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Quantitative analysis of biosurfactants in water samples by a modified oil spreading technique.

Haoshuai LiChao FangXinrui LiuKaiwen BaoYang LiMutai Bao
Published in: RSC advances (2023)
The oil spreading technique relies on biosurfactant to reduce the surface tension of an oil film and form an oil spreading ring in the center, and then judges the content of biosurfactant according to the diameter of the spreading ring. However, the instability and large errors of the traditional oil spreading technique limit its further application. In this paper, we modified the traditional oil spreading technique by optimizing the oily material, image acquisition and calculation method, which improves the accuracy and stability of the quantification of biosurfactant. We screened lipopeptides and glycolipid biosurfactants for rapid and quantitative analysis of biosurfactant concentrations. By selecting areas by color done by the software to modify image acquisition, the results showed that the modified oil spreading technique has a good quantitative effect, reflected in the concentration of biosurfactant being proportional to the diameter of the sample droplet. More importantly, using the pixel ratio method instead of the diameter measurement method to optimize the calculation method, the region selection was more exact, and the accuracy of the data results was high, and the calculation efficiency was improved significantly. Finally, the contents of rhamnolipid and lipopeptide in oilfield water samples were judged by the modified oil spreading technique, the relative errors were analyzed according to the different substances as the standard, and the quantitative measurement and analysis of oilfield water samples (the produced water of Zhan 3-X24 and the injected water of the estuary oil production plant) were realized. The study provides a new perspective on the accuracy and stability of the method in the quantification of biosurfactant, and provided some theoretical and data support for the study of the microbial oil displacement technology mechanism.
Keyphrases
  • fatty acid
  • bacillus subtilis
  • machine learning
  • drinking water
  • emergency department
  • single cell