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Predictive Microbiology in Foods

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Predictive Microbiology in Foods

Part of the book series: SpringerBriefs in Food, Health, and Nutrition ((BRIEFSFOOD,volume 5))

Abstract

Predictive microbiology in foods is a research area within food microbiology intended to provide mathematical models to predict microbial behavior in food environments. Although the first predictive models were dated at the beginning of the 20th century, its great development has occurred in the past decades as a result of computer software advances. In addition to the exhaustive knowledge on food microbiology, the predictive microbiology field is based on important mathematical and modeling concepts that should be previously introduced for predictive microbiology beginners. The different typology of predictive models allows predicting growth, inactivation, and probability of growth of bacteria in foods under different environmental conditions and considering additional factors such as the physiological state of cells or interaction with other microorganisms. Nowadays, predictive models have become a necessary tool to support decisions concerning food safety and quality because models can provide rapid responses to specific questions. Furthermore, predictive models have been incorporated as helpful elements into the self-control systems such as Hazard Analysis for Critical Control Point (HACCP) programs and food safety risk-based metrics. National and international food safety policies are now based on the development of Quantitative Microbial Risk Assessment studies, which is greatly supported by the application of predictive models. Predictive microbiology is still growing but at the same time is turning into an important tool for improving food safety and quality.

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References

  • Baranyi J, Roberts TA (1994) A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 23:277–294. doi:10.1016/0168-1605(94)90157-0

    Article  CAS  Google Scholar 

  • Bigelow WD (1921) The logarithmic nature of thermal death time curves. J Infect Dis 27:528–536. doi:10.1093/infdis/29.5.528

    Article  Google Scholar 

  • Bigelow WD, Esty JR (1920) The thermal death point in relation to typical thermophylic organisms. J Infect Dis 27:602–617. doi:10.1093/infdis/27.6.602

    Article  Google Scholar 

  • Brul S, Mensonides FIC, Hellingwerf KJ, Teixeira de Mattos MJ (2008) Microbial systems biology: new frontiers open to predictive microbiology. Int J Food Microbiol 128:16–21. doi:10.1016/j.ijfoodmicro.2008.04.029

    Article  Google Scholar 

  • Castillejo-Rodríguez AM, Gimeno RMG, Cosano GZ, Alcalá EB, Pérez MR (2002) Assessment of mathematical models for predicting Staphylococcus aureus growth in cooked meat products. J Food Prot 65:659–665

    Google Scholar 

  • Codex Alimentarius Commission (1999) Principles and guidelines for the conduct of microbiological risk assessment. CAC/GL-30-1999. Secretariat of the Joint FAO/WHO Food Standards Programme. FAO, Rome

    Google Scholar 

  • Dalgaard P, Buch P, Silberg S (2002) Seafood Spoilage Predictor: development and distribution of a product specific application software. Int J Food Microbiol 73:343–349. doi:10.1016/S0168-1605(01)00670-5

    Article  Google Scholar 

  • Dupont C, Augustin JC (2009) Influence of stress on single-cell lag time and growth probability for Listeria monocytogenes in half Fraser broth. Appl Environ Microbiol 75:3069–3076. doi:10.1128/AEM.02864-08

    Article  CAS  Google Scholar 

  • Esty JR, Meyer KF (1922) The heat resistance of spores of B. botulinus and related anaerobes. J Infect Dis 31:650–663. doi:10.1093/infdis/31.6.650

    Article  Google Scholar 

  • Genigeorgis CA (1981) Factors affecting the probability of growth of pathogenic microorganisms in foods. J Am Vet Med Assoc 179:1410–1417

    CAS  Google Scholar 

  • Gibson AM, Bartchetll N, Roberts TA (1987) The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurised pork slurry. J Appl Bacteriol 62:479–490. doi:10.1111/j.1365-2672.1987.tb02680.x

    Article  CAS  Google Scholar 

  • Janevska DP, Gospavic R, Pacholewicz E, Popov V (2010) Application of a HACCP–QMRA approach for managing the impact of climate change on food quality and safety. Food Res Int 43:1915–1924. doi:10.1016/j.foodres.2010.01.025

    Article  Google Scholar 

  • Koutsoumanis K, Nychas GJE (2000) Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life prediction. Int J Food Microbiol 60:171–184. doi:10.1016/S0168-1605(00)00309-3

    Article  CAS  Google Scholar 

  • Lammerding AM, Fazil A (2000) Hazard identification and exposure assessment for microbial food safety risk assessment. Int J Food Microbiol 58:147–157. doi:10.1016/S0168-605(00)00269-5

    Article  CAS  Google Scholar 

  • Lammerding AM, Paoli GM (1997) Quantitative risk assessment: an emerging tool for emerging foodborne pathogens. Emerg Infect Dis 3:483–487. doi:10.3201/eid0304.970411

