Salmonella Prevalence in Raw Cocoa Beans and a Microbiological Risk Assessment to Evaluate the Impact of Cocoa Liquor Processing on the Reduction of Salmonella

Salmonella in raw cocoa beans (n = 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3-46 MPN/g except for one sample (4.1 × 104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70-90% dark chocolate tablet) was set up to understand changes in Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting, or liquor sterilization (achieving a 0-6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose-response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35 × 10-8 (95% CI: 3.27 × 10-10-1.59 × 10-7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1-418). The estimated mean risk per serving was 3.35 × 10-9 (95% CI: 3.27 × 10-11-1.59 × 10-8), and the estimated salmonellosis cases per year was 9 (95% CI: <1-42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no postcontamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process was considered to be adequate.

Risk Assessment Predicts Most of the Salmonellosis Risk in Raw Chicken Parts is Concentrated in Those Few Products with High Levels of High-Virulence Serotypes of Salmonella

Salmonella prevalence declined in U.S. raw poultry products since adopting prevalence-based Salmonella performance standards, but human illnesses did not reduce proportionally. We used Quantitative Microbial Risk Assessment (QMRA) to evaluate public health risks of raw chicken parts contaminated with different levels of all Salmonella and specific high- and low-virulence serotypes. Lognormal Salmonella level distributions were fitted to 2012 USDA-FSIS Baseline parts survey and 2023 USDA-FSIS HACCP verification sampling data. Three different Dose-Response (DR) approaches included (i) a single DR for all serotypes, (ii) DR that reduces Salmonella Kentucky ST152 virulence, and (iii) multiple serotype-specific DR models. All scenarios found risk concentrated in the few products with high Salmonella levels. Using a single DR model with Baseline data (μ = -3.19, σ = 1.29 Log CFU/g), 68% and 37% of illnesses were attributed to the 0.7% and 0.06% of products with >1 and >10 CFU/g Salmonella, respectively. Using distributions from 2023 HACCP data (μ = -5.53, σ = 2.45), 99.8% and 99.0% of illnesses were attributed to the 1.3% and 0.4% of products with >1 and >10 CFU/g Salmonella, respectively. Scenarios with serotype-specific DR models showed more concentrated risk at higher levels. Baseline data showed 92% and 67% and HACCP data showed >99.99% and 99.96% of illnesses attributed to products with >1 and >10 CFU/g Salmonella, respectively. Regarding serotypes using Baseline or HACCP input data, 0.002% and 0.1% of illnesses were attributed to the 0.2% and 0.4% of products with >1 CFU/g of Kentucky ST152, respectively, while 69% and 83% of illnesses were attributed to the 0.3% and 0.6% of products with >1 CFU/g of Enteritidis, Infantis, or Typhimurium, respectively. Therefore, public health risk in chicken parts is concentrated in finished products with high levels and specifically high levels of high-virulence serotypes. Low-virulence serotypes like Kentucky contribute few human cases.

Modeling Preharvest Cyclospora cayetanensis Sampling and Testing for Various Water and Produce Sampling Plans

As of August 2023, the two U.S. Food and Drug Administration (FDA) official detection methods for C. cayetanensis are outlined in the FDA Bacteriological Analytical Manual (BAM) Chapters 19b (produce testing) and 19c (agricultural water testing). These newly developed detection methods have been shown to not always detect contamination when present at low levels. Yet, industry and regulators may choose to use these methods as part of their monitoring and verification activities while detection methods continue to be improved. This study uses simulation to better understand the performance of these methods for various produce and water sampling plans. To do so, we used published FDA test validation data to fit a logistic regression model that predicts the methods' detection rate given the number of oocysts present in a 10-L agricultural water or 25 g produce sample. By doing so, we were able to determine contamination thresholds at which different numbers of samples (n = 1, 2, 4, 8, 16, and 32) would be adequate for detecting contamination. Furthermore, to evaluate sampling plans in use cases, a simulation was developed to represent C. cayetanensis contamination in agricultural water and on cilantro throughout a 45-day growth cycle. The model included uncertainty around the contamination sources, including scenarios of unintentionally contaminated irrigation water or in-field contamination. The results demonstrate that in cases where irrigation water was the contamination source, frequent water testing proved to be more powerful than produce testing. In scenarios where contamination occurred in-field, conducting frequent produce testing or testing produce toward the end of the season more reliably detected contamination. This study models the power of C. cayetanensis detection methods to understand the sampling plan performance and how these methods can be better used to monitor this emerging food safety hazard.

