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.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning

To predict bacterial population behavior in food, statistical models with specific function form have been applied in the field of predictive microbiology. Modelers need to consider the linear or non-linear relationship between the response and explanatory variables in the statistical modeling approach. In the present study, we focused on machine learning methods to skip definition of primary and secondary structure model. Support vector regression, extremely randomized trees regression, and Gaussian process regression were used to predict population growth of Escherichia coli O157 at 15 and 25 °C without defining the primary and secondary models. Furthermore, the support vector regression model was applied to predict small population of bacteria cells with probability theory. The model performance of the machine learning models were nearly equal to that of the current statistical models. Machine learning models have a potential for predicting bacterial population behavior.

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Salmonella is a leading cause of foodborne illness (i.e., salmonellosis) outbreaks, which on occasion are attributed to ground turkey. The poultry industry uses Salmonella prevalence as an indicator of food safety. However, Salmonella prevalence is only one of several factors that determine risk of salmonellosis. Consequently, a model for predicting risk of salmonellosis from individual lots of ground turkey as a function of Salmonella prevalence and other risk factors was developed. Data for Salmonella contamination (prevalence, number, and serotype) of ground turkey were collected at meal preparation. Scenario analysis was used to evaluate effects of model variables on risk of salmonellosis. Epidemiological data were used to simulate Salmonella serotype virulence in a dose-response model that was based on human outbreak and feeding trial data. Salmonella prevalence was 26% (n = 100) per 25 g of ground turkey, whereas Salmonella number ranged from 0 to 1.603 with a median of 0.185 log per 25 g. Risk of salmonellosis (total arbitrary units (AU) per lot) was affected (p ≤ 0.05) by Salmonella prevalence, number, and virulence, by incidence and extent of undercooking, and by food consumption behavior and host resistance but was not (p > 0.05) affected by serving size, serving size distribution, or total bacterial load of ground turkey when all other risk factors were held constant. When other risk factors were not held constant, Salmonella prevalence was not correlated (r = -0.39; p = 0.21) with risk of salmonellosis. Thus, Salmonella prevalence alone was not a good indicator of poultry food safety because other factors were found to alter risk of salmonellosis. In conclusion, a more holistic approach to poultry food safety, such as the process risk model developed in the present study, is needed to better protect public health from foodborne pathogens like Salmonella.

Impact of sodium lactate, encapsulated or unencapsulated polyphosphates and their combinations on Salmonella Typhimurium, Escherichia coli O157:H7 and Staphylococcus aureus growth in cooked ground beef

Foodborne illnesses affect the health of consumers worldwide, and thus searching for potential antimicrobial agents against foodborne pathogens is given an increased focus. This research evaluated the influence of sodium lactate (SL), encapsulated (e) and unencapsulated (u) polyphosphates (PP; sodium tripolyphosphate, STP; sodium acid pyrophosphate, SPP), and their combinations on Salmonella Typhimurium, Escherichia coli O157:H7 and Staphylococcus aureus growth in cooked ground beef during 30 day storage at 4 or 10 °C. pH, water activity (a_w), oxidation-reduction potential (ORP) and S. Typhimurium, E. coli O157:H7 and S. aureus counts were determined. S. Typhimurium was not found in SPP-SL combination groups after 30 day storage at 4 °C (P <0.05). Lower S. Typhimurium levels were determined in only SL containing groups stored at 10 °C than group with only tested microorganism (MO, P < 0.05). Although there was no change in S. Typhimurium load in all SL incorporated groups during 10 °C storage, S. Typhimurium count increased in other groups (P < 0.05). E. coli O157:H7 in MO and STP groups showed an increase at 4 °C, whereas it decreased in SPP-SL combination groups (P < 0.05). A gradual increase in E. coli O157:H7 at 10 °C was determined in MO and only PP incorporated groups, whereas there was a decrease in STP-SL or SPP-SL combination groups (P < 0.05). E. coli O157:H7 count was stable in SL containing groups during 10 °C storage. A gradual decrease in S. aureus was determined in all treatments at 4 °C, whereas S. aureus count increased in MO and uSTP groups during 10 °C storage (P < 0.05). There was no change in S. aureus level in only eSTP or uSPP or ueSTP containing groups at 10 °C, meantime it decreased in other groups (P < 0.05). The lowest S. aureus load was achieved by uSPP-SL or eSPP-SL or ueSPP-SL combinations after 30 days at both storage temperatures (P < 0.05). In general, pH was higher in samples with STP than those with SPP and control (P < 0.05). The lowest a_w was generally obtained in all SL containing groups at both storage temperatures (P < 0.05). Lower ORP was determined in all PP incorporated groups during storage at both temperatures compared to others (P < 0.05). ORP in all treatments generally increased (P < 0.05) during storage at both storage temperatures. This study showed that encapsulation is not a factor affecting antimicrobial efficiency of PP and using PP-SL combinations have synergistic effect on reducing the viability of S. Typhimurium, E. coli O157:H7 and S. aureus and their subsequent growth ability in cooked ground beef.