    Article  CAS  Google Scholar 

  • Larsen P, Hamada Y, Gilbert J (2012) Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction. J Biotechnol. doi:10.1016/j.jbiotec.2012.03.009

  • Mataragas M, Drosinos EH, Vaidanis A, Metaxopoulos I (2006) Development of a predictive model for spoilage of cooked cured meat products and its validation under constant and dynamic temperature storage conditions. J Food Sci 71:M157–M167. doi:10.1111/j.1750-3841.2006.00058.x

    Article  CAS  Google Scholar 

  • Mataragas M, Zwietering MH, Skandamis PN, Drosinos EH (2010) Quantitative microbiological risk assessment as a tool to obtain useful information for risk managers–specific application to Listeria monocytogenes and ready-to-eat meat products. Int J Food Microbiol 141((suppl)):S170–S179. doi:10.1016/j.ijfoodmicro.2010.01.005

    Article  Google Scholar 

  • McDonald K, Sun DW (1999) Predictive food microbiology for the meat industry: a review. Int J Food Microbiol 52:1–27. doi:10.1016/S0168-1605(99)00126-9

    Article  CAS  Google Scholar 

  • McMeekin TA, Ross T (2002) Predictive microbiology: providing a knowledge-based framework for change management. Int J Food Microbiol 78:133–153. doi:10.1016/S0168-1605(02)00231-3

    Article  CAS  Google Scholar 

  • McMeekin TA, Olley J, Ross T, Ratkowsky DA (1993a) Predictive microbiology: theory and application. Research Studies Press, Taunton

    Google Scholar 

  • McMeekin TA, Olley JN, Ross T, Ratkowsky DA (1993b) Predictive microbiology: theory and application. Trends Food Sci Technol 4:340. doi:10.1016/0924-2244(93)90049-G

    Google Scholar 

  • McMeekin TA, Olley J, Ratkowsky DA, Ross T (2002) Predictive microbiology: towards the interface and beyond. Int J Food Microbiol 73:395–407. doi:10.1016/S0168-1605(01)00663-8

    Article  CAS  Google Scholar 

  • Membré JM, Lambert R (2008) Application of predictive modelling techniques in industry: from food design up to risk assessment. Int J Food Microbiol 128:10–15. doi:10.1016/j.ijfoodmicro.2008.07.006

    Article  Google Scholar 

  • Métris A, George S, Baranyi J (2011) Modelling osmotic stress by flux balance analysis at the genomic scale. Int J Food Microbiol 152:123–128. doi:10.1016/j.ijfoodmicro.2011.06.016

    Article  Google Scholar 

  • Monod J (1949) The growth of bacterial cultures. Annu Rev Microbiol 3:371–394. doi:10.1146/annurev.mi.03.100149.002103

    Article  CAS  Google Scholar 

  • Nixon PA (1971) Temperature integration as a means of assessing storage conditions. Report on quality in fish products. Seminar No. 3. Fishing Industry Board, New Zealand, pp 33–44

    Google Scholar 

  • Pérez-Rodríguez F, Valero A, Carrasco E, García-Gimeno RM, Zurera G (2008) Understanding and modelling bacterial transfer to foods: a review. Trends Food Sci Technol 19:131–144. doi:10.1016/j.tifs.2007.08.003

    Article  Google Scholar 

  • Pin C, Avendaño-Pérez G, Cosciani E, Gómez N, Gounadakic A, Nychas G, Skandamis P, Barker G (2011) Modelling Salmonella concentration throughout the pork supply chain by considering growth and survival in fluctuating conditions of temperature, pH and aw. Int J Food Microbiol 145:S96–S102. doi:0.1016/j.ijfoodmicro.2010.09.025

    Article  Google Scholar 

  • Ratkowsky DA (2004) Model fitting and uncertainty. In: McKellar RC, Lu X (eds) Modelling microbial responses in foods. CRC Press, Boca Raton, pp 191–195

    Google Scholar 

  • Ratkowsky DA, Olley J, McMeekin TA, Ball A (1982) Relationship between temperature and growth rates of bacterial cultures. J Bacteriol 149:1–5

    CAS  Google Scholar 

  • Roberts TA, Jarvis B (1983) Predictive modelling of food safety with particular reference to Clostridium botulinum in model cured meat systems. In: Roberts TA, Skinner FA (eds) Food microbiology: advances and prospects. Academic Press, New York, pp 85–95

    Google Scholar 

  • Roberts TA, Gibson AM, Robinson A (1981) Prediction of toxin production by Clostridium botulinum in pasteurised pork slurry. J Food Technol 16:337–355

