to Predictive Microbiology

[1]  A H Geeraerd,et al.  Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products. , 1998, International journal of food microbiology.

[2]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[3]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[4]  John Durkin,et al.  Expert systems - design and development , 1994 .

[5]  J G Phillips,et al.  Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri. , 1994, International journal of food microbiology.

[6]  John G Phillips,et al.  Response Surface Model for Predicting the Effects of Temperature pH, Sodium Chloride Content, Sodium Nitrite Concentration and Atmosphere on the Growth of Listeria monocytogenes. , 1990, Journal of food protection.

[7]  J Olley,et al.  Relationship between temperature and growth rate of bacterial cultures , 1982, Journal of bacteriology.

[8]  T. A. Roberts,et al.  A response surface study on the role of some environmental factors affecting the growth of Saccharomyces cerevisiae. , 1995, International journal of food microbiology.

[9]  Da‐Wen Sun,et al.  Predictive food microbiology for the meat industry: a review. , 1999, International journal of food microbiology.

[10]  P. Briggs,et al.  The concept and application of expert systems in the field of microbiological safety , 1993, Journal of Industrial Microbiology.

[11]  J. Baranyi Notes on reparameterization of bacterial growth curves , 1992 .

[12]  M W Peck,et al.  Modelling the growth, survival and death of microorganisms in foods: the UK food micromodel approach. , 1994, International journal of food microbiology.

[13]  R. C. Whiting,et al.  A quantitative model for bacterial growth and decline , 1992 .

[14]  César Hervás-Martínez,et al.  Correction of Temperature Variations in Kinetic-Based Determinations by Use of Pruning Computational Neural Networks in Conjunction with Genetic Algorithms , 2000, J. Chem. Inf. Comput. Sci..

[15]  R. C. Whiting,et al.  Modeling bacterial survival in unfavorable environments , 1993, Journal of Industrial Microbiology.

[16]  Robert L. Buchanan,et al.  USING SPREADSHEET SOFTWARE FOR PREDICTIVE MICROBIOLOGY APPLICATIONS , 1990 .

[17]  M. Adams,et al.  Microbiología de los alimentos , 1997 .

[18]  K van't Riet,et al.  A decision support system for the prediction of microbial food safety and food quality. , 1998, International journal of food microbiology.

[19]  M. H. Zwietering,et al.  Modeling of Bacterial Growth with Shifts in Temperature , 1994, Applied and environmental microbiology.

[20]  Mohammad A. Ketabchi,et al.  Modeling Application Domains , 1997, Data Knowl. Eng..

[21]  Kenneth W. Davies,et al.  Quantitative microbiological risk assessment: principles applied to determining the comparative risk of salmonellosis from chicken products. , 1998, Journal of food protection.

[22]  Michael Georgiopoulos,et al.  Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization , 1997, Neurocomputing.

[23]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[24]  J P Flandrois,et al.  Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth , 1995, Applied and environmental microbiology.

[25]  Ali A. Minai,et al.  Back-propagation heuristics: a study of the extended delta-bar-delta algorithm , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[26]  Frank M. Rombouts,et al.  A decision support system for prediction of microbial spoilage in foods , 2005, Journal of Industrial Microbiology.

[27]  T. Ross Indices for performance evaluation of predictive models in food microbiology. , 1996, The Journal of applied bacteriology.

[28]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[29]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[30]  J. Monod The Growth of Bacterial Cultures , 1949 .

[31]  César Hervás-Martínez,et al.  Computational Neural Networks for Resolving Nonlinear Multicomponent Systems Based on Chemiluminescence Methods , 1998, J. Chem. Inf. Comput. Sci..

[32]  J F Van Impe,et al.  Dynamic mathematical model to predict microbial growth and inactivation during food processing , 1992, Applied and environmental microbiology.

