An approach for mapping the number and distribution of Salmonella contamination on the poultry carcass

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: I. Initial Contamination

The Poultry Food Assess Risk Model (PFARM) project was initiated in 1995 to develop data collection and modeling methods for simulating the risk of salmonellosis from poultry food produced by individual production chains. In the present study, the Initial Contamination (IC) step of PFARM for Salmonella and chicken gizzards (CG) was conducted as a case study. Salmonella prevalence (Pr), number (N), and serotype/zoonotic potential (ZP) data (n = 100) for one sample size (56 g) of CG were collected at meal preparation (MP), and then Monte Carlo simulation (MCS) was used to obtain data for other sample sizes (112, 168, 224, 280 g). The PFARM was developed in Excel and was simulated with @Risk. Data were simulated using a moving window of 60 samples to determine how Salmonella Pr, N, and ZP changed over time in the production chain. The ability of Salmonella to survive, grow, and spread in the production chain and food, and then cause disease in humans was ZP, which was based on U. S. Centers for Disease Control and Prevention data for salmonellosis. Of 100 CG samples tested, 35 were contaminated with Salmonella with N from 0 to 0.809 (median) to 2.788 log per 56 g. Salmonella serotype Pr per 56 g was 16% for Kentucky (ZP_mode = 1.1), 9% for Infantis (ZP_mode = 4.4), 6% for Enteritidis (ZP_mode = 5.0), 3% for Typhimurium (ZP_mode = 4.9), and 1% for Thompson (ZP_mode = 3.7). Results from MCS indicated that Salmonella Pr, N, and ZP among portions of CG at MP changed (P ≤ 0.05) over time in the production chain. Notably, the main serotype changed from Kentucky (low ZP) to Infantis (high ZP). However, the pattern of change for Salmonella Pr, N, and ZP differed over time in the production chain and by the statistic used to characterize it. Thus, a performance standard (PS) based on Salmonella Pr, N, or ZP at testing or MP will likely not be a good indicator of poultry food safety or risk of salmonellosis.

Use of enrichment real-time PCR to enumerate salmonella on chicken parts

Salmonella bacteria that survive cooking or that cross-contaminate other food during meal preparation and serving represent primary routes of consumer exposure to this pathogen from chicken. In the present study, enrichment real-time PCR (qPCR) was used to enumerate Salmonella bacteria that contaminate raw chicken parts at retail or that cross-contaminate cooked chicken during simulated meal preparation and serving. Whole raw chickens obtained at retail were partitioned into wings, breasts, thighs, and drumsticks using a sterilized knife and cutting board, which were then used to partition a cooked chicken breast to assess cross-contamination. After enrichment in buffered peptone water (400 ml, 8 h, 40°C, 80 rpm), subsamples were used for qPCR and cultural isolation of Salmonella. In some experiments, chicken parts were spiked with 0 to 3.6 log of Salmonella Typhimurium var. 5- to generate a standard curve for enumeration by qPCR. Of 10 raw chickens examined, 7 (70%) had one or more parts contaminated with Salmonella. Of 80 raw parts examined, 15 (19%) were contaminated with Salmonella. Of 20 cooked chicken parts examined, 2 (10%) were cross-contaminated with Salmonella. Predominant serotypes identified were Typhimurium (71%) and its variants (var. 5-, monophasic, and nonmotile) and Kentucky (18%). The number of Salmonella bacteria on contaminated parts ranged from one to two per part. Results of this study indicated that retail chicken parts examined were contaminated with low levels of Salmonella, which resulted in low levels of cross-contamination during simulated meal preparation and serving. Thus, if consumers properly handle and prepare the chicken, it should pose no or very low risk of consumer exposure to Salmonella.

Initial contamination of chicken parts with Salmonella at retail and cross-contamination of cooked chicken with Salmonella from raw chicken during meal preparation

The current study was undertaken to acquire data on contamination of chicken parts with Salmonella at retail and to acquire data on cross-contamination of cooked chicken with Salmonella from raw chicken during meal preparation. Whole raw chickens (n = 31) were obtained from local retail stores and cut into two wings, two breasts without skin or bones, two thighs, and two drumsticks. Data for cross-contamination were obtained by cutting up a sterile, cooked chicken breast with the same board and knife used to cut up the raw chicken. The board, knife, and latex gloves used by the food handler were not rinsed or washed before cutting up the sterile, cooked chicken breast, thus providing a worst-case scenario for cross-contamination. Standard curves for the concentration of Salmonella bacteria in 400 ml of buffered peptone water after 6 h of incubation of chicken parts as a function of the initial log number of Salmonella bacteria inoculated onto chicken parts were developed and used to enumerate Salmonella bacteria. Standard curves were not affected by the type of chicken part but did differ (P < 0.05) among the five isolates of Salmonella examined. Consequently, Salmonella bacteria were enumerated on naturally contaminated chicken parts using a standard curve developed with the serotype of Salmonella that was isolated from the original sample. The prevalence of contamination was 3 % (4 of 132), whereas the incidence of cross-contamination was 1.8 % (1 of 57). The positive chicken parts were a thigh from chicken 4, which contained 3 CFU of Salmonella enterica serotype Kentucky, and both wings, one thigh, and one cooked breast portion from chicken 15, which all contained 1 CFU of serotype 8,20:-:z(6). These results indicated that the poultry industry is providing consumers in the studied area with chicken that has a low prevalence and low number of Salmonella bacteria at retail and that has a low incidence and low level of cross-contamination of cooked chicken with Salmonella from raw chicken during meal preparation under a worst-case scenario.

