T4-04 Predictive Model for Growth of Salmonella Typhimurium DT104 on Ground Chicken Breast Meat

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T4-04Predictive 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

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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

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Hurdles for modeling Salmonella growth on chicken
with a natural microflora

• Use of a low initial density
• Strain with a proper phenotype

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Salmonella Typhimurium DT104

• Occurs in nature
• Low prevalence on chicken
• Resistant to multiple antibiotics
• Stable phenotype
• Growth similar to other strains

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Growth of Salmonella Typhimurium DT104 (ATCC
700408) from High Initial Density (103.8 CFU/g)
on Ground Chicken Breast Meat with a Natural
Microflora
Oscar, T. P. 2006. (unpublished data)
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Objective

• To overcome the hurdles for developing and
  validating a predictive model for growth of
  Salmonella on ground chicken with a natural
  microflora.

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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
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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
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Pathogen Enumeration

• MPN (0 to 3.28 log MPN/g)
• 3 x 4 assay in BPW
• Spot (2 ml) onto XLH-CATS
• CFU (gt 3 log CFU/g)
• Direct plating on XLH-CATS

Xylose-lysine agar base with 25 mM HEPES
(buffering agent) plus 25 mg/ml of the following
antibiotics chloramphenicol (C), ampicillin (A),
tetracycline (T) and streptomycin (S).
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Primary Modeling
N(t) Nmax/(1 ((Nmax/No) 1) exp (- m
t))
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Comparison of MPN and CFU
a
b
Means with different superscripts differ at P lt
0.05
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Primary Modeling
Dependent Data
Temp. Nmax (log/g)
10oC 1.63
12oC 2.70
14oC 4.98
22oC 6.43
30oC 8.49
40oC 9.36
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Primary Modeling
Independent Data
Temp. Nmax (log/g)
11oC 2.28
18oC 5.34
26oC 7.63
34oC 9.29
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Performance EvaluationSecondary Models

• Relative Error (RE)
• m and Nmax (O P) / P
• 95 PI (P O) / P
• Acceptable Prediction Zone
• m -0.3 to 0.15
• Nmax and PI -0.8 to 0.40
• RE
• REIN / RETOTAL
• gt 70 acceptable

1. Oscar, T. P. 2005. J. Food Sci.
70M129-M137. 2. Oscar, T. P. 2005. J. Food
Prot. 682606-2613.
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Secondary Model for m
RE 83 100
mi 0.047 h-1 To 15.6oC mrate 0.22
h-1/oC mopt 0.41 h-1

• mi
  
  if T lt To
• m mopt/1 ((mopt/mi) - 1) exp (-mrate (T
  To) if T gt To

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Secondary Model for Nmax
RE 83 75
a 2.47 Tmin 9.11oC Tsubmin 5.66oC
Nmax exp(a (T Tmin)/(T Tsubmin))
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Secondary Model for 95 Prediction Interval
PI1 1.33 log/g PI2 2.58 log/g PI3 1.94
log/g T1 10oC T2 14.8oC T3 26.9oC
RE 100 50
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Tertiary Modeling
Tertiary Model
Secondary Models
Observed W
Predicted W
W Model
Observed N(t)
Observed PI
Predicted PI
PI Model
Primary Model
Primary Model
m Model
Observed m
Predicted m
Predicted N(t)
Predicted N(t)
Nmax Model
Observed Nmax
Predicted Nmax
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Performance Evaluation Tertiary Model

• 90 Concordance
• N(t)IN / N(t)TOTAL gt 90
• Dependent Data
• 93 (322/344)
• Independent Data
• 94 (223/236)

Oscar, T. P. 2006. J. Food Prot. (in press)
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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.