Machine learning approach for predicting the antifungal effect of gilaburu (Viburnum opulus) fruit extracts on Fusarium spp. isolated from diseased potato tubers

Zöngür A., Kavuncuoglu H., Kavuncuoğlu E., Dursun Çapar T., Yalçın H., Buzpınar M. A.

JOURNAL OF MICROBIOLOGICAL METHODS, cilt.192, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 192
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.mimet.2021.106379
  • Dergi Adı: JOURNAL OF MICROBIOLOGICAL METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, Environment Index, Food Science & Technology Abstracts, MEDLINE, Veterinary Science Database
  • Anahtar Kelimeler: Prediction, Viburnum opulusL, Fusarium spp, Antifungal effect, Machine learning, COEFFICIENT, MYCOTOXINS
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.