1. bookVolume 57 (2018): Edizione 3 (January 2018)
Dettagli della rivista
Prima pubblicazione
01 Mar 1961
Frequenza di pubblicazione
4 volte all'anno
Inglese, Polacco
Accesso libero


Pubblicato online: 26 Feb 2022
Volume & Edizione: Volume 57 (2018) - Edizione 3 (January 2018)
Pagine: 229 - 243
Ricevuto: 01 Nov 2017
Accettato: 01 Feb 2018
Dettagli della rivista
Prima pubblicazione
01 Mar 1961
Frequenza di pubblicazione
4 volte all'anno
Inglese, Polacco

Akmel D.C., Tapé T. i wsp.: Quantitative assessment of the microbiological risk associated with the consumption of attieke in Côte d’Ivoire. Food Control, 81, 65–73 (2017)10.1016/j.foodcont.2017.05.035Search in Google Scholar

Alber S.A., Schaffner D.W.: Evaluation of data transformations used with squere root and Schoolfield models for predicting bacterial growth rate. App. Environ. Microbiol. 58, 3337–3342 (1992)Search in Google Scholar

Alber S.A., Schaffner D.W.: New modified squere root and Schoolfield models for predicting bacterial growth rate as a function of temperature. J. Ind. Microbiol. 12, 206–210 (1993)Search in Google Scholar

Amina M., Panagou E.Z., Kodogiannis V.S., Nychas G-J.E.; Wavelet neural networks for modeling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk. Chemom. Intell. Lab. Syst. Discipline. 103, 170–183 (2010)Search in Google Scholar

Aspridou Z., Koutsoumanis K.P.: Individual cell heterogeneity as variability source in population dynamics of microbial inactivation, Food Microbiol. 45, 216–221 (2015)Search in Google Scholar

Atsamnia D., Hamadache M., Hanini S., Benkortbi O., Oukrif D.: Prediction of the antibacterial activity of garlic extract on E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks. LWT – Food Science and Technology, 82, 287–295 (2017)10.1016/j.lwt.2017.04.053Search in Google Scholar

Augustin J-Ch., Carlier V.: Modelling the growth rate of Listeria monocytogenes with a multiplicative type model including interactions between environmental factors. Int. J. Food Microbiol. 56, 53–70 (2000)10.1016/S0168-1605(00)00224-5Search in Google Scholar

Baranyi J., da Silva N.S.: The use of predictive models to optimize risk of decisions. Int. J. Food Microbiol. 240, 19–23 (2017)10.1016/j.ijfoodmicro.2016.10.016Search in Google Scholar

Baranyi J., Pin C.: Estimating bacterial growth parameters by means of detection times. Appl. Environ. Microbiol. 65, 732–736 (1999)Search in Google Scholar

Baranyi J., Roberts T.A., McClure P.: A non-autonomous differential equation to model bacterial growth. Food Microbiol. 10, 43–59 (1993)10.1006/fmic.1993.1005Search in Google Scholar

Barker-Reid F., Harper G.A., Hamilton A.J.: Affluent effluent: growing vegetables with wastewater in Melbourne, Australia – a wealthy but bone-dry city. Irrigat. Drain Syst. 24, 79–94 (2010)10.1007/s10795-009-9082-xSearch in Google Scholar

Basheer I.A., Hajmeer M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiological Methods, Neural Computting in Micrbiology, 43, 3–31 (2000)10.1016/S0167-7012(00)00201-3Search in Google Scholar

Bowman J., McMeekin T., McQuestion O., Mellefont L., Ross T., Tamplin M.: The future of predictive microbiology: strategic research, innovative applications and great expectations. Int. J. Food Microbiol. 128, 2–9 (2008)10.1016/j.ijfoodmicro.2008.06.02618703250Search in Google Scholar

Brucker S., Albrecht A., Petersen B., Kreyenschmidt J.: A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains. Food Control, 29, 451–460 (2013)10.1016/j.foodcont.2012.05.048Search in Google Scholar

Brul S., Mensonides F.I.C., Hellingwerf K.J., Teixeira de Mattos J.M.: Microbial systems biology: New frontiers open to predictive microbiology. Int. J. Food Microbiol. 5th International Conference on Predictive Modelling in Foods, 128, 16–21 (2008)Search in Google Scholar