Aggregative Soil Sampling Using Boot Covers Compared to Soil Grabs From Commercial Romaine Fields Shows Similar Indicator Organism and Microbial Community Recoveries

Aggregative boot cover sampling may be a more representative, practical, and powerful method for preharvest produce soil testing than grab sampling because boot covers aggregate soil from larger areas. Our study tests if boot cover sampling results reflect quality and safety indicator organisms and community diversity of grab sampling. We collected soil samples from commercial romaine lettuce fields spanning 5060 m2 using boot covers (n = 28, m = 1.1 ± 0.4 g; wearing boot covers and walking along the path), composite grabs (n = 28, m = 231 ± 24 g; consisting of 60 grabs of 3-5 g each), and high-resolution grabs (n = 72, m = 56 ± 4 g; taking one sample per stratum). Means and standard deviations of log-transformed aerobic plate counts (APCs) were 7.0 ± 0.3, 7.1 ± 0.2, and 7.3 ± 0.2 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. APCs did not show biologically meaningful differences between sample types. Boot covers recovered on average 0.6 log(CFU/g) more total coliforms than both grabs (p < 0.001) where means and standard deviations of log-transformed counts were 3.2 ± 1.0, 2.6 ± 0.6, and 2.6 ± 1.0 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. There were no generic E. coli detected in any sample by enumeration methods with LODs of 1.3-2.1 log(CFU/g) for boot covers and 0.5 log(CFU/g) for both grabs. By 16S rRNA sequencing, community species diversity (alpha diversity) was not significantly different within collection methods. While communities differed (p < 0.001) between soil sampling methods (beta diversity), variance in microbial communities was not significantly different. Of the 28 phyla and 297 genera detected, 25 phyla (89%) and 258 genera (87%) were found by all methods. Overall, aggregative boot cover sampling is similar to both grab methods for recovering quality and safety indicator organisms and representative microbiomes. This justifies future work testing aggregative soil sampling for foodborne pathogen detection.

Assessing food safety risks based on a geospatial analysis: toward a cross-regional food safety management

BACKGROUND

Food safety risks (FSRs) are increasingly characterized by geographical complexity along with rapid urbanization, changing dietary pattern, and the modernization of the food industry. These factors pose challenges for food risk control in developing economies, more so during the global COVID-19 pandemic. The accurate assessment of risk source and transfer path is a crucial step toward enhancing cross-regional food safety management. This study aims to examine the spatial distribution, transfer path and driving factors of FSRs in China, provided with a national food safety database collected from 8.63 million batches of food sampling inspections for 33 different types of foods across 30 provinces.

RESULTS

The findings reveal significant regional disparities in FSRs, which is the highest in the west with small-scale sampling inspection and the lowest in the east with intensive sampling inspection. Catering and processed foods with higher daily consumption suffer more profound FSR than agricultural products. As evidenced by the shrinking low-low agglomeration areas, the local FSRs have been effectively controlled. The high-high agglomeration areas playing positive impacts on risk control are expanding while distributed discretely.

CONCLUSION

The spatial transfer of FSRs is significantly driven by multiple drivers: regulatory capacity and intensity, information disclosure, food industry, regional economy, and food consumption. Assessing FSRs based on a geospatial analysis contributes to identifying risk sources, optimizing risk management, and constructing a sustainable food safety system. © 2023 Society of Chemical Industry.

Simulation Evaluation of Power of Sampling Plans to Detect Cronobacter in Powdered Infant Formula Production

Cronobacteris a hazard in Powdered Infant Formula (PIF) products that is hard to detect due to localized and low-level contamination. We adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. We evaluated performance to detect published Cronobacter contamination profiles for a recalled PIF batch [42% prevalence, -1.8±0.7 log(CFU/g)] and a reference, non-recalled, PIF batch [1% prevalence, -2.4±0.8 log(CFU/g)]. Simulating a range of numbers of grabs [n=1 to 22,000 (representing testing every finished package)] with 300 g total composite mass showed that taking 30 or more grabs detected contamination reliably (<1% median probability to accept the recalled batch). Benchmarking representative sampling plans ([n=30, mass grab=10g], [n=30, m=25g], [n=60, m=25g], [n=180, m=25g]) showed that all plans would reject the recalled batch (<1% median probability to accept) but would rarely reject the reference batch (>50% median probability of acceptance, all plans). Overall, (i) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (ii) taking more samples, even if smaller, can increase the power to detect contamination.

