Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600054 Tamil Nadu, India.
Department of Computer Science and Engineering, VIT University, Chennai, 632014 Tamil Nadu, India.
Biomed Res Int. 2022 Jul 15;2022:9592365. doi: 10.1155/2022/9592365. eCollection 2022.
In distinct parts of the food web, Fusarium culmorum and Fusarium preserving the relationship can germinate and grow zearalenone (ZEA) and fumonisins (FUM), accordingly. Antimicrobial drugs used to combat these fungi and toxic metabolites raise the risk of hazardous residue in food products, as well as the development of fungus tolerance. For modeling fungal growth and pathogenicity under separate water action ( ) (0.96 and 0.99) and surface temp (20 and 28°C) tyrannies, several machine learning (ML) methodologies (artificial neural, regression trees, and extreme rise enhanced trees) and multiple regression model (MLR) were used also especially in comparison. GR and mycotoxin levels inside the environment often reduced as EOC concentrations grew, although some treatment in association with specific and temperature values caused ZEA production. In terms of predicting the growth rate of F. culmorum and F. maintaining the relationship and the production of ZEA and FUM, random forest techniques outperformed neural network models and extreme gradient boosted trees. The MLR option was the most inefficient. It is the first research to look at the ML potential of bio EVOH products containing EOCs and ambient variables of F. culmorum and F. proliferatum development, as well as the generation of zearalenone and fumonisins. The findings show that these entire novel wrapping technologies, in tandem using machine learning techniques, could be useful in predicting and controlling the dangers connected with fungal species or biotoxins in foodstuff.
在食物网的不同部分，串珠镰刀菌和禾谷镰刀菌能够保持关系并产生玉米赤霉烯酮（ZEA）和伏马菌素（FUM）。用于对抗这些真菌和有毒代谢物的抗菌药物增加了食品中有害残留物以及真菌耐药性发展的风险。为了分别模拟水作用（）（0.96 和 0.99）和表面温度（20 和 28°C）条件下真菌的生长和致病性，还使用了几种机器学习（ML）方法（人工神经网络、回归树和极端梯度提升树）和多元回归模型（MLR）进行比较。尽管某些处理与特定的温度值有关会导致 ZEA 的产生，但环境中的 GR 和霉菌毒素水平通常随着 EOC 浓度的增加而降低。在预测串珠镰刀菌和禾谷镰刀菌的生长速度、ZEA 和 FUM 的产生方面，随机森林技术优于神经网络模型和极端梯度提升树。MLR 选项的效率最低。这是首次研究包含 EOC 和环境变量的 EVOH 产品的 ML 潜力，以及串珠镰刀菌和禾谷镰刀菌的发展和玉米赤霉烯酮和伏马菌素的产生。研究结果表明，这些全新的包装技术，结合使用机器学习技术，可以用于预测和控制与食品中真菌物种或生物毒素相关的危险。
