Ursula Gonzales-Barron

Count data distributions and their zero-modified equivalents as a framework for modelling microbial data with a relatively high occurrence of zero counts.

In many cases, microbial data are characterised by a relatively high proportion of zero counts, as occurs with some hygiene indicators and pathogens, which complicates the statistical treatment under the assumption of log normality. The objective of this work was to introduce an alternative Poisson-based distribution framework capable of representing this kind of data without incurring loss of information. The negative binomial, and two zero-modified parameterizations of the Poisson and negative binomial distributions (zero-inflated and hurdle) were fitted to actual zero-inflated bacterial data consisting of total coliforms (n=590) and Escherichia coli (n=677) present on beef carcasses sampled from nine Irish abattoirs. Improvement over the simple Poisson was shown by the simple negative binomial (p=0.426 for chi(2) test for the coliforms data) due to the added heterogeneity parameter, although it slightly overestimated the zero counts and underestimated the first few positive counts for both data sets. Whereas, the zero-modified Poisson could not cope with the data over-dispersion in any of its parameterizations (p<0.001 for chi(2) tests), the parameterizations of the zero-modified negative binomial presented differences in fit due to approximation errors. While the zero-inflated negative binomial parameterization was apparently reduced to a negative binomial due to a non-convergence of the logit parameter estimate, the goodness of fit of the hurdle negative binomial parameterization indicated that for the data sets under evaluation (coliforms data with approximately 13% zero counts and E.coli data with approximately 42% zero counts), the zero-modified negative binomial distribution was comparable to the simpler negative binomial distribution. Thus, bacterial data consisting of a considerable number of zero counts can be appropriately represented by using such count distributions, and this work serves as the starting point for an alternative statistical treatment of this kind of data and stochastic risk assessment modelling.

Tracking the Salmonella status of pigs and pork from lairage through the slaughter process in the Republic of Ireland.

Salmonella Typhimurium is the predominant serotype isolated from humans in Europe. Pork and pork products are recognized vehicles of Salmonella and are responsible for outbreaks of human salmonellosis. Pigs can become infected with Salmonella on the breeding or fattening farm and during transport, lairage, and slaughter. The aim of this study was to investigate selected points of Salmonella contamination from the time pigs entered the lairage to the time the carcass was processed in the boning hall and to determine the importance of different sources of Salmonella along the Irish pork production chain. A second objective was to evaluate whether the serological status or category of a herd influenced the levels of bacteriological contamination detected on individual carcasses and pork cuts during slaughter and dressing operations. All samples were tested for the presence and numbers of Salmonella. Enterobacteriaceae numbers were also determined. Serotype, phage type, and pulsed-field gel electrophoresis were utilized to determine similarity among Salmonella isolates. Lairage was a major source of cross-contamination with Salmonella as were the hands of evisceration operatives, conveyor belts, and equipment in the boning hall. Cross-contamination within the slaughter plant environment accounted for up to 69 % of Salmonella carcass contamination. In general, herd category reflected the bacteriological status of carcasses and pork cuts. Major findings were a strong association (P < 0.01) between Enterobacteriaceae counts and Salmonella occurrence on prechill carcasses and a significant association (P < 0.05) between Enterobacteriaceae counts and Salmonella occurrence on pork cut samples.

Prevalence and numbers of Salmonella spp. and Enterobacteriaceae on pork cuts in abattoirs in the Republic of Ireland

Aims:  This study aimed to determine the numbers and types of Salmonella spp. and Enterobacteriaceae on pork cuts in the meat cutting room environment of four commercial pork abattoirs in the Republic of Ireland.

The use of meta-analytical tools in risk assessment for food safety

This communication deals with the use of meta-analysis as a valuable tool for the synthesis of food safety research, and in quantitative risk assessment modelling. A common methodology for the conduction of meta-analysis (i.e., systematic review and data extraction, parameterisation of effect size, estimation of overall effect size, assessment of heterogeneity, and presentation of results) is explained by reviewing two meta-analyses derived from separate sets of primary studies of Salmonella in pork. Integrating different primary studies, the first meta-analysis elucidated for the first time a relationship between the proportion of Salmonella-carrier slaughter pigs entering the slaughter lines and the resulting proportion of contaminated carcasses at the point of evisceration; finding that the individual studies on their own could not reveal. On the other hand, the second application showed that meta-analysis can be used to estimate the overall effect of a critical process stage (chilling) on the incidence of the pathogen under study. The derivation of a relationship between variables and a probabilistic distribution is illustrations of the valuable quantitative information synthesised by the meta-analytical tools, which can be incorporated in risk assessment modelling. Strengths and weaknesses of meta-analysis within the context of food safety are also discussed.

