doi:10.1016/j.fm.2006.11.003 
Published by Elsevier Ltd.
Predictive models for growth of Salmonella typhimurium DT104 from low and high initial density on ground chicken with a natural microflora
T.P. Oscar
, a, 
aMicrobial Food Safety Research Unit, Agricultural Research Service, USDA, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
Received 24 August 2006;
revised 7 November 2006;
accepted 12 November 2006.
Available online 26 December 2006.
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Abstract
A single strain (ATCC 700408) of Salmonella typhimurium DT104 was used to investigate and model growth from a low (1.12 log10 mpn g−1) and high (3.7 log10 cfu g−1) initial density on ground chicken with a natural microflora. Kinetic data for growth of the pathogen on ground chicken were fit to a primary model to determine lag time (λ), maximum specific growth rate (μ) and maximum population density (Nmax). Secondary models for λ, μ and Nmax, as a function of temperature (10–40 °C), were developed and compared among initial densities. Variation of pathogen growth among replicates (n=4 or 5) was higher at 10–18 °C than at 22–40 °C and was higher for Nmax than λ and μ. Prediction problems were observed when secondary models developed with one initial density were used to predict λ, μ and Nmax from the other initial density, especially at 10–18 °C and for Nmax. These results indicated that variation of growth among replicate challenge studies and initial density are important factors to consider when developing predictive models for growth of S. typhimurium DT104 on ground chicken with a natural microflora.
Keywords: Salmonella typhimurium DT104; Predictive models; Ground chicken; Growth
Fig. 1. Representative primary model fits for growth of Salmonella typhimurium DT104 from low (Δ) or high (□) initial density on ground chicken incubated at (A) 10 °C, (B) 14 °C, (C) 18 °C, (D) 22 °C, (E) 30 °C or (F) 40 °C.
Fig. 2. Secondary model fits to natural logarithm transformations (ln) of (A) lag time (λ), (B) maximum specific growth rate (μ), and (C) maximum population density (Nmax) of Salmonella typhimurium DT104 from low (Δ) or high (□) initial density on ground chicken. Symbols are means±standard errors of the mean.
Fig. 3. Acceptable prediction zone analysis of prediction errors (PE) for dependent (○) and independent (●) data (replicate) for (A) the low initial density model for lag time (λ), (B) the high initial density model for λ, (C) the low initial density model for maximum specific growth rate (μ), (D) the high initial density model for μ, (E) the low initial density model for maximum population density (Nmax) and (F) the high initial density model for Nmax.
Fig. 4. Acceptable prediction zone analysis of prediction errors (PE) for dependent (○) and independent (●) data (mean) for (A) the low initial density model for lag time (λ), (B) the high initial density model for λ, (C) the low initial density model for maximum specific growth rate (μ), (D) the high initial density model for μ, (E) the low initial density model for maximum population density (Nmax) and (F) the high initial density model for Nmax.
Table 1.
Statistical summary of primary modeling for growth of Salmonella typhimurium DT104 on ground chicken as a function of initial density and temperature: coefficient of determination (R2)
Table 2.
Statistical summary of primary modeling for growth of Salmonella typhimurium DT104 on ground chicken as a function of initial density and temperature: lag time (h)
Table 3.
Statistical summary of primary modeling for growth of Salmonella typhimurium DT104 on ground chicken as a function of initial density and temperature: maximum specific growth rate (h−1)
Table 4.
Statistical summary of primary modeling for growth of Salmonella typhimurium DT104 on ground chicken as a function of initial density and temperature: maximum population density (log10 mpn or cfu g−1)
a The coefficient of variation was calculated using
Nmax values expressed as mpn or cfu g
−1 and not as log
10 mpn or cfu g
−1 so as to provide a better assessment of the variation of
Nmax among replicate challenge studies.
Table 5.
Statistical summary of secondary modeling for lag time (λ), maximum specific growth rate (μ) and maximum population density (Nmax) of Salmonella typhimurium DT104 on ground chicken: comparison of fits to replicate and mean values
Table 6.
Evaluation of performance of secondary models for predicting lag time (λ), maximum specific growth rate (μ) and maximum population density (Nmax) of Salmonella typhimurium DT104 on ground chicken as a function of initial density and temperature: percentage of prediction errors (%PE) in the acceptable prediction zone for replicate and mean values of the growth parameters
a Dependent data were growth parameter data used in model development, whereas independent data were data for growth parameters from the other initial density. Thus, %PE values for dependent data evaluate goodness-of-fit of the model, whereas %PE values for independent data evaluate how well the model predicts growth from the other initial density.
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