Cumhuriyet University, Gemerek Vocation School, Sivas, Turkey.
Erciyes University, Engineering Faculty, Department of Food Engineering, Kayseri, Turkey.
J Microbiol Methods. 2022 Jan;192:106379. doi: 10.1016/j.mimet.2021.106379. Epub 2021 Nov 19.
This work addresses the mathematical model building to detect the diameter of the inhibition zone of gilaburu (Viburnum opulus L.) extract against eight different Fusarium strains isolated from diseased potato tubers. Gilaburu extracts were obtained with acetone, ethanol or methanol. The isolated Fusarium strains were: F. solani, F. oxysporum, F. sambucinum, F. graminearum, F. coeruleum, F. sulphureum, F. auneaceum and F. culmorum. In general, it was observed that ethanolic extracts showed highest antifungal activity. The antifungal activity of extracts was evaluated with machine learning (ML) methods. Several ML methods (classification and regression trees (CART), support vector machines (SVM), k-Nearest Neighbors (k-NN), artificial neural network (ANN), ensemble algorithms (EA), AdaBoost (AB) algorithm, gradient boosting (GBM) algorithm, random forests (RF) bagging algorithm and extra trees (ET)) were applied and compared for modeling fungal growth. From this research, it is clear that ML methods have the lowest error level. As a result, ML methods are reliable, fast, and cheap tools for predicting the antifungal activity of gilaburu extracts. These encouraging results will attract more research efforts to implement ML into the field of food microbiology instead of traditional methods.
这项工作涉及建立数学模型来检测吉拉布鲁（Viburnum opulus L.）提取物对从患病马铃薯块茎中分离出的八种不同镰刀菌菌株的抑菌圈直径的抑制作用。用丙酮、乙醇或甲醇提取吉拉布鲁。分离出的镰刀菌菌株为：茄病镰刀菌、尖孢镰刀菌、伏革镰刀菌、禾谷镰刀菌、亮镰刀菌、硫色镰刀菌、旋孢镰刀菌和茄病镰刀菌。总的来说，观察到乙醇提取物显示出最高的抗真菌活性。使用机器学习（ML）方法评估提取物的抗真菌活性。应用了几种 ML 方法（分类和回归树（CART）、支持向量机（SVM）、k-最近邻（k-NN）、人工神经网络（ANN）、集成算法（EA）、AdaBoost（AB）算法、梯度提升（GBM）算法、随机森林（RF）袋算法和 Extra Trees（ET））对真菌生长进行建模，并进行了比较。从这项研究中可以清楚地看出，ML 方法的错误水平最低。因此，ML 方法是预测吉拉布鲁提取物抗真菌活性的可靠、快速且廉价的工具。这些令人鼓舞的结果将吸引更多的研究努力将 ML 应用于食品微生物学领域，而不是传统方法。