Inactivation of salmonella in biofilms and on chicken cages and preharvest poultry by levulinic Acid and sodium dodecyl sulfate

Surface contamination (skin and feathers) of broilers with Salmonella occurs primarily during growth and transportation. Immediately after transporting chickens, chicken cage doors were sprayed with a foam containing 3% levulinic acid plus 2% sodium dodecyl sulfate (SDS). Samples were collected for Salmonella assay after 45 min. Salmonella on cage doors was reduced from 19% (19 of 100 doors) before treatment to 1% (1 of 100 doors) after treatment, coliform counts were reduced from 6 to 8 to 2 to 4 log CFU/9 cm(2), and aerobic plate counts were reduced from 7 to 9 to 4 to 6 log CFU/9 cm(2). Whole chicken carcasses with feathers were inoculated with 10(8) CFU of Salmonella Enteritidis, soaked for 5 min at 21°C in 72 liters of a treatment or control solution, and assayed for Salmonella. Salmonella counts on chickens treated with water were 6.8 to 8.5 log CFU/9 cm(2), those treated with 50 ppm of calcium hypochlorite were 7.6 to 8.9 log CFU/9 cm(2), and those treated with 3% levulinic acid plus 2% SDS were <1.7 to 2.8 CFU/9 cm(2) (>4-log reduction). Results of biofilm studies on surfaces of various materials revealed that a 3% levulinic acid plus 2% SDS treatment used as either a foam or liquid for 10 min effectively reduced Salmonella populations by 5 and >6 log CFU/cm(2), respectively.

Qualitative map of Salmonella contamination on young chicken carcasses

Salmonella contamination of poultry is a global public health problem. The objective of this study was to map the distribution of Salmonella on the young chicken carcass, to improve poultry inspection and food safety. Young chickens (n = 70) in the Cornish game hen class were obtained at retail over a 3-year period. Carcasses were aseptically sectioned into 12 parts, and then Salmonella was isolated from whole-part incubations by conventional culture methods. Isolates were characterized for serotype and antibiotic resistance, and by pulsed-field gel electrophoresis (PFGE). Salmonella incidence was 21.5% (181 of 840) for parts and 57.1% (40 of 70) for carcasses. The number of contaminated parts per carcass ranged from 0 to 12, with a mean of 4.5 among contaminated carcasses. Chi-square analysis indicated that Salmonella incidence differed (P < 0.05) among parts, with rib back (38.6%) and sacral back (34.3%) being the most contaminated. Among the 40 contaminated carcasses, there were 37 different patterns of contamination among parts. Of the 33 carcasses with more than one contaminated part, 12.1% contained two serotypes, 33.3% contained two or more antibiotic resistance profiles, and 100% contained two or more PFGE patterns. The most common serotype was Typhimurium (94.5%), and most (97.2%) isolates were resistant to multiple antibiotics. These results indicated a diverse pattern of Salmonella contamination among carcasses and that multiple subtypes of Salmonella were often present on contaminated carcasses. Thus, whole-carcass incubation succeeded by characterization of multiple isolates per carcass is needed to properly assess and manage this risk to public health.

Predictive model for survival and growth of Salmonella typhimurium DT104 on chicken skin during temperature abuse

To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50 degrees C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (lamda), specific growth rate (micrro), and the 95% prediction interval (PI). Secondary models for lamda, mu, and PI as a function of storage temperature were developed and then combined with the primary model to create a tertiary model. Performance of the tertiary model was evaluated against dependent data, independent data for interpolation, and independent data for extrapolation to kosher chicken skin by using an acceptable prediction zone from -1 (fail-safe) to 0.5 (fail-dangerous) log per skin portion. Survival of Salmonella Typhimurium DT104 on chicken skin was observed during 8 h of storage at 5 to 20 degrees C and at 50 degrees C, whereas growth was observed from 25 to 45 degrees C and was optimal at 40 degrees C with a lamda of 2.5 h and a mu of 1.1 log/h. Variation of pathogen growth, as assessed by PI, increased in a nonlinear manner as a function of temperature and was greater for growth conditions than no-growth conditions. The percentage of acceptable prediction errors was 82.6% for dependent data, 83.7% for independent data for interpolation, and 81.6% for independent data for extrapolation to kosher skin, which all exceeded the performance criterion of 70% acceptable predictions. Thus, it was concluded that the tertiary model provided valid predictions for survival and growth of Salmonella Typhimurium DT104 from a low initial dose on both nonkosher and kosher chicken skin with native flora.

Predicting pathogen growth during short-term temperature abuse of raw sausage

Lag-phase duration (LPD) and growth rate (GR) values were calculated from experimental data obtained using a previously described protocol (S. C. Ingham, M. A. Fanslau, G. M. Burnham, B. H. Ingham, J. P. Norback, and D. W. Schaffner, J. Food Prot. 70:1445-1456, 2007). These values were used to develop an interval accumulation-based tool designated THERM (temperature history evaluation for raw meats) for predicting growth or no growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw sausage. Data (time-temperature and pathogen log CFU per gram) were obtained from six inoculation experiments with Salmonella, E. coli O157:H7, and S. aureus in three raw pork sausage products stored under different temperature abuse conditions. The time-temperature history from each experiment was entered into THERM to predict pathogen growth. Predicted and experimental results were described as growth (> 0.3 log increase in CFU) or no growth (< or = 0.3 log increase in CFU) and compared. The THERM tool accurately predicted growth or no growth for all 18 pathogen-experiment combinations. When compared with the observed changes in log CFU values for the nine pathogen-experiment combinations in which pathogens grew, the predicted changes in log CFU values were within 0.3 log CFU for three combinations, exceeded observed values by 0.4 to 1.5 log CFU in four combinations, and were 1.2 to 1.4 log CFU lower in two combinations. The THERM tool approach appears to be useful for predicting pathogen growth versus no growth in raw sausage during temperature abuse, although further development and testing are warranted.