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

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.