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

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• 作者：Francisco Fern&#xe1 ; ndez-Navarro ; Antonio Valero ; C&#xe9 ; sar Herv&#xe1 ; s-Mart&#xed ; nez ; Pedro A. Guti&#xe9 ; rrez ; Rosa M. Garc&#xed ; a-Gimeno ; Gonzalo Zurera-Cosano
• 关键词：Multi-Classification ; Sensitivity ; Growth/no growth interface ; Accuracy ; Memetic algorithm ; Imbalanced datasets ; Oversampling method ; SMOTE ; S. aureus
• 刊名：International Journal of Food Microbiology
• 出版年：2010
• 出版时间：15 July 2010
• 年：2010
• 卷：141
• 期：3
• 页码：203-212
• 全文大小：496 K

文摘

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.