Predictive Model for Growth of Salmonella Typhimurium DT104 ...

Predictive Model for Growth of Salmonella Typhimurium DT104 ...

T4-04 Predictive Model for Growth of Salmonella Typhimurium DT104 on Ground Chicken Breast Meat Thomas P. Oscar, Ph.D. USDA-ARS, Microbial Food Safety Research Unit and USDA, Center of Excellence Program University of Maryland Eastern Shore Princess Anne, MD Ground Chicken Survey 1996 Natural Microflora 100% (25-g sample) 4.6 log CFU/g Salmonella 45% (25-g sample) 0.1 log MPN/g Hurdles for modeling Salmonella growth on chicken with a natural microflora Use of a low initial density Strain with a proper phenotype Salmonella Typhimurium DT104 Occurs in nature Low prevalence on chicken Resistant to multiple antibiotics Stable phenotype Growth similar to other strains

Growth of Salmonella Typhimurium DT104 (ATCC 700408) from High Initial Density (103.8 CFU/g) on Ground Chicken Breast Meat with a Natural Microflora 1.0 Replicates Mean (h-1) 0.8 0.6 0.4 0.2 0.0 10 15 20 25 30 Temperature ( C) 35 Oscar, T. P. 2006. (unpublished data) 40 Objective

To overcome the hurdles for developing and validating a predictive model for growth of Salmonella on ground chicken with a natural microflora. Challenge Study S. Typhimurium DT104 ATCC 700408 Stationary phase cells BHI broth at 30oC for 23 h Initial Density 0.6 log MPN or CFU/g Ground chicken breast meat 1 gram portions Jacquelyn B. Ludwig Experimental Design Model development 10, 12, 14, 22, 30, 40oC

Model evaluation 11, 18, 26, 34oC Replication 5 batches per temperature To assess variation of pathogen growth Pathogen Enumeration MPN (0 to 3.28 log MPN/g) 3 x 4 assay in BPW Spot (2 l) onto XLH-CATS CFU (> 3 log CFU/g) Direct plating on XLH-CATS Xylose-lysine agar base with 25 mM HEPES (buffering agent) plus 25 g/ml of the following antibiotics: chloramphenicol (C), ampicillin (A), tetracycline (T) and streptomycin (S). Primary Modeling 8 log MPN or CFU/g 7 95% PI 6 5 4 3

MPN & CFU 2 1 N(t) = [Nmax/(1 + ((Nmax/No) 1) * exp (- * t))] 0 0 10 20 30 40 Time (h) 50 60 70 Comparison of MPN and CFU Sample 1 2 3 4 5 6

7 8 o T ( C) 11 14 14 26 26 30 30 40 Time (h) 175.9 38.7 68.0 8.7 9.7 6.0 6.8 4.4 Mean log MPN/g 3.28 3.09 3.28 2.95 3.28 2.95 3.00 3.09

b 3.12 Means with different superscripts differ at P < 0.05 log CFU/g 3.08 3.30 3.38 3.65 3.66 3.00 3.51 3.56 a 3.39 12oC 2.70 14oC 4.98 22 C o 30oC 40 C o 6.43 8.49 9.36

log MPN or CFU/g 100 200 Time (h) 12 14 C 10 8 6 4 2 0 0 50 12 30 C 10 8 6 4 2 0 0 10 12 12 C 10 8 6 4 2 0 0 50

300 Dependent Data 100 150 Time (h) 200 12 22 C 10 8 6 4 2 0 0 10 20 30 40 50 60 70 200 Time (h) log MPN or CFU/g 1.63 12 10 C 10 8 6 4 2 0 0 100

150 Time (h) log MPN or CFU/g 10 C o log MPN or CFU/g Nmax (log/g) log MPN or CFU/g Temp. log MPN or CFU/g Primary Modeling 20 30 Time (h) 40 50 12 40 C 10 8 6 4

2 0 0 10 20 30 Time (h) 40 50 2.28 18oC 5.34 26oC 7.63 34oC 9.29 12 26 C 10 8 6 4 2

0 0 25 200 12 18 C 10 8 6 4 2 0 0 25 100 12 34 C 10 8 6 4 2 0 0 10 log MPN or CFU/g 11oC 12 11 C 10 8 6

4 2 0 0 50 100 150 Time (h) log MPN or CFU/g Nmax (log/g) log MPN or CFU/g Temp. log MPN or CFU/g Primary Modeling 50 Time (h) 75 Independent Data 50 75 100 125 150 Time (h) 20

Time (h) 30 40 Performance Evaluation Secondary Models Relative Error (RE) 1.6 and Nmax = (O P) / P = (P O) / P Acceptable Prediction Zone = -0.3 to 0.15 Nmax and PI = -0.8 to 0.40 % RE REIN / RETOTAL > 70% = acceptable Relative error 95% PI

1.2 "Overly Fail-dangerous" 0.8 0.4 -0.0 -0.4 -0.8 -1.2 "Acceptable" "Overly Fail-safe" 4 5 6 7 8 9 10 Predicted N(t) (log CFU/g) 11 1. Oscar, T. P. 2005. J. Food Sci. 70:M129-M137. 2. Oscar, T. P. 2005. J. Food Prot. 68:2606-2613. Secondary Model for %RE Dependent 83 Independent 100

0.5 (h-1) 0.4 0.3 i = 0.047 h-1 0.2 To = 15.6oC rate = 0.22 h-1/oC 0.1 0.0 opt = 0.41 h-1 5 10 15 20 25 30 35 Temperature ( C)

= i = opt/[1 + ((opt/i) - 1)* exp (-rate (T To)] 40 45 if T <= To if T > To Nmax (log MPN or CFU/g) Secondary Model for Nmax %RE Dependent 83 Independent 75 12 10 8 a = 2.47 6 Tmin = 9.11oC Tsubmin = 5.66oC 4 2 0 Nmax = exp[(a * [(T Tmin)/(T Tsubmin)])] 5

10 15 20 25 30 35 Temperature ( C) 40 45 Secondary Model for 95% Prediction Interval 3.0 PI (log/g) 2.5 2.0 PI1 = 1.33 log/g 1.5 PI2 = 2.58 log/g 1.0 0.5 0.0 PI3 = 1.94 log/g %RE Dependent 100 Independent 50 5

10 15 20 25 T1 = 10oC T2 = 14.8oC 30 Temperature ( C) 35 T3 = 26.9oC 40 45 Tertiary Modeling Observed Tertiary Model Predicted Model

Observed N(t) Observed PI Primary Model Observed PI Model Predicted PI Secondary Models Model Primary Model Predicted Predicted N(t) Predicted N(t) Observed Nmax Nmax Model

Predicted Nmax Performance Evaluation Tertiary Model 90% Concordance N(t)IN / N(t)TOTAL > 90% Dependent Data 93% (322/344) Independent Data 94% (223/236) Oscar, T. P. 2006. J. Food Prot. (in press) Summary MPN and CFU data can be used in tandem to model pathogen growth from a low initial density. 95% PI provides a simple stochastic method for modeling variation of pathogen growth among batches of food with natural microflora. 90% concordance is a simple method for validating stochastic models.

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