Development of a Prediction Software for the Growth Kinetics of Pseudomonas spp. in Culture Media using Various Primary Models

  Background and Objective: Pseudomonas spp. are bacteria with the widest effects on food spoilage. These bacteria can be found in several environments such as soil and water. The major purpose of this study was to develop a software; by which, the growth behaviours of Pseudomonas spp. in culture media could be predicted. Material and Methods: A total number of 509 bacterial data points of Pseudomonas spp. in culture media were collected from the ComBase database. Temperature and pH were used as the major prediction variables for the description of Pseudomonas spp. behaviours in culture media. Modified Gompertz, Baranyi and Huang models, the most commonly used models in predictive food microbiology to predict the count of microorganisms, were used as well. Fitting capability of each model was assessed and compared with other capabilities considering their statistical indices of the root mean square error, RMSE; coefficient of determination, R2; corrected Akaike information criterion, AICc; and Bayesian information criterion, BIC. Results and Conclusion: Huang model provided better predictions with 0.951 of R2 and 0.825 of RMSE, compared to those of traditionally used models. Prediction capability of the Huang model was assessed considering externally collected data from the ComBase database. Huang model in the validation process provided satisfactory statistical indices (bias factor = 1.027 and accuracy factor = 1.075). These results have revealed that Huang model can be reliably used as a model of describing the growth behaviours of Pseudomonas spp. Furthermore, developed software in this study includes significant potentials for predicting Pseudomonas counts in culture media. Conflict of interest: The authors declare no conflict of interest