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

Zongur, Alper; Kavuncuoglu, Hatice; Kavuncuoglu, Erhan; Dursun Capar, Tugba; Yalcin, Hasan; Buzpinar, Mehmet Akif

Zongur, Alper; Kavuncuoglu, Hatice; Kavuncuoglu, Erhan; Dursun Capar, Tugba; Yalcin, Hasan; Buzpinar, Mehmet Akif.

Afiliação

Zongur A; Cumhuriyet University, Gemerek Vocation School, Sivas, Turkey.
Kavuncuoglu H; Erciyes University, Engineering Faculty, Department of Food Engineering, Kayseri, Turkey. Electronic address: haticetk@erciyes.edu.tr.
Kavuncuoglu E; Cumhuriyet University, Gemerek Vocation School, Sivas, Turkey.
Dursun Capar T; Erciyes University, Engineering Faculty, Department of Food Engineering, Kayseri, Turkey.
Yalcin H; Erciyes University, Engineering Faculty, Department of Food Engineering, Kayseri, Turkey.
Buzpinar MA; Cumhuriyet University, Gemerek Vocation School, Sivas, Turkey.

J Microbiol Methods ; 192: 106379, 2022 01.

Article em En | MEDLINE | ID: mdl-34808145

Assuntos

Antifúngicos/farmacologia; Fusarium/crescimento & desenvolvimento; Aprendizado de Máquina; Extratos Vegetais/farmacologia; Solanum tuberosum/microbiologia; Viburnum/química; Algoritmos; Antioxidantes/farmacologia; Testes de Sensibilidade a Antimicrobianos por Disco-Difusão/métodos; Microbiologia de Alimentos; Fusarium/efeitos dos fármacos; Fusarium/isolamento & purificação

Palavras-chave

Antifungal effect; Fusarium spp.; Machine learning; Prediction; Viburnum opulus L.

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Solanum tuberosum / Extratos Vegetais / Viburnum / Aprendizado de Máquina / Fusarium / Antifúngicos Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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