Abstract
Biosurfactants are surface active, amphiphilic, multifunctional, non-toxic and biodegradable alternatives to chemical surfactants. The present study reported biosurfactant production from yeast Debaryomyces hansenii CBS767 using soybean oil as the low-cost carbon source. Characterization of extracellularly synthesized biosurfactant revealed it as of glycolipid type. D. hansenii was able to synthesize the biosurfactant at diverse physiochemical conditions of temperature, pH, percentage of soyabean oil and incubation period. Isolated cell free extract displayed reducing activity as evident by synthesis of silver nanoparticles of size between 23.7 and 77.5 nm. This reducing action might be due to the presence of certain reducing metabolites in cell extract. Silver nanoparticles displayed distinct spherical morphology. This implies surface functionality of the biosurfactant as capping agent helping in regulating the process of nanoparticle aggregation. To aid the future optimization of biosurfactant production from D. hansenii, five support vector machine models were developed, trained and evaluated utilizing experimental optimization datasets. Significant prediction of E24, oil displacement and surface tension using these models encourage the use of machine learning in the future assessment of biosurfactant production.
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A.P.M. acknowledges the Director, UIET for providing facilities for research. Special thanks to Dr. N. Gupta for technical assistance.
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This study was partial financially supported by Technical Education Quality Improvement Programme (TEQIP) assisted by World Bank project.
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Conceptualization: APM Methodology: APM, MK, Formal analysis and investigation: JK, SK, PK, Writing—original draft preparation: APM, SK, MK; Writing—review and editing: APM, MK, JK, PK Funding acquisition: APM, JK Resources: APM, JK Supervision: APM, MK, JK.
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Kaur, J., Kaur, S., Kumar, M. et al. Studies on production, optimization and machine learning-based prediction of biosurfactant from Debaryomyces hansenii CBS767. Int. J. Environ. Sci. Technol. 19, 8465–8478 (2022). https://doi.org/10.1007/s13762-021-03639-x
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DOI: https://doi.org/10.1007/s13762-021-03639-x