A Validated Preharvest Sampling Simulation Shows that Sampling Plans with a Larger Number of Randomly Located Samples Perform Better than Typical Sampling Plans in Detecting Representative Point-Source and Widespread Hazards in Leafy Green Fields

Commercial leafy greens customers often require a negative preharvest pathogen test, typically by compositing 60 produce sample grabs of 150 to 375 g total mass from lots of various acreages. This study developed a preharvest sampling Monte Carlo simulation, validated it against literature and experimental trials, and used it to suggest improvements to sampling plans. The simulation was validated by outputting six simulated ranges of positive samples that contained the experimental number of positive samples (range, 2 to 139 positives) recovered from six field trials with point source, systematic, and sporadic contamination. We then evaluated the relative performance between simple random, stratified random, or systematic sampling in a 1-acre field to detect point sources of contamination present at 0.3% to 1.7% prevalence. Randomized sampling was optimal because of lower variability in probability of acceptance. Optimized sampling was applied to detect an industry-relevant point source [3 log(CFU/g) over 0.3% of the field] and widespread contamination [-1 to -4 log(CFU/g) over the whole field] by taking 60 to 1,200 sample grabs of 3 g. More samples increased the power of detecting point source contamination, as the median probability of acceptance decreased from 85% with 60 samples to 5% with 1,200 samples. Sampling plans with larger total composite sample mass increased power to detect low-level, widespread contamination, as the median probability of acceptance with -3 log(CFU/g) contamination decreased from 85% with a 150-g total mass to 30% with a 1,200-g total mass. Therefore, preharvest sampling power increases by taking more, smaller samples with randomization, up to the constraints of total grabs and mass feasible or required for a food safety objective. IMPORTANCE This study addresses a need for improved preharvest sampling plans for pathogen detection in leafy green fields by developing and validating a preharvest sampling simulation model, avoiding the expensive task of physical sampling in many fields. Validated preharvest sampling simulations were used to develop guidance for preharvest sampling protocols. Sampling simulations predicted that sampling plans with randomization are less variable in their power to detect low-prevalence point source contamination in a 1-acre field. Collecting larger total sample masses improved the power of sampling plans in detecting widespread contamination in 1-acre fields. Hence, the power of typical sampling plans that collect 150 to 375 g per composite sample can be improved by taking more, randomized smaller samples for larger total sample mass. The improved sampling plans are subject to feasibility constraints or to meet a particular food safety objective.

Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn

Probe sampling plans for aflatoxin in corn attempt to reliably estimate concentrations in bulk corn given complications like skewed contamination distribution and hotspots. To evaluate and improve sampling plans, three sampling strategies (simple random sampling, stratified random sampling, systematic sampling with U.S. GIPSA sampling schemes), three numbers of probes (5, 10, 100, the last a proxy for autosampling), four clustering levels (1, 10, 100, 1,000 kernels/cluster source), and six aflatoxin concentrations (5, 10, 20, 40, 80, 100 ppb) were assessed by Monte-Carlo simulation. Aflatoxin distribution was approximated by PERT and Gamma distributions of experimental aflatoxin data for uncontaminated and naturally contaminated single kernels. The model was validated against published data repeatedly sampling 18 grain lots contaminated with 5.8-680 ppb aflatoxin. All empirical acceptance probabilities fell within the range of simulated acceptance probabilities. Sensitivity analysis with partial rank correlation coefficients found acceptance probability more sensitive to aflatoxin concentration (-0.87) and clustering level (0.28) than number of probes (-0.09) and sampling strategy (0.04). Comparison of operating characteristic curves indicate all sampling strategies have similar average performance at the 20 ppb threshold (0.8-3.5% absolute marginal change), but systematic sampling has larger variability at clustering levels above 100. Taking extra probes improves detection (1.8% increase in absolute marginal change) when aflatoxin is spatially clustered at 1,000 kernels/cluster, but not when contaminated grains are homogenously distributed. Therefore, taking many small samples, for example, autosampling, may increase sampling plan reliability. The simulation is provided as an R Shiny web app for stakeholder use evaluating grain sampling plans.

