Microbiología predictiva: una ciencia en auge
Palabras clave:
Microbiología de alimentos, modelos predictivos, factores de crecimiento, algoritmos matemáticos, superficies de respuesta, APPCC, seguridad alimentaria, análisis de riesgos, PCCResumen
En las últimas dos décadas, para el estudio de la microbiología de alimentos, se han incluido como herramientas de análisis, el uso de la matemática y la estadística; tales conocimientos se combinan para desarrollar modelos matemáticos que describan la evolución de los microorganismos en los alimentos [1]. Para los modelos predictivos hay una gran variedad de estudios aplicados en diferentes matrices e industrias alimenticias [2-4]; estos buscan determinar a priori las condiciones de proceso (pH, la temperatura, la actividad de agua, el tiempo de agitación, entre otros), en las cuales hay activación, desactivación, crecimiento o muerte de los microorganismos que pueden ser perjudiciales tanto para el ser humano como para las propiedades organolépticas y nutricionales de un alimento [5, 6], de esta manera establecer puntos de control que eviten tales resultados [7, 8]. Los modelos matemáticos incluyen ecuaciones de diversos tipos como las polinómicas, logarítmicas, exponenciales, diferenciales, hasta llegar a modelos que incluyan ecuaciones de redes neuronales artificiales; también se clasifican en modelos primarios, secundarios o terciarios; que después de ser consolidados y aplicados logran unas predicciones robustas y seguras; sobre el comportamiento de los microorganismos en alimentos [9].
Descargas
Citas
Dantigny, P., A. Guilmart, and M. Bensoussan, Basis of predictive mycology. International Journal of Food Microbiology, 2005. 100(1–3): p. 187-196.
Chemaly, R.F., et al., Microbiology of liver abscesses and the predictive value of abscess gram stain and associated blood cultures. Diagnostic Microbiology and Infectious Disease, 2003. 46(4): p. 245-248.
Cornu, M., et al., Effect of temperature, waterphase salt and phenolic contents on Listeria monocytogenes growth rates on cold-smoked salmon and evaluation of secondary models. International Journal of Food Microbiology, 2006. 106(2): p. 159-168.
Mafart, P., Food engineering and predictive microbiology: on the necessity to combine biological and physical kinetics. International Journal of Food Microbiology, 2005. 100(1–3): p. 239-251.
Dens, E.J. and J.F. Van Impe, On the need for another type of predictive model in structured foods. International Journal of Food Microbiology, 2001. 64(3): p. 247-260.
McClure, P.J., et al., Predictive modelling of growth of Listeria monocytogenes The effects on growth of NaCl, pH, storage temperature and NaNO2. International Journal of Food Microbiology, 1997. 34(3): p. 221-232.
Armitage, N.H., Use of predictive microbiology in meat hygiene regulatory activity. International Journal of Food Microbiology, 1997. 36(2–3): p. 103-109.
McMeekin, T.A. and T. Ross, Predictive microbiology: providing a knowledge-based framework for change management. International Journal of Food Microbiology, 2002. 78(1–2): p. 133-153.
Jeyamkondan, S., D.S. Jayas, and R.A. Holley, Microbial growth modelling with artificial neural networks. International Journal of Food Microbiology, 2001. 64(3): p. 343-354.
Van Impe, J.F., et al., Predictive microbiology in a dynamic environment: a system theory approach. International Journal of Food Microbiology, 1995. 25(3): p. 227-249.
Fernández, P.S., et al., Predictive model of the effect of CO2, pH, temperature and NaCl on the growth of Listeria monocytogenes. International Journal of Food Microbiology, 1997. 37(1): p. 37-45.
Baranyi, J. and T.A. Roberts, Mathematics of predictive food microbiology. International Journal of Food Microbiology, 1995. 26(2): p. 199-218.
Tejedor, W., Ruiz, P., Rodrigo M. y Martínez, A., Microbiología Predictiva.Instituto de Agroquímica y Tecnología de Alimentos (CSIC), 2000. Apartado de Correos 73, 46100 Burjassot, Valencia, España: p. 31.
Vadasz, P. and A.S. Vadasz, Predictive modeling of microorganisms: LAG and LIP in monotonic growth. International Journal of Food Microbiology, 2005. 102(3): p. 257-275.
Zwietering, M.H., J.C. de Wit, and S. Notermans, Application of predictive microbiology to estimate the number of Bacillus cereus in pasteurised milk at the point of consumption. International Journal of Food Microbiology, 1996. 30(1–2): p. 55-70.
Graham, A.F., D.R. Mason, and M.W. Peck, Predictive model of the effect of temperature, pH and sodium chloride on growth from spores of non-proteolytic Clostridium botulinum. International Journal of Food Microbiology, 1996. 31(1–3): p. 69-85.
McMeekin, T.A., T. Ross, and J. Olley, Application of predictive microbiology to assure the quality and safety of fish and fish products. International Journal of Food Microbiology, 1992. 15(1–2): p. 13-32.
Baranyi, J.R., T.A.,McClure, P.J. , A nonautonomous differential equation to model bacterial growth. Food Microbiology, (1993). 10: p. 43-59.