Buchanan R.L.: Predictive food microbiology. Trends Food Sci. Technol. 4, 6–11 (1993)Search in Google Scholar

Buchanan R.L., Damert W.G., Whiting R.C., Van Schothorst M.: An approach for using epidemiologic and microbial food survey data to develop a ‘purposefully conservative’ estimate of the dose-response relationship between Listeria monocytogeneslevels and the incidence of foodborne listeriosis. J. Food Prot. 60, 918–922 (1997)10.4315/0362-028X-60.8.91831207816Search in Google Scholar

Buchanan R.L., Havelaar A.H., Smith M.A., Whiting R.C., Julien E.: The key events dose-response framework: its potential for application to foodborne pathogenic microorganisms. Crit. Rev. Food Sci. Nutr. 49, 718–728 (2009)Search in Google Scholar

Buchanan R.L., Smith J.L., Long W.: Microbial risk assessment: Dose-response relations and risk characterization. Int. J. Food Microbiol. 58, 159–172 (2000)10.1016/S0168-1605(00)00270-1Search in Google Scholar

Butler F., Xu Y.: Prediction of Staphylococcus aureus growth in ham during chilling using Pathogen Modeling Program. Biosystem Engineering, 16, 20–23 (2011)Search in Google Scholar

Carrasco E., Perez-Rodriguez F., Valero A., Garcia-Gimeno R.M., Zurera G.: Risk assessment and management of Listeria monocytogenes in ready-to-eat lettuce salads. Compr. Rev. Food Sci. F. 9, 498–512 (2010)Search in Google Scholar

Codex Alimentarius Commission. Report of the Thirtieth Session of the Codex Committee on Food Hygiene (CCFH). Alinorm 99/13 Appendix IV: s. 58–64 1999. http://www.codexali-mentarius.net/download/report/112/Al99_13e.pdf (18.09.2017)Search in Google Scholar

Coleman M.E., Marks H.M.: Qualitative and quantitative risk assessment, Food Control,10, 289–297(1999).Search in Google Scholar

Couvert O., Augustin J-Ch. i wsp. Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods. Int. J. Food Microbiol. 144, 236–242 (2010)Search in Google Scholar

Daughtry B.J., Davey K.R., King K.D.: Temperature dependence of growth kinetics of food bacteria. Food Microbiol. 14,21–30 (1997)10.1006/fmic.1996.0064Search in Google Scholar

Davey K.R.: Applicability of the Davey (linear Arrhenius) predictive model to the lag phase of microbial growth. J. App. Bacteriol., 70,253–257 (1991)Search in Google Scholar

Davey K.R.: Linear-Arrhenius models for bacterial growth and depth and vitamin denaturations. J. In. Microbiol. 12, 172–179 (1993)Search in Google Scholar

Davey K.R.: Modeling the combined effect of temperature and pH on the rate coefficient for bacterial growth. Int. J. Food Microbiol. 23, 295–303 (1994)10.1016/0168-1605(94)90158-9Search in Google Scholar

De Keuckelaere A., Jacxsens L., Amoah P., Medema G., McClure P., Jaykus L.-A., Uyttendaele M.: Zero risk does not exist: lessons learned from microbial risk assessment related to use of water and safety of fresh produce. Compr. Rev. Food Sci. F. 14, 387–410 (2015)Search in Google Scholar

Dermesonluoglu E., Fileri K., Orfanoudaki A., Tsevdou M., Modeling the microbial spoilage and quality decay of pre-packed dandelion leaves as function of temperature. J. Food Eng. 184, 21–30 (2016)Search in Google Scholar

Devlieghere F., Francois K., Vermeulen A., Debevere J.: Predictive microbiology (w) Predictive modeling and risk assessment. Integrating safety and environmental knowledge into food studies towards European sustainable development, red. R. Costa, K. Kristbergsson, Springer, Boston, 2009, s. 29–5310.1007/978-0-387-68776-6_3Search in Google Scholar

Dominguez S., Schaffner D.W.: Microbiological quantitative risk assessment (w) Safety of meat and processed meat, red. F. Toldrá, Springer, New York, 2009, s. 591–61410.1007/978-0-387-89026-5_23Search in Google Scholar

