Development of a multi-classification neural network model to determine the microbial growth/no growth interface

Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al...

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Bibliographic Details
Published in:International journal of food microbiology 2010-07, Vol.141 (3), p.203-212
Main Authors: Fernández-Navarro, Francisco, Valero, Antonio, Hervás-Martínez, César, Gutiérrez, Pedro A., García-Gimeno, Rosa M., Zurera-Cosano, Gonzalo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biological and medical sciences
Food industries
Food Microbiology
food safety
Fundamental and applied biological sciences. Psychology
growth boundaries
Growth/no growth interface
Imbalanced datasets
Memetic algorithm
microbial growth
Models, Biological
Multi-Classification
neural networks
Neural Networks (Computer)
Oversampling method
S. aureus
Sensitivity
SMOTE
Smote Memetic Radial Basis Function
Staphylococcus aureus
Staphylococcus aureus - chemistry
Staphylococcus aureus - growth & development
Staphylococcus aureus - metabolism
Temperature
Water - metabolism
water activity
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Summary:Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al. [Valero, A., Pérez-Rodríguez, F., Carrasco, E., Fuentes-Alventosa, J.M., García-Gimeno, R.M., Zurera, G., 2009. Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity. International Journal of Food Microbiology 133 (1–2), 186–194] belonging to growth/no growth conditions of Staphylococcus aureus against temperature, pH and a w were divided into three categorical classes: growth (G), growth transition (GT) and no growth (NG). Subsequently, they were modelled by using a Radial Basis Function Neural Network (RBFNN) in order to create a multi-classification model that was able to predict the probability of belonging at one of the three mentioned classes. The model was developed through an over sampling procedure using a memetic algorithm (MA) in order to balance in part the size of the classes and to improve the accuracy of the classifier. The multi-classification model, named Smote Memetic Radial Basis Function (SMRBF) provided a quite good adjustment to data observed, being able to correctly classify the 86.30% of training data and the 82.26% of generalization data for the three observed classes in the best model. Besides, the high number of replicates per condition tested ( n = 30) produced a smooth transition between growth and no growth. At the most stringent conditions, the probability of belonging to class GT was higher, thus justifying the inclusion of the class in the new model. The SMRBF model presented in this study can be used to better define microbial growth/no growth interface and the variability associated to these conditions so as to apply this knowledge to a food safety in a decision-making process.
ISSN:0168-1605
1879-3460
DOI:10.1016/j.ijfoodmicro.2010.05.013