In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities

Food facilities need time- and cost-saving methods during the development and optimization of environmental monitoring for pathogens and their surrogates. Rapid virtual experimentation through in silico modeling can alleviate the need for extensive real-world, trial-and-error style program design. Two agent-based models of fresh-cut produce facilities were developed as a way to simulate the dynamics of Listeria in the built environment by modeling the different surfaces of equipment and employees in a facility as agents. Five sampling schemes at three time points were evaluated in silico on their ability to locate the presence of Listeria contamination in a facility with sample sites for each scheme (i.e., scenario, as modeled using scenario analysis) based on the following: the facilities' current environmental monitoring program (scenario 1), Food and Drug Administration recommendations (scenario 2), random selection (scenario 3), sites exclusively from zone 3 (i.e., sites in the production room but not directly adjacent to food contact surfaces) (scenario 4), or model prediction of elevated risk of contamination (scenario 5). Variation was observed between the scenarios on how well the Listeria prevalence of the virtually collected samples reflected the true prevalence of contaminated agents in the modeled operation. The zone 3 only (scenario 4) and model-based (scenario 5) sampling scenarios consistently overestimated true prevalence across time, suggesting that those scenarios could provide a more sensitive approach for determining if Listeria is present in the operation. The random sampling scenario (scenario 3) may be more useful for operations looking for a scheme that is most likely to reflect the true prevalence. Overall, the developed models allow for rapid virtual experimentation and evaluation of sampling schemes specific to unique fresh-cut produce facilities. IMPORTANCE Programs such as environmental monitoring are used to determine the state of a given food facility with regard to the presence of environmental pathogens, such as Listeria monocytogenes, that could potentially cross-contaminate food product. However, the design of environmental monitoring programs is complex, and there are infinite ways to conduct the sampling that is required for these programs. Experimentally evaluating sampling schemes in a food facility is time-consuming, costly, and nearly impossible. Therefore, the food industry needs science-based tools to aid in developing and refining sampling plans that reduce the risk of harboring contamination. Two agent-based models of two fresh-cut produce facilities reported here demonstrate a novel way to evaluate how different sampling schemes can be rapidly evaluated across multiple time points as a way to understand how sampling can be optimized in an effort to locate the presence of Listeria in a food facility.

Salmonella, Food Safety and Food Handling Practices

Salmonellosis is the second most reported gastrointestinal disorder in the EU resulting from the consumption of Salmonella-contaminated foods. Symptoms include gastroenteritis, abdominal cramps, bloody diarrhoea, fever, myalgia, headache, nausea and vomiting. In 2018, Salmonella accounted for more than half of the numbers of foodborne outbreak illnesses reported in the EU. Salmonella contamination is mostly associated with produce such as poultry, cattle and their feeds but other products such as dried foods, infant formula, fruit and vegetable products and pets have become important. Efforts aimed at controlling Salmonella are being made. For example, legislation and measures put in place reduced the number of hospitalizations between 2014 and 2015. However, the number of hospitalizations started to increase in 2016. This calls for more stringent controls at the level of government and the private sector. Food handlers of "meat processing" and "Ready to Eat" foods play a crucial role in the spread of Salmonella. This review presents an updated overview of the global epidemiology, the relevance of official control, the disease associated with food handlers and the importance of food safety concerning salmonellosis.

