Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Nelofer Rubina, Nagasundara Ramanan Ramakrishnan, Raja Noor Zaliha Abd Rahman, Mahiran Basri, Arbakariya B Ariff

    Research output: Contribution to journalArticleResearchpeer-review

    31 Citations (Scopus)

    Abstract

    Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R-2) and adjusted R-2 values for the model. Although the R-2 value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R-2, adjusted R 2, AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32 degrees C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30 degrees C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.
    Original languageEnglish
    Pages (from-to)243 - 254
    Number of pages12
    JournalJournal of Industrial Microbiology & Biotechnology
    Volume39
    Issue number2
    DOIs
    Publication statusPublished - 2012

    Cite this