    Article  CAS  Google Scholar 

  • Ross T, Ratkowsky DA, Mellefont LA, McMeekin TA (2003) Modelling the effects of temperature, water activity, pH and lactic acid concentration on the growth rate of Escherichia coli. Int J Food Microbiol 82:33–43. doi:10.1016/S0168-1605(02)00252-0

    Article  CAS  Google Scholar 

  • Rosso L, Lobry JR, Bajard S, Flandrois JP (1995) Convenient model to describe the combined effects of temperature and pH on microbial growth. Appl Environ Microbiol 61:610–616

    CAS  Google Scholar 

  • Salter MA, Ross T, Ratkowsky DA, McMeekin TA (2000) Modelling the combined temperature and salt (NaCl) limits for growth of a pathogenic Escherichia coli strain using generalised non-linear regression. Int J Food Microbiol 61:159–167. doi:10.1016/S0168-1605(00)00352-4

    Article  CAS  Google Scholar 

  • Scott WJ (1937) The growth of microorganisms on ox muscle. I. The influence of temperature. J Counc Sci Ind Res Aust 10:338–350

    Google Scholar 

  • Shimoni E, Labuza PT (2000) Modelling pathogen growth in meat products: future challenges. Trends Food Sci Technol 11:394–402. doi:10.1016/S0924-2244(01)00023-1

    Article  CAS  Google Scholar 

  • Spencer R, Baines CR (1964) The effect of temperature on the spoilage of wet fish: I. Storage at constant temperature between -1°C and 25°C. Food Technol Champaign 18:769–772

    Google Scholar 

  • Stringer M (2005) Summary report. Food safety objectives—role in microbiological food safety management. Food Cont 16:775–794. doi:10.1016/j.foodcont.2004.10.018

    Article  Google Scholar 

  • Sumner J, Krist K (2002) The use of predictive microbiology by the Australian meat industry. Int J Food Microbiol 73:363–366. doi:10.1016/S0168-1605(01)00672-9

    Article  Google Scholar 

  • Vaikousi H, Biliaderis CG, Koutsoumanis K (2009) Applicability of a microbial time temperature indicator (TTI) for monitoring spoilage of modified atmosphere packed minced meat. Int J Food Microbiol 133:272–278. doi:10.1016/j.ijfoodmicro.2009.05.030

    Article  CAS  Google Scholar 

  • Valero A, Rodríguez M, Carrasco E, Pérez-Rodríguez F, García-Gimeno RM, Zurera G (2010) Studying the growth boundary and subsequent time to growth of pathogenic Escherichia coli serotypes by turbidity measurements. Food Microbiol 27:819–828. doi:10.1016/j.fm.2010.04.016

    Article  CAS  Google Scholar 

  • Van Boekel MAJS (2008) Kinetic modelling of food quality: a critical review. Compr Rev Food Sci Food Saf 7:144–158. doi:10.1111/j.1541-4337.2007.00036.x

    Article  Google Scholar 

  • Van Schothorst MV (2004) A proposed framework for the use of FSOs. Food Cont. doi:10.1016/j.foodcont.2004.10.021

  • Vermeiren L, Devlieghere F, Vandekinderen I, Debevere J (2006) The interaction of the non-bacteriocinogenic Lactobacillus sakei 10A and lactocin S producing Lactobacillus sakei 148 towards Listeria monocytogenes on a model cooked ham. Food Microbiol 23:511–518. doi:10.1016/j.fm.2005.10.005

    Article  CAS  Google Scholar 

  • Zwietering MH, Jongenburger I, Rombouts FM, Van’t Riet D (1990) Modelling of the bacterial growth curve. App Environ Microbiol 56:1876–1881

    Google Scholar 

  • Zwietering MH, Witjzes T, de Wit JC, Van’t Riet K (1992) A decision support system for prediction of the microbial spoilage in foods. J Ind Microbiol 12:324–329. doi:10.1007/BF01584209

    Article  Google Scholar 

  • Zwietering MH, de Wit JC, Notermans S (1996) Application of predictive microbiology to estimate the number of Bacillus cereus in pasteurised milk at the point of consumption. Int J Food Microbiol 30:55–70. doi:10.1016/0168-1605(96)00991-9

    Article  CAS  Google Scholar 

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  1. Dpt Bromatologia y Tec. de los Alimentos, University of Córdoba, Córdoba, Spain

    Fernando Pérez-Rodríguez & Antonio Valero

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  1. Fernando Pérez-Rodríguez
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  2. Antonio Valero
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© 2013 Fernando Pérez-Rodríguez and Antonio Valero

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Pérez-Rodríguez, F., Valero, A. (2013). Predictive Microbiology in Foods. In: Predictive Microbiology in Foods. SpringerBriefs in Food, Health, and Nutrition, vol 5. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5520-2_1

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