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Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production

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Abstract

An accurate and reliable forecast of biosurfactant production with minimum error is useful in any bioprocess engineering. Bacterial isolate FKOD36 capable of producing biosurfactant was isolated in this study and pre-inoculums was prepared from the agar slants in a small test tube and incubated at 30 °C for 24 h at 120 rpm. Due to inherent non-linearity characteristics of the data set in a bioprocess, conventional modeling techniques are not adequate for predicting biosurfactant production in a microbiological process. The main contribution of the study was to compare two soft-computing models, i.e., support vector regression (SVR) and support vector regression coupled with firefly algorithm to evaluate the best performance of the two mentioned models. Based on the results it was noted that support vector regression coupled with firefly algorithm performs better compared to the simple SVR.

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Prishtina, Kneza Milosa 7, Kosovska Mitrovica, Serbia

    Srdjan Jovic, Dejan Guresic & Ljiljana Babincev

  2. Faculty of Agriculture, University of Pristina, Kopaonicka, Lešak, 38219, Serbia

    Nenad Draskovic

  3. Faculty of Natural Sciences and Mathematics, University of Pristina, Kosovska Mitrovica, Ive Lola Ribara 29, Kosovska Mitrovica, 38220, Serbia

    Vidosav Dekic

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  1. Srdjan Jovic
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  2. Dejan Guresic
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  3. Ljiljana Babincev
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  4. Nenad Draskovic
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  5. Vidosav Dekic
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Correspondence to Srdjan Jovic.

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Jovic, S., Guresic, D., Babincev, L. et al. Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production. Bioprocess Biosyst Eng 42, 1695–1699 (2019). https://doi.org/10.1007/s00449-019-02165-y

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  • DOI: https://doi.org/10.1007/s00449-019-02165-y

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