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DT104 (ATCC ) from High Initial Density ( CFU/g) on Ground Chicken Breast Meat with a Natural Microflora Oscar, T. P (unpublished data)

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 Stationary phase cells –BHI broth at 30 o C 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, 40 o C Model evaluation –11, 18, 26, 34 o C 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 95% PI MPN & CFU N(t) = [N max /(1 + ((N max /N o ) – 1) * exp (-  * t))]

Comparison of MPN and CFU ab Means with different superscripts differ at P < 0.05

Primary Modeling Dependent Data Temp.N max (log/g) 10 o C o C o C o C o C o C9.36

Primary Modeling Independent Data Temp.N max (log/g) 11 o C o C o C o C9.29

Performance Evaluation Secondary Models Relative Error (RE) –  and N max = (O – P) / P – 95% PI = (P – O) / P Acceptable Prediction Zone –  = -0.3 to 0.15 – N max and PI = -0.8 to 0.40 % RE –RE IN / RE TOTAL –> 70% = acceptable 1. Oscar, T. P J. Food Sci. 70:M129-M Oscar, T. P J. Food Prot. 68:

Secondary Model for  %RE  =  i if T <= T o  =  opt /[1 + ((  opt /  i ) - 1)* exp (-  rate (T – T o )] if T > T o  i = h -1 T o = 15.6 o C  rate = 0.22 h -1 / o C  opt = 0.41 h -1

Secondary Model for N max %RE N max = exp[(a * [(T – T min )/(T – T submin )])] a = 2.47 T min = 9.11 o C T submin = 5.66 o C

Secondary Model for 95% Prediction Interval %RE PI 1 = 1.33 log/g PI 2 = 2.58 log/g PI 3 = 1.94 log/g T 1 = 10 o C T 2 = 14.8 o C T 3 = 26.9 o C

Secondary Models Primary Model Primary Model N max Model  Model PI Model  Model Observed  Predicted  Observed PIPredicted PI Observed  Predicted  Observed N max Predicted N max Predicted N(t) Observed N(t) Tertiary Model Predicted N(t) Tertiary Modeling

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 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.