(3), 408. https://doi.org/10.3390/foods13030408