Estimation of Staphylococcus aureus growth parameters from turbidity data: characterization of strain variation and comparison of methods

Abstract

Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17 degrees C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.

FIG. 1.

Distributions of estimated growth parameters among 34 S. aureus strains grown in chicken broth at 17°C. The circles represent observed data, and the lines show the best-fitting distribution functions as ranked by the Anderson-Darling test. a) Maximum specific growth rate, μ_max, estimated by the BP_dec method, logistic distribution (0.125088; 0.019735) (the two values separated by semicolons are the parameter values for the distribution [α and β, respectively, for the logistic distribution and μ and σ, respectively, for the lognormal distribution]); b) lag time, λ, estimated by the BP_dec method, lognormal distribution (17.318; 4.9808) −6.9082 (the value outside the parentheses is the shift factor supplied by the BestFit software program if the input data exceed the domain range of the fitted distribution; the negative sign of the shift value indicates that this amount should be subtracted from the value drawn from the distribution); c) the physiological state variable, α (BP_dec), lognormal distribution (0.66041; 0.19039) −0.33366; d) μ_max estimated by the DD method, lognormal distribution (0.12855; 0.046388) −0.020530. The distributions and their parameters have been described by Vose (32).