An Assessment of Listeriosis Risk Associated with a Contaminated Production Lot of Frozen Vegetables Consumed under Alternative Consumer Handling Scenarios

Frozen foods do not support the growth of Listeria monocytogenes (LM) and should be handled appropriately for safety. However, consumer trends regarding preparation of some frozen foods may contribute to the risk of foodborne listeriosis, specifically when cooking instructions are not followed and frozen products are instead added directly to smoothies or salads. A quantitative microbial risk assessment model FFLLoRA (Frozen Food Listeria Lot Risk Assessment) was developed to assess the lot-level listeriosis risk due to LM contamination in frozen vegetables consumed as a ready-to-eat food. The model was designed to estimate listeriosis risk per serving and the number of illnesses per production lot of frozen vegetables contaminated with LM, considering individual facility factors such as lot size, prevalence of LM contamination, and consumer handling prior to consumption. A production lot of 1 million packages with 10 servings each was assumed. When at least half of the servings were cooked prior to consumption, the median risk of invasive listeriosis per serving in both the general and susceptible population was <1.0 × 10^-16 with the median (5th, 95th percentiles) predicted number of illnesses per lot as 0 (0, 0) and 0 (0, 1) under the exponential and Weibull-gamma dose-response functions, respectively. In scenarios in which all servings are consumed as ready-to-eat, the median predicted risk per serving was 1.8 × 10^-13 and 7.8 × 10^-12 in the general and susceptible populations, respectively. The median (5th, 95th percentile) number of illnesses was 0 (0, 0) and 0 (0, 6) for the exponential and Weibull-Gamma models, respectively. Classification tree analysis highlighted initial concentration of LM in the lot, temperature at which the product is thawed, and whether a serving is cooked as main predictors for illness from a lot. Overall, the FFLLoRA provides frozen food manufacturers with a tool to assess LM contamination and consumer behavior when managing rare and/or minimal contamination events in frozen foods.

Increasing incubation periods during a prolonged monophasic Salmonella Typhimurium outbreak with environmental contamination of a commercial kitchen at Oslo Airport, Norway, 2017

In September 2017, a cluster of monophasic Salmonella Typhimurium isolates was identified at the National Reference Laboratory for Enteropathogenic Bacteria in Norway. We investigated the cluster to identify the source and implement control measures. We defined a case as a person with laboratory-confirmed salmonellosis with the outbreak strain multiple locus variable-number tandem repeat analysis type. We conducted descriptive epidemiological and environmental investigations and performed whole genome sequencing (WGS) with core and accessory genome multilocus sequence typing of all isolates from cases or the environment connected with this outbreak. We identified 21 cases, residing in 10 geographically dispersed counties, all of whom had consumed food or drinks from a café at Oslo Airport. Case distribution by date of symptom onset suggested that a point source was introduced in mid-August followed by continued environmental contamination. The incubation periods ranged 0-16 days and increased as the outbreak progressed, likely due to increasingly low-dose exposure as control measures were implemented. WGS confirmed an identical cluster type-944 in all cases and six environmental specimens from the café. Control measures, including temporary closure and kitchen refurbishment, failed to eliminate the environmental source. We recommend strengthened hygiene measures for established environmental contamination during an outbreak.

Tail or artefact? Illustration of the impact that uncertainty of the serial dilution and cell enumeration methods has on microbial inactivation

The estimation of the concentration of microorganisms in a sample is crucial for food microbiology. For instance, it is essential for prevalence studies, challenge tests (growth and/or inactivation studies) or microbial risk assessment. The application of serial dilutions followed by viable counts in Petri dishes is probably the most extended experimental methodology for this purpose. However, this enumeration technique is also a source of uncertainty. In this article, the uncertainty of the serial dilution and viable count methodology related to the sampling error is analyzed, as well as the approximation of the microbial concentration by the number of colonies in a Petri dish. We analyze from a theoretical point of view (statistical analysis) the application of the binomial and Poisson models, demonstrating that the Poisson distribution increases the variance when used to model individual serial dilutions. On the other hand, the binomial model produces unbiased results. Therefore, the Poisson distribution is only applicable when it is a good approximation of the binomial distribution, so the use of the latter is recommended. The relevance of this uncertainty is demonstrated by Monte Carlo simulations of a generic microbial inactivation experiment, where the only source of uncertainty/variability considered is the one generated by serial plating and viable cell enumeration. Due to both the uncertainty of the methodology and the omission of zero-count plates because of the log-transformation, the simulated survival curve can have a tail. Therefore, this phenomenon, which is usually attributed to biological variability, can be to some extent an artefact of the experimental design and/or methodology.