FAO/WHO. Microbiological Risk Assessment Series 2: Risk Assessments of Salmonella in Eggs and Broiler Chickens. World Health Organization, Food and Agricultural Organization of the United Nations, Geneva, Rome, 2002, www.fao.org/3/a-y4392e. pdf (11.09.2017)Search in Google Scholar

FAO/WHO. Microbiological risk assessment series 3: Hazard characterization for pathogens in food and water. World Health Organization, Food and Agricultural Organization of the United Nations, Geneva, Roma, 2003, whqlibdoc.who.int/publications/2003/9241562374.pdf (11.09.2017)Search in Google Scholar

Fernández J.C., Hervás C., Martínez-Estudillo F.J., Gutiérrez P.A.: Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Appl. Soft Comput. 11, 534–550 (2011)10.1016/j.asoc.2009.12.013Search in Google Scholar

Fernández-Navarro F., Hervás-Martínez C., Cruz-Ramírez M., Gutiérrez P.A., Valero A.: Evolutionary q-Gaussian Radial Basis Function Neural Network to determine the microbial growth/ no growth interface of Staphylococcus aureus. Appl. Soft Comput. 11, 3012–3020 (2011)10.1016/j.asoc.2010.11.027Search in Google Scholar

Ferrari A., Lombardi S., Signoroni A.: Bacterial colony counting with convolutional Neural Networks in Digital Microbiology Imaging. Patter Recognit. 61, 629–640 (2017)10.1016/j.patcog.2016.07.016Search in Google Scholar

Giaccone V., Ferri, M..: Microbiological quantitative risk assessment and food safety: an update. Vet. Res. Commun. 29, 101– 106 (2005)10.1007/s11259-005-0020-616244933Search in Google Scholar

Guiller L.: Predictive microbiology models and operational readiness. Procedia Food Sci. 7, 133–136 (2016)10.1016/j.profoo.2016.05.003Search in Google Scholar

Gunvig A., Hansen F., Borggaard C.: A mathematical modeling for predicting growth/no-growth of psychrotrophic C. botulinum in meat products with five variables. Food Control, 29, 309–317 (2013)10.1016/j.foodcont.2012.06.046Search in Google Scholar

Hamilton A.J., Stagnitti F., Premier R., Boland A.M., Hale G.: Quantitative microbial risk assessment models for consumption of raw vegetables irrigated with reclaimed water. Appl Environ Mikrob. 72, 3284–3290 (2006)10.1128/AEM.72.5.3284-3290.2006Search in Google Scholar

Heinemann, M., Sauer U.: Systems biology of microbial metabolism. Curr. Opin. Microbiol. 13, 337–43 (2010)Search in Google Scholar

Huang L.: IPMP 2013 – A comprehensive data analysis tool for predictive microbiology. Int. J. Food Microbiol. 171, 100–107 (2014).10.1016/j.ijfoodmicro.2013.11.019Search in Google Scholar

Huang L., Juneja V. K., Yan X.: Thermal inactivation of food-borne pathogens in the USA pathogen modeling program. J. Therm. Anals. and Colorim. 106, 191–198 (2011)Search in Google Scholar

Ilnicka-Olejniczak O., Hornecka D.: Prognozowanie w mikro-biologii żywności (w) Food Product Development. Opracowanie nowych produktów żywnościowych. Wydawnictwo AR, Poznań, 1995, s. 149–168Search in Google Scholar

Julien E., Boobis A.R., Olin S.S.: The key events dose-response framework: a cross disciplinary mode-of-action based approach to examining dose-response and thresholds. Crit Rev. Food Sci. Nutr. 49, 682–689 (2009)10.1080/10408390903110692Search in Google Scholar

Keeratipibul S., Phewpan A., Lursinsap Ch.: Prediction of coli-forms and Escherichia coli on tomato fruits and lettuce leaves after sanitizing by using Artificial Neural Networks. LWT-Food Sci. Technol. 44,130–138 (2011)Search in Google Scholar

Kilcast D., Subramanian P.: The stability and shelf-life of food. Woodhead Publishing, Cambridge, 200010.1201/9781439822869Search in Google Scholar

Kim H.W., Lee K., Kim S. H., Rhee M.S.: Predictive modeling of bacterial growth in ready-to-use salted napa cabbage (Brassica pekinensis) at different storage temperatures. Food Microbiol. 70, 129–136 (2018)10.1016/j.fm.2017.09.017Search in Google Scholar

