Pirkko Tuominen

Trapping the food safety performance of a small or medium-sized food company using a risk-based model. The HYGRAM® system

The requirements of implementing Hazard Analysis and Critical Control Points (HACCP) principles in food production are increasing. A practical risk quantification model, HYGRAM, was developed for small and medium-sized enterprises to meet this challenge. The model makes the user familiar with the HACCP principles by software-assisted guidance through the procedure, connecting special microbiological hazards, good hygiene practice, and other prerequisite programs to HACCP. HYGRAM is a tool to analyze and quantify risks of different processes, and to compare them. It is developed to relieve enterprises with limited resources in confirming the food safety of their production.

Estimation of True Salmonella Prevalence Jointly in Cattle Herd and Animal Populations Using Bayesian Hierarchical Modeling

The Finnish salmonella control program (FSCP) for beef production is based on both randomized and selective testing of herds and animals. Sampling of individual animals in abattoirs is randomized. Herds are selectively tested for salmonella on the basis of clinical symptoms and/or other factors. Risk assessment of FSCP is inherently difficult due to the complexity of the complete data set, especially if the detailed labeling of the testing types is lost. However, for a risk assessment of the whole production chain, methods for exploiting all available data should be considered. For this purpose, a hierarchical Bayesian model of true salmonella prevalence was constructed to combine information at different levels of aggregation: herds in geographical regions and individual animals arriving for slaughter. The conditional (municipality specific) probability of selection of a herd for testing was modeled given the underlying true infection status of the herd and information about the general sampling activity in the specific region. The model also accounted for the overall sensitivity of the sampling methods, both at the herd and at the animal level. In 1999, the 95% posterior probability intervals of true salmonella prevalence in the herd population, in individual cattle, and in slaughter animal populations were [0.54%, 1.4%] (mode 0.8%), [0.15%, 0.39%] (mode 0.2%), and [0.12%, 0.36%] (mode 0.2%), respectively. The results will be further exploited in other risk assessments focusing on the subsequent parts of the beef production chain, and in evaluation of the FSCP.

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring.

A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.

A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat

Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk,

Quantitative risk assessment on the dietary exposure of Finnish children and adults to nitrite

E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})

Dietary exposure of Finnish children to heavy metal mixture – a cumulative assessment

, and relative posterior risk,

Bayesian model for tracing Salmonella contamination in the pig feed chain

\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})

Salmonella risk to consumers via pork is related to the Salmonella prevalence in pig feed

, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.

Dietary heavy metal exposure of Finnish children of 3 to 6 years

ABSTRACT Nitrite intake from the consumption of cured meat and tap water was estimated for Finnish children of 1, 3 and 6 years as well as Finnish adults of 25–74 years. Nitrite content in the foods was measured by capillary electrophoresis, and was then used together with individual food consumption data from the FINDIET 2007 and DIPP studies in a stochastic exposure assessment by a Monte Carlo Risk Assessment (MCRA) program. Nitrite intake from additive sources and tap water was assessed, and more than every 10th child between the ages 3 and 6 years was estimated to have a nitrite intake exceeding the acceptable daily intake (ADI) of nitrite. The high exposure levels were caused by frequent consumption of large portions of sausages, up to 350 g day–1 or 750 g in 3 days, among the children. Median nitrite intake from cured meat was 0.016, 0.040, 0.033 and 0.005 mg kg–1 body weight day–1 for children of 1, 3 and 6 years and adults, respectively. Bayesian estimation was employed to determine safe consumption levels of sausages and cold cuts for children, and these results gave rise to new national food consumption advice. Graphical Abstract

Attitudes towards own-checking and HACCP plans among Finnish food industry employees

ABSTRACT A preliminary assessment of the cumulative exposure to heavy metals among Finnish preschool children is reported. Cadmium, lead, arsenic,and mercury affect many of the same organs in the human body. The effects are mostly caused by oxidative stress or disruption of enzyme function. The cumulative effects of the heavy metals on the central nervous system and on the kidneys are determined based on national concentration and consumption data, and comparison of the relative toxicity of the heavy metals is based on dose–response values found in the literature. The cumulative effects were assumed to be additive. The main contributors to kidney toxicity among the studied population groups were cadmium and lead, while lead was the main contributor to neurotoxic effects.