Evaluation of sampling methods for the detection of pathogenic bacteria on pre-harvest leafy greens

Recent outbreaks of foodborne disease associated with leafy greens have led to increased pre-harvest testing for pathogens and indicator microorganisms. However, the scientific and statistical rationale and the performance attributes for pre-harvest sampling methods are not well understood. The performance of three pre-harvest sampling methods, random, stratified random, and Z-pattern sampling, was evaluated by consideration of their mathematical derivations, computer simulations and field validation. Consideration of the probabilistic basis of the sampling methods indicated that the mean detection rates were similar. However, use of simulation modeling to assess the uncertainty associated with the three sampling methods indicated that the inherent variability of the Z-pattern sampling method was substantially greater than the other two sampling methods. A simulation tool was developed in Matlab that allowed the evaluation of the effectiveness of the three sampling methods. A limited validation study also observed that Z-pattern sampling had higher variability than the other two sampling methods. This study indicates that while the mean detection probabilities for the three sampling methods are similar, the random or stratified random sampling are less variable, particularly when the number of contamination sites or number of samples analyzed are small.

Evaluating the U.S. Food Safety Modernization Act Produce Safety Rule Standard for Microbial Quality of Agricultural Water for Growing Produce

The U.S. Food and Drug Administration (FDA) has defined standards for the microbial quality of agricultural surface water used for irrigation. According to the FDA produce safety rule (PSR), a microbial water quality profile requires analysis of a minimum of 20 samples for Escherichia coli over 2 to 4 years. The geometric mean (GM) level of E. coli should not exceed 126 CFU/100 mL, and the statistical threshold value (STV) should not exceed 410 CFU/100 mL. The water quality profile should be updated by analysis of a minimum of five samples per year. We used an extensive set of data on levels of E. coli and other fecal indicator organisms, the presence or absence of Salmonella, and physicochemical parameters in six agricultural irrigation ponds in West Central Florida to evaluate the empirical and theoretical basis of this PSR. We found highly variable log-transformed E. coli levels, with standard deviations exceeding those assumed in the PSR by up to threefold. Lognormal distributions provided an acceptable fit to the data in most cases but may underestimate extreme levels. Replacing censored data with the detection limit of the microbial tests underestimated the true variability, leading to biased estimates of GM and STV. Maximum likelihood estimation using truncated lognormal distributions is recommended. Twenty samples are not sufficient to characterize the bacteriological quality of irrigation ponds, and a rolling data set of five samples per year used to update GM and STV values results in highly uncertain results and delays in detecting a shift in water quality. In these ponds, E. coli was an adequate predictor of the presence of Salmonella in 150-mL samples, and turbidity was a second significant variable. The variability in levels of E. coli in agricultural water was higher than that anticipated when the PSR was finalized, and more detailed information based on mechanistic modeling is necessary to develop targeted risk management strategies.

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross-contamination. We therefore developed a stochastic model to quantitatively describe cross-contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross-contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to -0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.

Microbiological and Modeling Approach to Derive Performance Objectives for Bacillus cereus Group in Ready-to-Eat Salads

In this article, the performance objectives (POs) for Bacillus cereus group (BC) in celery, cheese, and spelt added as ingredients in a ready-to-eat mixed spelt salad, packaged under modified atmosphere, were calculated using a Bayesian approach. In order to derive the POs, BC detection and enumeration were performed in nine lots of naturally contaminated ingredients and final product. Moreover, the impact of specific production steps on the BC contamination was quantified. Finally, a sampling plan to verify the ingredient lots' compliance with each PO value at a 95% confidence level (CL) was defined. To calculate the POs, detection results as well as results above the limit of detection but below the limit of quantification (i.e., censored data) were analyzed. The most probable distribution of the censored data was determined and two-dimensional (2D) Monte Carlo simulations were performed. The PO values were calculated to meet a food safety objective of 4 log₁₀ cfu of BC for g of spelt salad at the time of consumption. When BC grows during storage between 0.90 and 1.90 log₁₀ cfu/g, the POs for BC in celery, cheese, and spelt ranged between 1.21 log₁₀ cfu/g for celery and 2.45 log₁₀ cfu/g for spelt. This article represents the first attempt to manage the concept of PO and 2D Monte Carlo simulation in the flow chart of a complex food matrix, including raw and cooked ingredients.