Klapwijk P.M., Jouve J.-L., Stringer M.F.: Microbiological risk assessment in Europe: the next decade. Int. J. Food Microbiol. 58, 223–230 (2000)10.1016/S0168-1605(00)00276-2Search in Google Scholar

Koseki S.: Microbial responses viewer (MRV): A new ComBase-derived database of microbial responses to food environments. Int. J. Food Microbiol. 134, 75–82 (2009)10.1016/j.ijfoodmicro.2008.12.01919181410Search in Google Scholar

Koseki S.: Risk assessment of microbial and chemical contamination in fresh produce (w) Global Safety of Fresh Produce red. J. Hoorfan, Woodhead Publishing Limited, Cambridge, 2014, s. 153–17110.1533/9781782420279.2.153Search in Google Scholar

Koutsoumanis, K.: A study on the variability in the growth limits of individual cells and its effect on the behavior of microbial populations. Int. J. Food Microbiol. 5th International Conference on Predictive Modelling in Foods, 128, 116–121 (2008)Search in Google Scholar

Koutsoumanis K. P., Lianou A.: Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells. Appl. Environ. Microbiol. 79, 2294–2301 (2013)Search in Google Scholar

Koutsoumanis K. P., Lianou A., Gougoul M.: Latest developments of foodborne pathogens modeling. Curr. Opin. Food Sci. 8, 89–98 (2016)Search in Google Scholar

Krishnan K. R., Babuskin S., Babu P. A. S., Sivarajan M., Sukumar M.: Evaluation and predictive modeling the effect of spice extracts an raw chicken meat stored at different temperatures. J. Food Eng. 166, 29–37 (2015)10.1016/j.jfoodeng.2015.05.021Search in Google Scholar

Lammerding, A.M., Fazil, A.: Hazard identification and exposure assessment for microbial food safety risk assessment. Int. J. Food Microbiol. 58, 147–157 (2000)10.1016/S0168-1605(00)00269-5Search in Google Scholar

Larsen P., Yuki H., Gilbert J.: Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction. J. Biotechnol. 160, 17–24 (2012)10.1016/j.jbiotec.2012.03.009Search in Google Scholar

Lim K.Y., Jiang S.C.: Reevaluation of health risk benchmark for sustainable water practice through risk analysis of rooftop-harvested rainwater. Water Res. 47, 7273–7286 (2013)10.1016/j.watres.2013.09.059Search in Google Scholar

Mafart P.: Food engineering and predictive microbiology: on the necessity to combine biological and physical kinetics. Int. J. Food Microbiol. 100, 239–251 (2005)10.1016/j.ijfoodmicro.2004.10.023Search in Google Scholar

Magnússon S.H., Verhagen H.: State of the art in benefit – risk analysis: Food microbiology. Food Chem. Toxicol. 50, 33–39 (2012)Search in Google Scholar

McKellar C., Xuewen L.: Modeling microbial responses in food. CRC Press, USA, 200410.1201/9780203503942Search in Google Scholar

McKellar, R. C., Knight K.: A combined discrete-continuous model describing the lag phase of Listeria monocytogenes. Int. J. Food Microbiol. 54 (3), 171–80 (2000)10.1016/S0168-1605(99)00204-4Search in Google Scholar

McMeekin T.A.: Predictive microbiology: quantitative science delivering quantifiable benefits to the meat industry and other food industries. Meat Science, 77, 17–27 (2007)10.1016/j.meatsci.2007.04.005Search in Google Scholar

McMeekin T. A., Bowman J., McQuestin O., Mellefont L., Ross T., Tamplin M.: The future of predictive microbiology: Strategic research, innovative applications and great expectations. Int. J. Food Microbiol. 128, 2–9 (2008)10.1016/j.ijfoodmicro.2008.06.026Search in Google Scholar

McMeekin T.A., Olley J., Ratkowsky D.A., Ross T.: Predictive microbiology: towards the interface and beyond. Int. J. Food Mocrobiol. 73, 395–407 (2002)10.1016/S0168-1605(01)00663-8Search in Google Scholar