McMeekin, T.A., et al., Predictive microbiology: towards the interface and beyond. International Journal of Food Microbiology, 2002. 73(2–3): p. 395-407.
Ross, T., P. Dalgaard, and S. Tienungoon, Predictive modelling of the growth and survival of Listeria in fishery products. International Journal of Food Microbiology, 2000. 62(3): p. 231-245.
Augustin, J.-C., Challenges in risk assessment and predictive microbiology of foodborne spore-forming bacteria. Food Microbiology, 2011. 28(2): p. 209-213.
Swinnen, I.A.M., et al., Predictive modelling of the microbial lag phase: a review. International Journal of Food Microbiology, 2004. 94(2): p. 137-159.
Genigeorgis, C., Avances en microbiología de los alimentos: significado para los problemas de salud alimentaria de la microbiología predictiva. Simposium commemorativo del Bicentenario de la Facultad de Veterinaria, 1993. Universidad Complutense, Madrid.: p. 34.
Buchanan, R.L., Predictive food microbiology. Trends in Food Science and Technology, 1993. 4p. 6-11.
Baranyi, J.R., T.A.,, Mathematics of predictive food microbiology. International Journal of Food Microbiology, 1995. 26: p. 199-218.
Buchanan, R.L., Whitting, R.C, Risk assesment and predictive microbiology. Journal of food Protection, 1996. 1996 supplement: p. 31-36.
Zweitering, M.H., Jongenburger, I., Rombouts, F.M., van’t Riet, K, Modelling of the bacterial growth curve. Applied and Environmental Microbiology, 1990. 56: p. 1875-1881.
al, M.C.M.D.e., Shelf life valuation of foods. 1994. 4: p. 21-30.
Reichart, O., Modelling the destruction of Escherichia coli on the base of reactions kinetics. International Journal of Food Microbiology, 1994. 23: p. 449-465.
Reichart, O., Mohácsi-Farkas, C, Mathematical modelling of the combined effect of water activity, pH and redox potential on the heat destruction. International Journal of Food Microbiology, 1994. 24: p. 103-112.
Periago, P.M., Fernández, P.S., Salmerón, M.C., Martínez, A, Predictive model to describe the combined effect of pH and NaCl on apparent heat resistance of Bacillus stearothermophilus. International Journal of Food Microbiology, 1998. 44: p. 21-30.
Davey, K.R., Lin, S.H., Wood, D.G., The effect of pH on continuous high temperature/short time sterilisation of liquids. Journal of American Institute of Chemical Engineering, 1978. 24: p. 537-540.
Mafart, P.a.L., I, Modelling combined effects of temperature and pH on heat resistance of spores by linear-Bigelow equation. Journal of Food Science, 1998. 63: p. 6-8.
Fernández, P.S., Ocio, M.J., Rodrigo, F., Rodrigo, M., Martínez, A, Mathematical model for the combined effect of temperature and pH on the thermal resistance of Bacillus stearothermophilus and Clostridium sporogenes spores. International Journal of Food Microbiology, 1996. 32: p. 225-233.
Muñoz-Cuevas, M., A. Metris, and J. Baranyi, Predictive modelling of Salmonella: From cell cycle measurements to e-models. Food Research International, 2012. 45(2): p. 852-862.
Davies, S.C.y.B., J.G., Predictive microbiology applications to chilled food microbiology. Microbiologie prédictive et HACCP, 1992. Amgar, A. ASEPT, Laval. Francia.
Whiting, R.C.B., R.L., Use of predictive microbial modeling in a HACCP program. Microbiologie prédictive et HACCP, 1992. Amgar, A. ASEPT, Laval. Francia.
Universidad autonoma de Barcelona. Metodos Rapidos y Automatizacion en microbiologia alimentaria VII Workshop. Alimentaria Congresos. Publicación trimestral, 2009. 3: p.18-19.
Baranyi, J. ,Pin, C. Primer curso teóricopráctico en Microbiología Predictiva de Alimentos. Facultad de Veterinaria,Universidad Complutense de Madrid. 2001
Ratkowsky, D.A., Olley, J., McMeekin, T.A.,and Ball, A. Relationship between temperatureand growth rate of bacterial cultures. Journal of Bacteriology, 1982. 142: p. 1-5.
Elliot, P. Predictive microbiology and HACCP. Journal of food Protection, 1996, suplement. p. 48-53.
Davey, K.R. Extension of the generalized sterilization chart for combined temperature and pH. Lebensmittel-Wissenschaft und – Technologie, 1993. 26: p. 476-479.
J.C. Fernández, C. Hervás, F.J. MartínezEstudillo, P.A. Gutiérrez. Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Applied Soft Computing, 2011: 11. p.534-550.
Thomas A. McMeekin; Thomas, Ross. Shelf life prediction: status and future possibilities. International Journal of Food Microbiology, 1996. 33: p. 65-83
Xezones, H. and Hutchings, I.S. Therma resistance of Clostridium botulinum (62A) spores as affected by fundamental food constituents. I. Effect of pH. Food Technology, 1965. 30: p. 1003-1005.
