An Artificial Neural Network Approach to <i>Escherichia Coli</i> O157:H7 Growth Estimation

Garcíaa-Gimeno, R.M.; Hervás-Martíanez, Cesar; Barco-Alcalá, E.; Zurera‐Cosano, Gonzalo; Sanz-Tapia, E.

doi:10.1111/j.1365-2621.2003.tb05723.x

Cited by 55 publications

(39 citation statements)

References 25 publications

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“…Over the last few years, artificial neural networks, as nonlinear modeling techniques, had been proposed for use in predictive microbiology (Ibarra and Yang, 1999;McKee et al, 2000;Jacoboni et al, 2001;Garcíaa-Gimeno et al, 2003;Hervas et al, 2007;Valero et al, 2007;Khanzadi et al, 2010). In the current study, two neural network modelsGeneral Regression NN Model and Polynomial Net Modelwere used to predict the effects of temperature on Salmonella outbreaks in MS. Several architectures of neural network models were developed to determine the best-fitting models.…”

Section: Nn Modeling Of Salmonella and Temperaturementioning

confidence: 99%

Effects of Climate Change on Salmonella Infections

Akil

Ahmad

Reddy

2014

Foodborne Pathogens and Disease

115

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Background: Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used. Methods: Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN. Results: A seasonal trend in Salmonella infections was observed ( p < 0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R 2 = 0.554; R 2 = 0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed. Conclusion: There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections.

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Section: Nn Modeling Of Salmonella and Temperaturementioning

confidence: 99%

Effects of Climate Change on Salmonella Infections

Akil

Ahmad

Reddy

2014

Foodborne Pathogens and Disease

115

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“…In predictive microbiology, some jobs with ANNs have been used for modelling complex time-dependent bacterial growth [27][28][29], or for predicting growth parameters such as lag time and exponential growth rate [30][31][32][33] as affected by extrinsic biochemical and environmental conditions. Basheer and Hajmeer [27] proposed feedforward neural networks based on Backpropagation minimization criterion applied to the area of predictive microbiology, along with applications for the estimation of bacterial growth parameters and growth curve modelling.…”

Section: Artificial Neural Network In Predictive Microbiologymentioning

confidence: 99%

Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology

Fernández

Hervás

Martínez-Estudillo³

et al. 2011

Applied Soft Computing

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“…With this simple idea in mind, Lambert and Pearson (2000) A similar methodology was also recently used by Arroyo-López et al (2008b) to estimate the MIC and NIC values of the preservatives sorbic and benzoic acids at selected pH values on a native spoilage yeast cocktail isolated from table olives. Neural network (NN) models have also been widely used in predictive microbiology to model microbial response (Hajmeer et al, 1997;Garc ıa-Gimeno et al, 2003;Esnor et al, 2006). An NN model is a computer algorithm which learns or is trained from examples through iteration and automatically derives the mathematical formulae to map the relationships between the input and output data, without any prior knowledge of their relationships.…”

Section: Model Equationmentioning

confidence: 99%

Role of Predictive Microbiology in Food Preservation

Arroyo‐López

Bautista-Gallego

Garrido‐Fernández

2012

Progress in Food Preservation

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The levels of microorganisms in food ecosystems can be controlled by diverse factors such as temperature, pH, water activity or preservatives, which determine their growth, survival or inactivation. Predictive microbiology is a multidisciplinary area of food microbiology which is devoted to studying and predicting, by means of mathematical models, the effects of the mentioned factors on microbial behaviour. The present chapter shows an overview of the successive steps to follow for the successful development and implementation of a predictive model, as well as the most common types of secondary models used in food microbiology. The chapter continues with a general review of the most powerful predictive microbiological tools that are currently available on the web, and finalizes by commenting on the direct implications and applications of predictive microbiology in food preservation.

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An Artificial Neural Network Approach to Escherichia Coli O157:H7 Growth Estimation

Cited by 55 publications

References 25 publications

Effects of Climate Change on Salmonella Infections

Effects of Climate Change on Salmonella Infections

Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology

Role of Predictive Microbiology in Food Preservation

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