McMeekin T.A., Olley J.N., Ross T., Ratkowsky D.A.: Predictive microbiology, theory and application, RST LTD, England, 1993Search in Google Scholar

McMeekin T. A., Ross T.: Predictive microbiology: providing a knowledge-based framework for change management. J. Food Microbiol. 1, 133–153 (2002)10.1016/S0168-1605(02)00231-3Search in Google Scholar

Moon H., Kim S., Chen J., George N., Kodell R..: Model uncertainty and model averaging in the estimation of infectious dose for microbial pathogens. Risk Anal. 33, 220–231 (2013).10.1111/j.1539-6924.2012.01853.xSearch in Google Scholar

Mota A., Mena K.D., Soto-Beltran M., Tarwater P.M., Chaidez C.: Risk assessment of Cryptosporidium and Giardia in water irrigating fresh produce in Mexico. J. Food Protect. 72, 2184– 2188 (2009)10.4315/0362-028X-72.10.2184Search in Google Scholar

Najjar Y.M., Basheer I.A., Hajmeer H.N.: Computional neural networks for predictive microbiology: I. Methodology. Int. J. Food Mocrobiol. 34, 27–49 (1997)10.1016/S0168-1605(96)01168-3Search in Google Scholar

Nauta M.J..: Modelling bacterial growth in quantitative microbiological risk assessment: is it possible? Int. J. Food Microbiol. 73, 297–304 (2002)10.1016/S0168-1605(01)00664-XSearch in Google Scholar

Nauta, M. van der Fels-Klerx I., Havelaar A.: A poultry-processing model for quantitative microbiological risk assessment. Risk Anal. 25, 85–98 (2005)Search in Google Scholar

O’Mahony C., Seman D. L.: Modeling the microbial shelf life of Foods and Beverages (w) The Stability and Shelf Life of Food, red. Persis Subramaniam, Woodhead Publishing Series in Food Science, Cambridge, 2016, s. 253–28910.1016/B978-0-08-100435-7.00009-5Search in Google Scholar

Pérez-Rodríguez F., Campos D., Ryser E.T., Buchholz A.L,. Posada-Izquierdo G.D., Marks B.P., Zurera G., Todd E.C.D.: A mathematical risk model for Escherichia coli O157:H7 cross contamination of lettuce during processing. Food Microbiol. 28, 694–701 (2011)Search in Google Scholar

Pérez-Rodríguez F., Valero A.: Application of Predictive Models in Quantitative Risk Assessment and Risk Management (w) Predictive Microbiology in Foods (red.) F. Pérez-Rodríguez, A. Valero, Springer, Berlin, 2013, s. 87–9710.1007/978-1-4614-5520-2_6Search in Google Scholar

Pérez-Rodríguez F., Valero A., Carrasco E., García R.M., Zurera G.: Understanding and modeling bacterial transfer to foods: a review. Trends Food Sci. Technol. 19, 131–44 (2008)Search in Google Scholar

Pérez-Rodríguez F., van Asselt E.D., Garcia-Gimeno R.M., Zurera G., Zwietering M.H.: Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats. J. Food Prot. 70, 1137–1152 (2007)10.4315/0362-028X-70.5.1137Search in Google Scholar

Petterson S.R, Ashbolt N.J., Sharma A.: Microbial risks from wastewater irrigation of salad crops: a screening-level risk assessment. Water Environ. Res. 73, 667–672 (2001)Search in Google Scholar

Plaza-Rodríguez C., Thoens C., Falenski A., Weiser A.A., Appel B., Kaesbohrer A., Filter M.: A strategy to establish Food safety Model Repositories. Int. J. Food Microbiol. 204, 81–90 (2015)10.1016/j.ijfoodmicro.2015.03.010Search in Google Scholar

Pruitt K.M., Kamau D.N.: Mathematical model of bacterial growth, inhibition and depth under combined stress conditions. J. Ind. Microbiol. 12, 221–231 (1993)Search in Google Scholar

Pujol L., Albert I., Magras C., Johnson N., B., Membre J.-M., Probabilistic exposure assessment model to estimate aseptic-UHT product failure rate. Int. J. Food Microbiol. 192, 124–141 (2015)10.1016/j.ijfoodmicro.2014.09.023Search in Google Scholar