Cameron, M.S., Leonard, S.J. and Barret, E.L. Effect of moderately acidic pH on heat resistance of Clostridium sporogenes spores in phosphate buffer and in buffered pea puree. Applied and Environmental Microbiology, 1980. 5: p. 943-949.
Fernández, P.S., Ocio, M.J., Sánchez, T. and Martínez A. Thermal resistance parameters of Bacillus stearothermophilus spores heated in acidified mushroom extract. Journal of Food Protection, 1994. 57: p. 37-41.
Peña, D. Estadística. Modelos y Métodos. 2. Modelos lineales y series temporales. Segunda edición. Alianza Editorial.
Ronald J.W. Lambert; Eva, Bidlas. An investigation of the Gamma hypothesis: A predictive modelling studyof the effect of combined inhibitors (salt, pH and weak acids)on the growth of Aeromonas hydrophila. International Journal of Food Microbiology, 2007. 115: p. 12–28.
Amit Pal, Theodore, P. Labuza, Francisco Diez-Gonzalez. Comparison of primary predictive models to study the growth of Listeria monocytogenes at low temperatures in liquid culturesand selection of fastest growing ribotypes in meat and turkey product slurries. Food Microbiology, 2008. 25: p. 460-470.
Ross, T. Indices for Performance Evaluation of Predictive Models in Food Microbiology. Journal of Applied Bacteriology, 1996. 81: p. 501-508.
Mª del Rocio Rodriguez Perez. Desarrollo y Validación de modelos matemáticos para la predicción de vida comercial de productos cárnicos. Facultad de veterinaria departamento de bromatologia y tecnologia de los alimentos. Tesis doctoral. Universidad de Cordoba, Cordoba 2003. p. 140-157
Jo´zsef, Baranyi. Carmen, Pin. Thomas, Ross. Validating and comparing predictive models. International Journal of Food Microbiology, 1999. 48: p.159–166.
K. Bernaerts, K.P.M. Gysemans, T. Nhan Minh, J.F. Van Impe. Optimal experiment design for cardinal values estimation:guidelines for data collection. International Journal of Food Microbiology, 2005. 100: p.153– 165.
Ross, T., McMeekin, T.A.,. Predictive microbiology – a review. Int. J. Food Microbiol, 1994. 23: p. 241–264
Stanley Brul , Femke I.C. Mensonides a, Klaas J. Hellingwerf M. Joost Teixeira de Mattos. Microbial systems biology: New frontiers open to predictive microbiology. International Journal of Food Microbiology, 2008. 128: p. 16–21.
Patrice, Buche. Juliette, Dibie-Barthélemy. Ollivier, Haemmerlé. Rallou, Thomopoulos. Fuzzy concepts applied to the design of a database in predictive microbiology. Fuzzy Sets and Systems, 2006. 157: p. 1188 – 1200.
Codex Alimentarius Commission 1998 Draft Principles and Guidelines for the Conduct of Microbiological Risk Assessment. ALINOR99/13ª.
Lund, B. M. y S. H. W. Notermans. Potential hazards associated with REPFEDs, In A.H.W. Hauschild and K.L. Dodds (ed.), Clostridium botulinum: ecology and control in foods1992. p. 279-301.. Marcel Dekker, Inc., New York.
Shapton D.A. and Shapton N.F. 1991. Principles and practices for the safe processing of foods. Ed. Shapton D.A. and Shapton N.F.. Butterworth Heinemann, Oxford, UK.
Jordi Ferrer, Clara Prats , Daniel López , Josep Vives-Rego. Mathematical modelling methodologies in predictive food microbiology: A SWOT analysis. International Journal of Food Microbiology, 2009. 134: p. 2–8.
B. Leporq, J.-M. Membre´, C. Dervinb, P. Bucheb, J.P. Guyonnet. The Sym PreviusQ software, a tool to support decisions to thefoodstuff safety. International Journal of Food Microbiology, 2005. 100: p. 231– 237.
Karl McDonald, Da-Wen Sun. Predictive food microbiology for the meat industry: a review. International Journal of Food Microbiology, 1999. 52: p.1–27.
Jeanne-Marie Membré a, Ronald J.W. Lambert. Application of predictive modelling techniques in industry: From food design up torisk assessment. International Journal of Food Microbiology, 2008. 128: p.10–15.
Régis Pouillot , Meryl B. Lubran. Predictive microbiology models vs. modeling microbial growth within Listeriamonocytogenes risk assessment: What parameters matter and why. Food Microbiology, 2011. 28: p. 720 -726.
Karina J. Versyck, Kristel Bernaerts, Annemie H. Geeraerd, Jan F. Van Impe. Introducing optimal experimental design in predictive modeling: A motivating example. International Journal of Food Microbiology, 1999. 51: p. 39–51.
Isabel Walls, Virginia N. Scott. Use of predictive microbiology in microbial food safety risk assessment. International Journal of Food Microbiology, 1997. 36: p. 97-102.
Descargas
Publicado
-
Resumen917
-
PDF1678
Cómo citar
Número
Sección
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Portal de Ciencia Abierta