Conducting inferential statistics for low microbial counts in foods using the Poisson-gamma regression

Distribution-based maximum likelihood estimation methods are preferred for estimating Salmonella concentration in chicken when contamination data are highly left-censored

Salmonella is a common chicken-borne pathogen that causes human infections. Data below the detection limit, referred to as left-censored data, are frequently encountered in the detection of pathogens. The approach of handling the censored data was regarded to affect the estimation accuracy of microbial concentration. In this study, a set of Salmonella contamination data was collected from chilled chicken samples using the most probable number (MPN) method, which consisted of 90.42% (217/240) non-detect values. Two simulated datasets with fixed censoring degrees of 73.60% and 90.00% were generated based on the real-sampling Salmonella dataset for comparison. Three methodologies were applied for handling left-censored data: (i) substitution with different alternatives, (ii) the distribution-based maximum likelihood estimation (MLE) method, and (iii) the multiple imputation (MI) method. For each dataset, the negative binomial (NB) distribution-based MLE and zero-modified NB distribution-based MLE were preferable for highly censored data and resulted in the least root mean square error (RMSE). Replacing the censored data with half the limit of quantification was the next best method. The mean concentration of Salmonella monitoring data estimated by the NB-MLE and zero-modified NB-MLE methods was 0.68 MPN/g. This study provided an available statistical method for handling bacterial highly left-censored data.

The COM-Poisson Process for Stochastic Modeling of Osmotic Inactivation Dynamics of Listeria monocytogenes

Controlling harmful microorganisms, such as Listeria monocytogenes, can require reliable inactivation steps, including those providing conditions (e.g., using high salt content) in which the pathogen could be progressively inactivated. Exposure to osmotic stress could result, however, in variation in the number of survivors, which needs to be carefully considered through appropriate dispersion measures for its impact on intervention practices. Variation in the experimental observations is due to uncertainty and biological variability in the microbial response. The Poisson distribution is suitable for modeling the variation of equi-dispersed count data when the naturally occurring randomness in bacterial numbers it is assumed. However, violation of equi-dispersion is quite often evident, leading to over-dispersion, i.e., non-randomness. This article proposes a statistical modeling approach for describing variation in osmotic inactivation of L. monocytogenes Scott A at different initial cell levels. The change of survivors over inactivation time was described as an exponential function in both the Poisson and in the Conway-Maxwell Poisson (COM-Poisson) processes, with the latter dealing with over-dispersion through a dispersion parameter. This parameter was modeled to describe the occurrence of non-randomness in the population distribution, even the one emerging with the osmotic treatment. The results revealed that the contribution of randomness to the total variance was dominant only on the lower-count survivors, while at higher counts the non-randomness contribution to the variance was shown to increase the total variance above the Poisson distribution. When the inactivation model was compared with random numbers generated in computer simulation, a good concordance between the experimental and the modeled data was obtained in the COM-Poisson process.

Probabilistic model for estimating Listeria monocytogenes concentration in cooked meat products from presence/absence data

A quantitative probabilistic model was developed to estimate the concentration of Listeria monocytogenes in cooked meat products based on presence/absence data and an assumed zero-inflated distribution, i.e. zero-inflated Poisson (ZIP) or zero-inflated Poisson lognormal (ZIPL) distribution. The performance of these two distributions was compared in two data sets (data set A and B), which represented L. monocytogenes prevalence and concentrations in cooked meat products. In this study, L. monocytogenes contamination data consisted of 4.23% (8/189) and 4.17% (5/120) non-zero counts for data set A and B, respectively. The contamination level of L. monocytogenes, determined by the most probable number (MPN) technique, ranged from 3 to 93 MPN/g among 13 positive samples. The goodness-of-fit test indicated that the ZIPL distribution was better than the simpler ZIP distribution, when L. monocytogenes contamination levels on positive cooked meat samples illustrated large heterogeneity. Results obtained from ZIPL distribution showed that the logarithmic mean value of L. monocytogenes positive samples was 1.5 log MPN/g (log σ = 0.4) for data set A and B. This study provides an alternative probabilistic method when only qualitative data is available in Quantitative microbial risk assessment (QMRA), in particular if pathogen concentrations consist of large numbers of zero counts and represent high variability.

Microbiological criteria: Principles for their establishment and application in food quality and safety

Legislation on food safety has led towards the standardization of food productions which, together with the existing quality certifications, aim to increase the level of protection of public health. It is recognized the need for the agri-food industry to have tools to harmonize their productions and to adequately manage their quality systems in order to improve consumers' confidence. The implementation of microbiological criteria is focused on facilitating this harmonization by enabling the discrimination of defective lots and acting as control tools at industrial level. Therefore, knowledge of the principles, components and factors influencing the efficiency of microbiological criteria may be helpful to better understand the consequences of their application. In the present study the main principles, methodologies and applications of microbiological criteria in foods are addressed for their implementation as a part of the management quality systems of agrifood industries. In addition, potential limitations and impact of microbiological criteria on food safety are discussed. Finally, an assessment of the performance of microbiological criteria at EU level in berries is described for the compliance of the socalled risk-based metrics, namely Performance Objectives and Food Safety Objectives.

Multifactorial effects of ambient temperature, precipitation, farm management, and environmental factors determine the level of generic Escherichia coli contamination on preharvested spinach

A repeated cross-sectional study was conducted to identify farm management, environment, weather, and landscape factors that predict the count of generic Escherichia coli on spinach at the preharvest level. E. coli was enumerated for 955 spinach samples collected on 12 farms in Texas and Colorado between 2010 and 2012. Farm management and environmental characteristics were surveyed using a questionnaire. Weather and landscape data were obtained from National Resources Information databases. A two-part mixed-effect negative binomial hurdle model, consisting of a logistic and zero-truncated negative binomial part with farm and date as random effects, was used to identify factors affecting E. coli counts on spinach. Results indicated that the odds of a contamination event (non-zero versus zero counts) vary by state (odds ratio [OR] = 108.1). Odds of contamination decreased with implementation of hygiene practices (OR = 0.06) and increased with an increasing average precipitation amount (mm) in the past 29 days (OR = 3.5) and the application of manure (OR = 52.2). On contaminated spinach, E. coli counts increased with the average precipitation amount over the past 29 days. The relationship between E. coli count and the average maximum daily temperature over the 9 days prior to sampling followed a quadratic function with the highest bacterial count at around 24°C. These findings indicate that the odds of a contamination event in spinach are determined by farm management, environment, and weather factors. However, once the contamination event has occurred, the count of E. coli on spinach is determined by weather only.