Application of machine learning to predict the yield of alginate lyase solid-state fermentation by Cunninghamella echinulata: artificial neural networks and support vector machine

Author:

De Farias Silva Carlos EduardoORCID,Costa Giselly Yanara Silva Canuto Mendes,Ferro João Victor,de Oliveira Carvalho FredeORCID,da Gama Brígida Maria VillarORCID,Meili LucasORCID,dos Santos Silva Margarete Cabral,Almeida Renata Maria Rosas Garcia,Tonholo JosealdoORCID

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Physical and Theoretical Chemistry,Catalysis

Reference79 articles.

1. Guiry MD (2014) AlgaeBase. World-wide eletronic publication. National University of Ireland, Galway. http://www.algaebase.org. Accessed 8 Sept 2021

2. Torres MR, Sousa APA, Silva Filho EAT et al (2007) Extraction and physicochemical characterization of Sargassum vulgare alginate from Brazil. Carbohydr Res 342(14):2067–2074. https://doi.org/10.1016/j.carres.2007.05.022

3. Karina T (2017) Estudo decifra mistério das algas que invadiram o Brasil. https://exame.com/ciencia/estudo-decifra-misterio-das-algas-que-invadiram-o-brasil/#:~:text=Embora%20as%20algas%20do%20g%C3%AAnero,o%20banho%20de%20mar%20invi%C3%A1vel. Accessed 07 Sept 2021

4. Trinanes J, Putman NF, Goni G et al (2021) Monitoring pelagic Sargassum inundation potential for coastal communities. J Oper Oceanogr 1:1–12. https://doi.org/10.1080/1755876X.2021.1902682

5. Bonilla-Loaiza AM, Rodríguez-Jasso RM, Belmares R et al (2022) Fungal Proteins from Sargassum spp. using solid-state fermentation as a green bioprocess strategy. Molecules 27(12):3887. https://doi.org/10.3390/molecules27123887

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