Predictive Microbiology Approach for Enumeration of Salmonella on Chicken Parts Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853; ; (fax); INTRODUCTION A data gap identified in risk assessments for Salmonella and chicken is lack of quantitative data. Enumeration of Salmonella on chicken parts is difficult because Salmonella are often present in low numbers. However, during the pre-enrichment phase of Salmonella isolation from chicken parts, there is a mathematical relationship between the initial number of Salmonella on the chicken part and the concentration of Salmonella in the pre-enrichment broth at early times of incubation 1. Thus, it should be possible to develop a mathematical model that predicts the number of Salmonella on chicken parts as a function of the concentration of Salmonella in isolation broth at an early time of incubation during pre-enrichment of whole chicken parts. OBJECTIVE To develop a predictive model for enumerating Salmonella on chicken parts during pre-enrichment. MATERIALS AND METHODS Salmonella. Predictive models for enumerating Salmonella on chicken parts were developed with four isolates of Salmonella: S. Typhimurium DT104 ATCC s165 (sT165), S. Typhimurium ATCC s2 (sT2), S. Kentucky s361 (sK361), and S. 8,20:-:z6 s362 (sz362). Chicken part preparation. Fresh, whole chickens were purchased at retail. A sterile cutting board and knife were used to partition the whole chicken into two wings, two breasts, two drumsticks, and two thighs. A sterile cooked chicken breast was then cut into two equal-size portions using the cutting board and knife used to partition the whole raw chicken; this was done to study transfer of Salmonella from raw chicken to cooked chicken during meat preparation. Chicken part inoculation and incubation. Chicken parts were pre-enriched in 400 ml of buffered peptone water (BPW) for 6 h at 42°C and 80 rpm. For predictive model development, chicken parts were spot inoculated (5 μl) with 0.36 to 4.86 logs of Salmonella before pre-enrichment in BPW. Model development. At 6 h of pre-enrichment, the concentration of Salmonella in the BPW pre-enrichment (Y; log CFU/ml) was determined by spiral plating onto XLT4 agar, graphed as a function of the log number of Salmonella inoculated onto the chicken parts (X; log 10 CFU/part) and then the data were fitted to a linear model using GraphPad Prism for Windows version 5.02: Y = a + bX where a was the Y-intercept and b was the slope of the best-fit line. Pair-wise comparisons of the parameters of the linear models among chicken parts and Salmonella isolates were made using an F-test in Prism. Prevalence of Salmonella among edible parts from whole chickens obtained at retail was 3% (4/132), whereas incidence of cross-contamination from raw chicken to cooked chicken breast during simulated meal preparation was 1.8% (1/57). The positive chicken parts (Table 3) were thigh from chicken #4, which was contaminated with 3 CFU of serotype Kentucky, and both wings, one thigh and one cooked breast portion from chicken #15, which were all contaminated with 1 CFU of serotype 8,20:-:z6. DISCUSSION The reason for acquiring data for initial contamination of chicken parts with Salmonella and cross-contamination of other foods with Salmonella from raw chicken during meal preparation was to fill important data gaps in risk assessments for Salmonella and chicken that are being performed by regulatory agencies in the United States, Europe and throughout the world. These risk assessments are being used as the scientific basis to inform policy decisions aimed at protecting public health from this important foodborne pathogen. For risk assessment purposes it is not only useful to know the number of pathogens on and in a food sample but also their ability to grow and cause infection. Thus, an enumeration method, such as the one used in the current study, that is based on the growth kinetics of Salmonella during incubation of the food sample under favorable conditions for growth, provides the type of quantitative data that is highly relevant for risk assessments. These results indicated that a predictive microbiology approach can be used to enumerate low numbers of Salmonella on chicken parts during pre-enrichment of samples in BPW. However, because of the low prevalence of Salmonella on the chicken parts examined it was not possible, at this time, to fill the data gap for quantitative data identified in recent risk assessments for Salmonella and chicken. Fig. 1. Pair-wise comparisons of linear models developed for different types of chicken parts; combined data from all isolates of Salmonella. Fig. 2. Pair-wise comparisons of linear models developed for different isolates of Salmonella; combined data from all types of chicken parts. RESULTS All linear models had high goodness-of-fit (Table 1) regardless of the serotype of Salmonella used. Parameters of the linear models were not affected (P > 0.05) by type of chicken part (Fig. 1) but were affected (P < 0.05) by isolate of Salmonella (Fig. 2). Isolates sT165 and sT2 from ATCC were found to grow faster (higher Y-intercept but similar slope) than isolates sK361 and sz362, which were isolated from chicken parts in the present study (Table 2). Table 3. Serotype of number of Salmonella per chicken part ChickenDateBrandPartWeight (g)IsolateSerotypelog CFU/mllog CFU/partCFU/part 412/14/2010Cthigh173s361Kentucky /12/2011Awing74s3628,20:-:z /12/2011Athigh129s3638,20:-:z /12/2011Acooked102s3648,20:-:z /12/2011Awing78s3658,20:-:z Table 2. Pair-wise comparison of standard curve parameters among isolates of Salmonella Y-interceptSlope ComparisonF-valueP-valueDFnDFdF-valueP-valueDFnDFd sT165 vs sT sT165 vs sK < sT165 vs sz sT2 vs sK < sT2 vs sz < sK361 vs sz Table 1. Standard curve parameters for different isolates of Salmonella inoculated onto chicken parts IsolateCodeParameterBFVSECIR2R2 S. Typhimurium DT104 ATCC s165sT165Y-int to S. Typhimurium ATCC s2sT to S. Kentucky s361sK to S. 8,20:-:z6 s362sz to S. Typhimurium DT104 ATCC s165sT165slope to 1.04 S. Typhimurium ATCC s2sT to 1.10 S. Kentucky s361sK to 1.08 S. 8,20:-:z6 s362sz to 1.19 Abbreviations: BFV = best fit value; SE = standard error; CI = 95% confidence interval; and R 2 = coefficient of determination. “worst-case scenario” ‘retail chicken’ ‘400 ml BPW’ ‘6 h of incubation’