In this section
@article{Zhu:2024:10.1016/j.fbio.2024.104797,
author = {Zhu, Y and Gao, Y and Wang, W and Kan, W and Tang, C and Wu, L},
doi = {10.1016/j.fbio.2024.104797},
journal = {Food Bioscience},
title = {Growth boundary of Fusarium graminearum spores as a function of temperature, pH, and H<inf>2</inf>S based on neural network},
url = {http://dx.doi.org/10.1016/j.fbio.2024.104797},
volume = {61},
year = {2024}
}
TY - JOUR
AB - Fusarium graminearum (F. graminearum) poses a substantial threat to global food security, with its impact closely linked to environmental conditions. This study aims to anticipate F. graminearum spore growth across diverse environments and assess alterations in the fungistatic capacity of hydrogen sulfide (H2S) gas in response to environmental variations. The experimental data, namely spore germination ratios, are monitored and recorded at hourly intervals across 100 permutations comprising four distinct temperature levels, five varying pH values, and five diverse concentrations of H2S. The investigation integrates three environmental factors as input variables, utilizing a feedforward neural network to establish growth boundaries based on spore germination ratios. The accuracy of models at various stages–training, validation, and testing–is evaluated using a confusion matrix. Logistic regression models are employed for effectiveness comparison with neural networks. Results indicate that neural networks successfully predict F. graminearum spore germination events. Notably, H2S does not influence the temperature preferences of spores. Temperature has a more substantial impact on the fungistatic ability of H2S compared to pH. The study reveals the adaptability of F. graminearum spores to adverse conditions over time. This research delves into temperature, pH, and H2S synergistic effects on spore germination, contributing to predictive models for F. graminearum and enhancing our understanding of how environmental factors regulate its growth.
AU - Zhu,Y
AU - Gao,Y
AU - Wang,W
AU - Kan,W
AU - Tang,C
AU - Wu,L
DO - 10.1016/j.fbio.2024.104797
PY - 2024///
SN - 2212-4292
TI - Growth boundary of Fusarium graminearum spores as a function of temperature, pH, and H<inf>2</inf>S based on neural network
T2 - Food Bioscience
UR - http://dx.doi.org/10.1016/j.fbio.2024.104797
VL - 61
ER -
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