RESUMEN
AIMS: To explore the predictions of a novel rearrangement of the Baranyi-Roberts model (BRM) with time to detection data obtained from optical density data of microbial growth. METHODS AND RESULTS: Growth of Escherichia coli and Salmonella Typhimurium under mild conditions of temperature (25-37°C), salt (0·086, 0·51 and 1·03 mol l(-1)) and pH (6·85-4·5) was examined using optical density. Time to detection (TTD) data were fitted to a model based on a rearrangement of the BRM. Observations showed compatibility with standard viable count studies and produced highly accurate specific growth rates and lag phase durations. At high salt and low pH, however, there was a substantial dependency on the initial inoculum for the observation of visible growth. At 30 and 37°C, with 1·03 mol l(-1) salt, and at pH <5·75, no visible growth was recorded for E. coli at initial inoculum levels below 10(7) CFU ml(-1). CONCLUSIONS: The rearranged BRM can be used directly with TTD data obtained from optical density measurements. SIGNIFICANCE AND IMPACT OF THE STUDY: A distinct advantage of the rearranged model is that it allows for a very simple interpretation of easily obtainable data using standard nonlinear regression. The rearranged model gives to TTD data the same modelling capability that the BRM gives to plate count data.
Asunto(s)
Escherichia coli/crecimiento & desarrollo , Modelos Biológicos , Salmonella typhimurium/crecimiento & desarrollo , Escherichia coli/efectos de los fármacos , Concentración de Iones de Hidrógeno , Salmonella typhimurium/efectos de los fármacos , Cloruro de Sodio/farmacología , TemperaturaRESUMEN
Time to detection (TTD) measurements using turbidometry allow a straightforward method for the measurement of bacterial growth rates under isothermal conditions. Growth rate measurements were carried out for Listeria monocytogenes at 25, 30 and 37°C and for Pseudomonas aeruginosa over the temperature range 25 to 45°C. The classical three-parameter logistic model was rearranged to provide the theoretical foundation for the observed TTD. A model was subsequently developed for the analysis of TTD data from non-isothermal studies based on the Malthusian approximation of the logistic model. The model was able to predict the TTD for cultures of L. monocytogenes or P. aeruginosa undergoing simple temperature shunts (e.g. 25 to 37°C and vice versa), and for a multiple temperature shunt for L. monocytogenes (25-37-25-37°C and 37-25-37-25°C) over a period of 24h. In no case did a temperature shunt induce a lag.
Asunto(s)
Listeria monocytogenes/crecimiento & desarrollo , Modelos Biológicos , Pseudomonas aeruginosa/crecimiento & desarrollo , Temperatura , Recuento de Colonia Microbiana , Modelos LogísticosRESUMEN
A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many models are used to fit microbial count data, the modified Gompertz and Baranyi equation being two of the most widely used. Rapid, automated methods such as turbidimetry have been widely used to obtain growth parameters, but do not directly give the microbial growth curve. Optical density (OD) data can be used to obtain the specific growth rate and if used in conjunction with the known initial inocula, the maximum population data and knowledge of the microbial number at a predefined OD at a known time then all the information required for the reconstruction of a standard growth curve can be obtained. Using multiple initial inocula the times to detection (TTD) at a given standard OD were obtained from which the specific growth rate was calculated. The modified logistic, modified Gompertz, 3-phase linear, Baranyi and the classical logistic model (with or without lag) were fitted to the TTD data. In all cases the modified logistic and modified Gompertz failed to reproduce the observed linear plots of the log initial inocula against TTD using the known parameters (initial inoculum, MPD and growth rate). The 3 phase linear model (3PLM), Baranyi and classical logistic models fitted the observed data and were able to reproduce elements of the OD incubation-time curves. Using a calibration curve relating OD and microbial numbers, the Baranyi equation was able to reproduce OD data obtained for Listeria monocytogenes at 37 and 30°C as well as data on the effect of pH (range 7.05 to 3.46) at 30°C. The Baranyi model was found to be the most capable primary model of those examined (in the absence of lag it defaults to the classic logistic model). The results suggested that the modified logistic and the modified Gompertz models should not be used as Primary models for TTD data as they cannot reproduce the observed data.