Molecular analyses of Salmonella serotype Typhi and Salmonella ...
Modeling Salmonella Growth from a Low Initial Density on Chicken Products with Native Microflora...
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Modeling Salmonella Growth from a Low Initial Density on Chicken Products with Native Microflora Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853; 410-651-6062; 410-651-8498 (fax); [email protected] INTRODUCTION Lack of Salmonella strains with phenotypes that can be quantified in the presence of other microorganisms and lack of rapid methods for quantifying low initial levels of Salmonella are two technical issues that prevent development of predictive microbiology models in chicken products with native micro-flora and from a low and ecological dose of Salmonella. Although transformation of Salmonella with a green fluorescent protein gene from a jelly fish produces strains with a phenotype that can be quantified in the presence of other microorganisms, the transformed strains grow slower than the parent strains. Moreover, the slower growth is affected by a strain by temperature by growth parameter interaction that prevents application of a correction factor that would allow use of the strains for model development. Recently, it was discovered that a multiple antibiotic resistant (MAR) strain of Salmonella Typhimurium DT104 that occurs in nature has a phenotype that can be quantified in chicken products with native micro-flora. This strain was found to grow similar to other Salmonella strains from poultry and its MAR phenotype allowed development of a rapid MPN assay for development and validation of models for growth of Salmonella from a low initial dose on chicken products (i.e. ground chicken breast meat and chicken frankfurters) with native micro-flora. OBJECTIVE To develop and validate a model for growth of Salmonella from a low initial dose on chicken skin with a native micro-flora. MATERIALS AND METHODS Organism. A multiple antibiotic resistant (MAR) strain (ATCC 700408) of Salmonella Typhimurium DT104 was used for model development and validation. Chicken skin preparation. Non-kosher chicken thighs were purchased from local retail outlets, whereas kosher chicken thighs were purchased online. Chicken thigh skin was frozen briefly and then cut into circular 2.14 cm 2 portions. Chicken skin inoculation and incubation. Chicken skin portions were spot inoculated (5 l) with 0.8 log 10 S. Typhimurium DT104 followed by incubation at 5 to 50 o C on top of thigh meat in plastic jars. At selected times of incubation, an inoculated portion was added to 9 ml of buffered peptone water, pulsified and then the number of Salmonella in the pulsifate was determined using a 3 x 4 MPN method for low cell counts (0 to 3.24 log) or a spiral plating method for higher cell counts (> 3 log). Modeling. Kinetic data were graphed as a function of time and were fit to the Baranyi primary model to determine lag time, growth rate and the 95% prediction interval, which characterized the uncertainty of the primary model fit as well as the variation of Salmonella growth among batches of chicken skin. Secondary models for primary model parameters as a RESULTS AND DISCUSSION Variation of Salmonella growth among portions and batches of chicken skin with native microflora was significant (Fig. 1). This necessitated the development of a stochastic model. Variation of growth among portions and batches of chicken skin was quantified using a 95% prediction interval. The 95% prediction interval increased as a function of temperature and, in general, was greater under growth than no growth conditions (Fig. 2). The minimum temperature for growth of Salmonella is usually 5.5C or above. In the current study, the minimum temperature for growth of Salmonella was 21.5C because the length of the storage trial was only 8 h (Fig. 3). Growth rate of Salmonella Typhimurium DT104 on chicken skin was optimal at 40C and was found to fit well to a cardinal temperature model (Fig. 4). The secondary models for 95% prediction interval (Fig. 2), lag time (Fig. 3) and growth rate (Fig. 4) were combined with the primary model (Fig. 1) in a computer spreadsheet to create a tertiary model (Fig. 5) for predicting the growth of Salmonella Typhimurium DT104 from a low initial dose on non-kosher chicken skin as a function of time and temperature. Performance of the tertiary model was evaluated against dependent data (Fig. 6) and independent data for interpolation (Fig. 7) and for extrapolation to kosher chicken skin (Fig. 8) using the acceptable prediction zone (APZ) method. The tertiary model had acceptable performance (i.e. > 70% of prediction errors were in the APZ) for all data sets indicating that it provided acceptable predictions of Salmonella Typhimurium DT104 growth on both non-kosher and kosher chicken skin. 0 2 4 6 8 0 2 4 6 8 40 C Tim e (h) D T104 (log) 0 10 20 30 40 50 0.0 0.5 1.0 1.5 2.0 2.5 Tem perature ( C) PI(log) 0 10 20 30 40 50 0 2 4 6 8 10 Excluded Tem perature ( C) (h) 0 10 20 30 40 50 60 0.0 0.5 1.0 1.5 Tem perature ( C) (log/h) 0 2 4 6 8 -2 -1 0 1 2 D ependent Predicted N ( t)(log) PE (log) 0 2 4 6 8 -3 -2 -1 0 1 2 Interpolation Predicted N ( t)(log) PE (log) 0 2 4 6 8 -4 -2 0 2 Extrapolation Predicted N( t)(log) PE (log) Current studies are aimed at determining how well the model can extrapolate to other strains of Salmonella, other chicken products with native micro-flora, other initial doses of Salmonella and other previous histories. I would like to than Jacquelyn Ludwig, Stacey Engster and Sharif Walker for their excellent technical assistance on this project.
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Transcript of Modeling Salmonella Growth from a Low Initial Density on Chicken Products with Native Microflora...