Salmonella Cold Stress Response: Mechanisms and Occurrence in Foods

Since bacteria in foods often encounter various cold environments during food processing, such as chilling, cold chain distribution, and cold storage, lower temperatures can become a major stress environment for foodborne pathogens. Bacterial responses in stressful environments have been considered in the past, but now the importance of stress responses at the molecular level is becoming recognized. Documenting how bacterial changes occur at the molecular level may help to achieve the in-depth understanding of stress responses, to predict microbial fate when they encounter cold temperatures, and to design and develop more effective strategies to control pathogens in food for ensuring food safety. Microorganisms differ in responding to a sudden downshift in temperature and this, in turn, impacts their metabolic processes and can cause various structural modifications. In this review, the fundamental aspects of bacterial cold stress responses focused on cell membrane modification, DNA supercoiling modification, transcriptional and translational responses, cold-induced protein synthesis including CspA, CsdA, NusA, DnaA, RecA, RbfA, PNPase, KsgA, SrmB, trigger factors, and initiation factors are discussed. In this context, specific Salmonella responses to cold temperature including growth, injury, and survival and their physiological and genetic responses to cold environments with a focus on cross-protection, different gene expression levels, and virulence factors will be discussed.

Effects of Domestic Storage and Thawing Practices on Salmonella in Poultry-Based Meat Preparations

Among consumer food handling practices, time-temperature abuse has been reported as one of the most common contributory factors in salmonellosis outbreaks where the evidence is strong. The present study performed storage tests of burgers, sausages, and kebabs and investigated (i) the effect of refrigerator temperatures (4°C versus 8 or 12°C, which were the temperatures recorded in 33 and 3%, respectively, of domestic refrigerators in Italy), with or without prior temperature abuse (25°C for 2 h, simulating transport of meats from shop to home), and (ii) the impact of the thawing method (overnight in the refrigerator at 8°C versus on the kitchen countertop at 23°C) on the presence and numbers of Salmonella bacteria. Storage tests were carried out on naturally or artificially (Salmonella enterica serovar Typhimurium at ca. 10 CFU/g) contaminated products, while freezing-thawing tests were conducted only on artificially contaminated products (Salmonella Typhimurium at ca. 10, 100, and 1,000 CFU/g). The results from the artificially contaminated products showed significant (P < 0.05) growth of Salmonella Typhimurium at 12°C (i.e., from ca. 8 most probable number [MPN]/g to > 710 MPN/g) in kebabs after 7 and 10 days but more moderate growth in sausages (i.e., from ca. 14 MPN/g to a maximum of 96 MPN/g after 9 days of storage). Storage of naturally contaminated burgers or sausages (contamination at or below 1 MPN/g) at 4, 8, or 12°C and a short time of temperature abuse (2 h at 25°C) did not facilitate an increase in the presence and numbers of Salmonella bacteria. Thawing overnight in the refrigerator led to either a moderate reduction or no change of Salmonella Typhimurium numbers in burgers, sausages, and kebabs. Overall, this study showed that domestic storage and thawing practices can affect food safety and that time-temperature abuse can cause a substantial increase of Salmonella numbers in some types of poultry-based meat preparations, highlighting that efforts for the dissemination of consumer guidelines on the correct storage and handling of meats need to be continued.

Neural Network Model for Survival and Growth of Salmonella enterica Serotype 8,20:-:z6 in Ground Chicken Thigh Meat during Cold Storage: Extrapolation to Other Serotypes

Mathematical models that predict the behavior of human bacterial pathogens in food are valuable tools for assessing and managing this risk to public health. A study was undertaken to develop a model for predicting the behavior of Salmonella enterica serotype 8,20:-:z6 in chicken meat during cold storage and to determine how well the model would predict the behavior of other serotypes of Salmonella stored under the same conditions. To develop the model, ground chicken thigh meat (0.75 cm(3)) was inoculated with 1.7 log Salmonella 8,20:-:z6 and then stored for 0 to 8 -8 to 16°C. An automated miniaturized most-probable-number (MPN) method was developed and used for the enumeration of Salmonella. Commercial software (Excel and the add-in program NeuralTools) was used to develop a multilayer feedforward neural network model with one hidden layer of two nodes. The performance of the model was evaluated using the acceptable prediction zone (APZ) method. The number of Salmonella in ground chicken thigh meat stayed the same (P > 0.05) during 8 days of storage at -8 to 8°C but increased (P < 0.05) during storage at 9°C (+0.6 log) to 16°C (+5.1 log). The proportion of residual values (observed minus predicted values) in an APZ (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was 0.939 for the data (n = 426 log MPN values) used in the development of the model. The model had a pAPZ of 0.944 or 0.954 when it was extrapolated to test data (n = 108 log MPN per serotype) for other serotypes (S. enterica serotype Typhimurium var 5-, Kentucky, Typhimurium, and Thompson) of Salmonella in ground chicken thigh meat stored for 0 to 8 days at -4, 4, 12, or 16°C under the same experimental conditions. A pAPZ of ≥0.7 indicates that a model provides predictions with acceptable bias and accuracy. Thus, the results indicated that the model provided valid predictions of the survival and growth of Salmonella 8,20:-:z6 in ground chicken thigh meat stored for 0 to 8 days at -8 to 16°C and that the model was validated for extrapolation to four other serotypes of Salmonella.