Comparative Analysis of Machine Learning Methods to Predict Growth of <i>F. sporotrichioides</i> and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils

Abstract

The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst.

Keywords

• <i>Fusarium sporotrichioides</i>
• ethylene-vinyl alcohol copolymers
• essential oil pure components
• machine learning
• fungal growth
• T-2 toxin