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

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Abstract

This work was aimed at applying machine learning techniques to predict Semi-Solid Fermentation (SSF) yield using Cunninghamella echinulata fungus for alginate lyase production using Sargassum macroalgae genus as substrate. Approximately 115 experimental data were obtained as a result of the variation of the following factors: tc (cultivation time) (1–7 days), Ci (inoculum concentration) (2.106–1.107 spore/gbiomass), pHNutri (nutrient solution pH) (2.5–8.5), uw/w (moisture content) (65–85% w/w) and %inducer (sodium alginate concentration) (0–33.33% w/w), which were the input variables and alginate lyase enzyme concentration (U mL−1) as the output variable. The programming language employed was Python, through its SciPylibrary. The non-linear relationship between fermentation, the factors and objectives were determined using the learning capacity of Artificial Neural Networks (ANN) and the Support Vector Machine (SVM), through the correlation coefficient (R2). For ANN, with 15 neurons and using the logistic activation function, a R2 = 0.877 was obtained. SVM, with a polynomial kernel function obtained R2 = 0.821. In both models, the variables with influence were tc, uw/w, %inducer and pHNutri. Ci did not have any significant influence in the range studied. In both methods, the R2 found was considered satisfactory contributing to the optimization of the alginate lyase production using Cunninghamella echinulata.

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Acknowledgements

The authors are grateful for the financial support by CNPq—Brazil (National Council of Research and Development of Brazil)—Process numbers 313195/2019-6, 440070/2019-8 and 407274/2018-9 and by calling 16/2020.

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Authors and Affiliations

  1. Center of Technology, Federal University of Alagoas, Av. Lourival Melo Mota Km 14, s/n, Maceió, AL, 57072-970, Brazil

    Carlos Eduardo De Farias Silva, Giselly Yanara Silva Canuto Mendes Costa, João Victor Ferro, Frede de Oliveira Carvalho, Brígida Maria Villar da Gama, Lucas Meili & Renata Maria Rosas Garcia Almeida

  2. Institute of Chemistry and Biotechnology, Federal University of Alagoas, Av. Lourival Melo Mota Km 14, s/n, Maceió, AL, 57072-970, Brazil

    Margarete Cabral dos Santos Silva & Josealdo Tonholo

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by GYSCMC, JVF, CEFS and FOC. The first draft of the manuscript was written by BMVG, Lucas Meili and CEFS and all authors evaluated previous versions of the manuscript. All authors read and approved the final manuscript.

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De Farias Silva, C.E., Costa, G.Y.S.C.M., Ferro, J.V. et al. Application of machine learning to predict the yield of alginate lyase solid-state fermentation by Cunninghamella echinulata: artificial neural networks and support vector machine. Reac Kinet Mech Cat 135, 3155–3171 (2022). https://doi.org/10.1007/s11144-022-02293-9

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