Table 1.
Xanthomonas arboricola pv. pruni strains used in the study.
Table 2.
Equations of models used in the study.
Fig 1.
Relationship between population density and optical density at 600 nm for X. arboricola pv. pruni.
Data from suspensions of seven strains incubated at 25°C were used. The curve generated by the logarithmic model is shown.
Table 3.
Regression analysis between optical density at 600 nm and viable count for X. arboricola pv. pruni with different models.
Fig 2.
Primary model fitting to one of the six experimental growth curves for X. arboricola pv. pruni strain CFBP 5530 at 25°C.
(A) Baranyi, (B) Gompertz modified, and (C) Buchanan models were fitted to experimental data. The residual sum of squares (RSS) for each model is reported.
Fig 3.
Arrhenius plot of the maximum specific growth rates for X. arboricola pv. pruni.
Lines show three linear regions: (a) ln (μmax) = 0.92T – 31.81 (R2 = 0.66); (b) ln (μmax) = -0.41T + 12.35 (R2 = 0.97); and (c) ln (μmax) = -1.08T + 35.18 (R2 = 0.99). Where T is 1/°K x 104.
Table 4.
Growth parameters and corresponding standard error for X. arboricola pv. pruni at different temperatures (T) estimated by the modified Gompertz model.
Fig 4.
Model fitting to the maximum specific growth rate for X. arboricola pv. pruni as a function of temperature.
Values of the maximum specific growth rate (black symbols) are the mean of two experiments, seven strains and three replicates per strain. Error bars are the standard errors. Modified Ratkowsky models are coincident and represented with continuous line; dashed line represents the Ratkowsky model. The modified Ratkowsky model (equation 10) fitting to the growth rate data from the literature (white symbols) [25] is shown in dotted line.
Table 5.
Parameter estimation and statistical evaluation for the secondary models describing the maximum specific growth rate for X. arboricola pv. pruni as a function of temperature.
Table 6.
Observed and predicted maximum specific growth rate for X. arboricola pv. pruni at temperatures tested in model validation.