[33]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[34]  J Andrew Hudson,et al.  Construction of and Comparisons Between Response Surface Models for Aeromonas hydrophila ATCC 7966 and a Food Isolate Under Aerobic Conditions. , 1992, Journal of food protection.

[35]  César Hervás-Martínez,et al.  Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. , 2002, International journal of food microbiology.

[36]  John von Neumann,et al.  The Computer and the Brain , 1960 .

[37]  Julie E. Jones A real-time database/models base/expert system in predictive microbiology , 2005, Journal of Industrial Microbiology.

[38]  J Baranyi,et al.  Validating and comparing predictive models. , 1999, International journal of food microbiology.

[39]  César Hervás-Martínez,et al.  Use of Pruned Computational Neural Networks for Processing the Response of Oscillating Chemical Reactions with a View to Analyzing Nonlinear Multicomponent Mixtures , 2001, Journal of chemical information and computer sciences.

[40]  J. V. Van Impe,et al.  Effect of dissolved carbon dioxide and temperature on the growth of Lactobacillus sake in modified atmospheres. , 1998, International journal of food microbiology.

[41]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[42]  P Dalgaard,et al.  Predictive modelling of the growth and survival of Listeria in fishery products. , 2000, International journal of food microbiology.

[43]  R L Buchanan,et al.  Expansion of response surface models for the growth of Escherichia coli O157:H7 to include sodium nitrite as a variable. , 1994, International journal of food microbiology.

[44]  M. B. Cole,et al.  Comparison of a quadratic response surface model and a square root model for predicting the growth rate of Yersinia enterocolitic , 1992 .

[45]  Digvir S. Jayas,et al.  Microbial growth modelling with artificial neural networks. , 2001 .

[46]  J G Phillips,et al.  Expanded response surface model for predicting the effects of temperatures, pH, sodium chloride contents and sodium nitrite concentrations on the growth rate of Yersinia enterocolitica. , 1995, The Journal of applied bacteriology.

[47]  Tom Ross,et al.  Development of Pseudomonas Predictor , 1997 .

[48]  J Baranyi,et al.  Mathematics of predictive food microbiology. , 1995, International journal of food microbiology.

[49]  A. Lebert,et al.  Modelling the growth of Listeria monocytogenes in dynamic conditions. , 2000, International journal of food microbiology.

[50]  Rashmi Pandya Expert systems: design and development, and Expert systems catalog of applications, by John Durkin. Prentice Hall International , 1999, Knowl. Eng. Rev..

[51]  I A Basheer,et al.  Computational neural networks for predictive microbiology: I. Methodology. , 1997, International journal of food microbiology.

[52]  M N Hajmeer,et al.  Computational neural networks for predictive microbiology. II. Application to microbial growth. , 1997, International journal of food microbiology.

[53]  D. Kilsby,et al.  Hazard analysis applied to microbial growth in foods: development of mathematical models describing the effect of water activity. , 1983, The Journal of applied bacteriology.

[54]  B. Dahhou,et al.  From a rule-based to a predictive qualitative model-based approach using automated model generation , 1998 .

[55]  Miguel Peris,et al.  Present and future of expert systems in food analysis , 2002 .

[56]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[57]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[58]  T. A. Roberts,et al.  Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. , 1988, International journal of food microbiology.

[59]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[60]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[61]  K. Davey Applicability of the Davey (linear Arrhenius) predictive model to the lag phase of microbial growth , 1991 .

[62]  R. C. Whiting,et al.  Time of growth model for proteolytic Clostridium botulinum , 1993 .

[63]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[64]  R. Buchanan,et al.  RESPONSE SURFACE MODELS FOR THE EFFECTS OF TEMPERATURE, pH, SODIUM CHLORIDE, AND SODIUM NITRITE ON THE AEROBIC AND ANAEROBIC GROWTH OF STAPHYLOCOCCUS AUREUS 196E , 1993 .

[65]  R. C. Whiting,et al.  When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves , 1997 .

[66]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.