A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling

Iranian Journal of Biotechnology
Masoud Salehi BorujeniSaeid Ansari-Mahyari

Abstract

The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth. In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies. Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,'c' and 'σ') of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE). Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method. Applic...Continue Reading

References

Apr 1, 2000·Applied and Environmental Microbiology·J C AugustinV Carlier
Feb 27, 2004·International Journal of Food Microbiology·Florent Baty, Marie-Laure Delignette-Muller
Jul 22, 2006·International Journal of Food Microbiology·Laurent Guillier, Jean-Christophe Augustin
Mar 17, 2007·Risk Analysis : an Official Publication of the Society for Risk Analysis·Arnout R StandaertAnnemie H Geeraerd
Mar 31, 2009·International Journal of Food Microbiology·Tom RossJohn Sumner

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Software Mentioned

NSGA
MATLAB
GA
LSSVM
II

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