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

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

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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 – PowerPoint PPT presentation

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


1
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

2
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

3
Hurdles for modeling Salmonella growth on chicken
with a natural microflora
  • Use of a low initial density
  • Strain with a proper phenotype

4
Salmonella Typhimurium DT104
  • Occurs in nature
  • Low prevalence on chicken
  • Resistant to multiple antibiotics
  • Stable phenotype
  • Growth similar to other strains

5
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 (unpublished data)
6
Objective
  • To overcome the hurdles for developing and
    validating a predictive model for growth of
    Salmonella on ground chicken with a natural
    microflora.

7
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
8
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
9
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).
10
Primary Modeling
N(t) Nmax/(1 ((Nmax/No) 1) exp (- m
t))
11
Comparison of MPN and CFU
a
b
Means with different superscripts differ at P lt
0.05
12
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
13
Primary Modeling
Independent Data
Temp. Nmax (log/g)
11oC 2.28
18oC 5.34
26oC 7.63
34oC 9.29
14
Performance Evaluation Secondary 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.
15
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

16
Secondary Model for Nmax
RE 83 75
a 2.47 Tmin 9.11oC Tsubmin 5.66oC
Nmax exp(a (T Tmin)/(T Tsubmin))
17
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
18
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
19
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20
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)
21
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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