Ratkowsky D.A.: Principles of nonlinear regression modeling, J. Ind. Microbiol., 12, 195–199 (1993)Search in Google Scholar

Roberts T. A.: Microbial Growth and Survival: Developments in Predictive Modelling. Int. Biodeterior. Biodegr. 36, 297–309 (1995)Search in Google Scholar

Ross T., McMeekin T.A.: Predictive microbiology. Int. J. Food Mocrobiol. 23, 241–264 (1994)10.1016/0168-1605(94)90155-4Search in Google Scholar

Rozporządzenie Komisji (WE) nr 2073/2005 z dnia 15 listopada 2005 r. w sprawie kryteriów mikrobiologicznych dotyczących środków spożywczych. Dz.U. L 338 z 22.12.2005Search in Google Scholar

Seliwiorstow T., Uyttendaele M., De Zutter L., Nauta M.: Application of TRiMiCri for the evaluation of risk based microbiological criteria for Campylobacter on broiler meat. Microb. Risk Anal. 2–3, 78–82 (2016)10.1016/j.mran.2016.05.001Search in Google Scholar

Sheen S., Hwang Ch.-A.: Mathematical modeling the cross-contamination of Escherichia coli O157:H7 on the surface of ready-to-eat meat product while slicing. Food Microbiol. 27, 37–43 (2010)10.1016/j.fm.2009.07.01619913690Search in Google Scholar

Smith M.A., Takeuchi K., Anderson G., Ware G.O., McClure H.M., Raybourne R.B., Mytle N., Doyle M. P.: Dose response for Listeria monocytogenes induced stillbirths in nonhuman primates. Infect. Immun. 76, 726–731 (2008)Search in Google Scholar

Strachan N.J.C., Doyle M.P., Kasuga F., Rotariu O., Ogden I.D.: Dose response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks. Int. J. Food Microbiol. 10, 35–47 (2005)10.1016/j.ijfoodmicro.2004.11.02316084264Search in Google Scholar

Tadeusiewicz R.: Elementarne wprowadzenie do techniki sieci neuronowych z przykładowymi programami, Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1998Search in Google Scholar

Tadeusiewicz R.: Sieci Neuronowe, Akademicka Oficyna Wydawnicza RM, Warszawa, 1993Search in Google Scholar

Tenenhaus-Aziza F., Ellouze M.: Software for predictive microbiology and risk assessment: A description and comparison of tools presented at the ICPMF8 Software Fair, Food Microbiol. 45, 290–299 (2015)Search in Google Scholar

Teunis, P.F.M., Van der Heijden, O.G., Van der Giessen, J.W.B., Havelaar, A.H.: The dose-response relation in human volunteers for gastro-intestinal pathogens, Report number 284550002. National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands 1996, http://www.rivm. nl/en/Documents_and_publications/Scientific/Reports/1996/ april/The_dose_response_relation_in_human_volunteers_for_ gastro_intestinal_pathogens (4.09.2017)Search in Google Scholar

van Gerwen S., Zwietering M.H.: Growth and inactivation models to be used in quantitative risk assessments. J. Food Prot. 61, 1541–1549 (1998)10.4315/0362-028X-61.11.15419829202Search in Google Scholar

van Ginneken M., Oron G.: Risk assessment of consuming agricultural products irrigated with reclaimed wastewater: an exposure model. Water Resour. Res. 36, 2691–2699 (2000)10.1029/2000WR900106Search in Google Scholar

Whiting R.C.: Microbial modeling in food. Critical Reviews in Food Scie. and Nutrit. 35, 467–494 (1995)Search in Google Scholar

Whiting, R. C., Buchanan R. L.: A classification of models for predictive microbiology. Food Microbiology,10, 175–177 (1993)10.1006/fmic.1993.1017Search in Google Scholar

Williams D., Irvin E.A., Chmielewski R.A., Frank J.F., Smith M.A.: Dose response of Listeria monocytogenes after oral exposure in pregnant guinea pigs. J. Food Prot. 70, 1122–1128 (2007)10.4315/0362-028X-70.5.1122Search in Google Scholar

Zwietring M.H., Jongenburger I., Rombouts F.M., Van’t Rient K.: Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56, 1875–1881 (1990)Search in Google Scholar

Articoli consigliati da Trend MD

Pianifica la tua conferenza remota con Sciendo