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


1 Electronics Department, Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran

2 Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran

3 Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan

4 Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran



Background: The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.
Objectives: 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.
Materials and Methods: 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).
Results: 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.
Conclusion: Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of ‘c’ and ‘σ’ parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.