Modeling Salmonella Growth from a Low Initial Density on Chicken Products with Native MicrofloraThomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center for Food Science and Technology, University of Maryland Eastern
Shore, Princess Anne, MD 21853; 410-651-6062; 410-651-8498 (fax); [email protected]
INTRODUCTIONLack of Salmonella strains with phenotypes that can be quantified in the presence of other microorganisms and lack of rapid methods for quantifying low initial levels of Salmonella are two technical issues that prevent development of predictive microbiology models in chicken products with native micro-flora and from a low and ecological dose of Salmonella.
Although transformation of Salmonella with a green fluorescent protein gene from a jelly fish produces strains with a phenotype that can be quantified in the presence of other microorganisms, the transformed strains grow slower than the parent strains. Moreover, the slower growth is affected by a strain by temperature by growth parameter interaction that prevents application of a correction factor that would allow use of the strains for model development.
Recently, it was discovered that a multiple antibiotic resistant (MAR) strain of Salmonella Typhimurium DT104 that occurs in nature has a phenotype that can be quantified in chicken products with native micro-flora. This strain was found to grow similar to other Salmonella strains from poultry and its MAR phenotype allowed development of a rapid MPN assay for development and validation of models for growth of Salmonella from a low initial dose on chicken products (i.e. ground chicken breast meat and chicken frankfurters) with native micro-flora.
OBJECTIVETo develop and validate a model for growth of Salmonella from a low initial dose on chicken skin with a native micro-flora.
MATERIALS AND METHODSOrganism. A multiple antibiotic resistant (MAR) strain (ATCC 700408) of Salmonella Typhimurium DT104 was used for model development and validation.
Chicken skin preparation. Non-kosher chicken thighs were purchased from local retail outlets, whereas kosher chicken thighs were purchased online. Chicken thigh skin was frozen briefly and then cut into circular 2.14 cm2 portions. Chicken skin inoculation and incubation. Chicken skin portions
were spot inoculated (5 l) with 0.8 log10 S. Typhimurium DT104 followed
by incubation at 5 to 50oC on top of thigh meat in plastic jars. At selected times of incubation, an inoculated portion was added to 9 ml of buffered peptone water, pulsified and then the number of Salmonella in the pulsifate was determined using a 3 x 4 MPN method for low cell counts (0 to 3.24 log) or a spiral plating method for higher cell counts (> 3 log).
Modeling. Kinetic data were graphed as a function of time and were fit to the Baranyi primary model to determine lag time, growth rate and the 95% prediction interval, which characterized the uncertainty of the primary model fit as well as the variation of Salmonella growth among batches of chicken skin. Secondary models for primary model parameters as a function of temperature were developed by non-linear regression and then combined with the primary model in a spreadsheet to create a tertiary model that predicted the variation of Salmonella growth as a function of time and temperature. Performance of the tertiary model was evaluated against both dependent and independent data using the acceptable prediction zone method.
RESULTS AND DISCUSSIONVariation of Salmonella growth among portions and batches of chicken skin with native microflora was significant (Fig. 1). This necessitated the development of a stochastic model.
Variation of growth among portions and batches of chicken skin was quantified using a 95% prediction interval. The 95% prediction interval increased as a function of temperature and, in general, was greater under growth than no growth conditions (Fig. 2).
The minimum temperature for growth of Salmonella is usually 5.5C or above. In the current study, the minimum temperature for growth of Salmonella was 21.5C because the length of the storage trial was only 8 h (Fig. 3).
Growth rate of Salmonella Typhimurium DT104 on chicken skin was optimal at 40C and was found to fit well to a cardinal temperature model (Fig. 4).
The secondary models for 95% prediction interval (Fig. 2), lag time (Fig. 3) and growth rate (Fig. 4) were combined with the primary model (Fig. 1) in a computer spreadsheet to create a tertiary model (Fig. 5) for predicting the growth of Salmonella Typhimurium DT104 from a low initial dose on non-kosher chicken skin as a function of time and temperature.
Performance of the tertiary model was evaluated against dependent data (Fig. 6) and independent data for interpolation (Fig. 7) and for extrapolation to kosher chicken skin (Fig. 8) using the acceptable prediction zone (APZ) method. The tertiary model had acceptable performance (i.e. > 70% of prediction errors were in the APZ) for all data sets indicating that it provided acceptable predictions of Salmonella Typhimurium DT104 growth on both non-kosher and kosher chicken skin.
0 2 4 6 8
0
2
4
6
840C
Time (h)
DT10
4 (log
)
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
Temperature (C)
PI (log
)
0 10 20 30 40 500
2
4
6
8
10
Excluded
Temperature (C)
(h
)
0 10 20 30 40 50 600.0
0.5
1.0
1.5
Temperature (C)
(log
/h)
0 2 4 6 8-2
-1
0
1
2Dependent
Predicted N(t) (log)
PE (lo
g)
0 2 4 6 8-3
-2
-1
0
1
2 Interpolation
Predicted N(t) (log)
PE (lo
g)
0 2 4 6 8-4
-2
0
2 Extrapolation
Predicted N(t) (log)
PE (lo
g)
Current studies are aimed at determining how well the model can extrapolate to other strains of Salmonella, other chicken products with native micro-flora, other initial doses of Salmonella and other previous histories.I would like to than Jacquelyn Ludwig, Stacey Engster and Sharif Walker for their excellent technical assistance on this project.