Predictive thermal inactivation model for the combined effect of temperature, cinnamaldehyde and carvacrol on starvation-stressed multiple Salmonella serotypes in ground chicken ☆

We investigated the combined effect of three internal temperatures (60, 65 and 71.1 °C) and four concentrations (0.0, 0.1, 0.5 and 1% vol/wt) of two natural antimicrobials on the heat resistance of an eight-strain cocktail of Salmonella serovars in chicken meat. A complete factorial design (3×4×4) was used to assess the effects and interactions of heating temperature and the two antimicrobials, carvacrol and cinnamaldehyde. The 48 variable combinations were replicated to provide a total of 96 survivor curves from the experimental data. Mathematical models were then developed to quantify the combined effect of these parameters on heat resistance of starved Salmonella cells. The theoretical analysis shows that the addition of plant-derived antimicrobials overcomes the heat resistance of starvation-stressed Salmonella in ground chicken meat. The influence of the antimicrobials allows reduced heat treatments, thus reducing heat-induced damage to the nutritional quality of ground-chicken products. Although the reported omnibus log-linear model with tail and the omnibus sigmoid model could represent the experimental survivor curves, their discrepancy only became apparent in the present study when lethality times (D-values and t7.0) from each of the models were calculated. Given the concave nature of the inactivation curves, the log-linear model with tail greatly underestimates the times needed to obtain 7.0 log lethality. Thus, a polynomial secondary model, based on the sigmoid model, was developed to accurately predict the 7.0-log reduction times. The three-factor predictive model can be used to estimate the processing times and temperatures required to achieve specific log reductions, including the regulatory recommendation of 7.0-log reduction of Salmonella in ground chicken.

Using Muzzle Pattern Recognition as a Biometric Approach for Cattle Identification

Arising from the current need for positive identification for cattle traceability, the objective of this work was to investigate the feasibility of using muzzle pattern as a biometric-based identifier for cattle by acquiring muzzle patterns through lifted ink prints and through digital images. A three-stage matching algorithm was evaluated for scanned muzzle ink prints and performed successfully in all cases. Digital imaging of muzzles was far simpler than the ink print lifting method. For these digital images, the techniques of principal component analysis and Euclidean distance classifier were used. The algorithm training was performed independently on a different number of normalized muzzle images from 29 cattle (sets of 2, 4, 6, 8, and 10 training images per animal). The performance of this technique was assessed on a separate set of images (3 normalized muzzle images per animal). Results showed that when using 230 eigenvectors (out of 290), the recognition rate was 98.85%, and that additional eigenvectors did not improve the recognition rate. As expected, fewer principal components (less than 230) reduced the recognition rate, while a higher number of training images per animal improved it. Although the results have demonstrated the potential of muzzle pattern recognition as a non-invasive, inexpensive, and accurate biometric identifier of cattle, further research towards automation is necessitated.

A preliminary investigation on face recognition as a biometric identifier of sheep

The suitability of face recognition was investigated as a biometric-based identifier for sheep using a holistic analysis of face images by the independent components technique. Algorithm training was performed independently on several normalized face images from 50 sheep (sets of two, three, and four training images per sheep). The performance of this technique was assessed on a separate set of images (three normalized face images per sheep) using the cosine distance classifier. When 180 to 200 components were extracted, the recognition rate was as high as 95.3% to 96%. As expected, fewer independent components reduced the recognition rate, while a higher number of training images per sheep improved it. Although our results have demonstrated the potential of face recognition as a non-invasive, inexpensive, and accurate novel biometric identifier of sheep, further work should aim at improving recognition rates on a larger set of sheep faces.

Meta-analysis of the Effects of Sanitizing Treatments on Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes Inactivation in Fresh Produce

ABSTRACT The aim of this study was to perform a meta-analysis of the effects of sanitizing treatments of fresh produce on Salmonella spp., Escherichia coli O157:H7, and Listeria monocytogenes. From 55 primary studies found to report on such effects, 40 were selected based on specific criteria, leading to more than 1,000 data on mean log reductions of these three bacterial pathogens impairing the safety of fresh produce. Data were partitioned to build three meta-analytical models that could allow the assessment of differences in mean log reductions among pathogens, fresh produce, and sanitizers. Moderating variables assessed in the meta-analytical models included type of fresh produce, type of sanitizer, concentration, and treatment time and temperature. Further, a proposal was done to classify the sanitizers according to bactericidal efficacy by means of a meta-analytical dendrogram. The results indicated that both time and temperature significantly affected the mean log reductions of the sanitizing treatment (P < 0.0001). In general, sanitizer treatments led to lower mean log reductions when applied to leafy greens (for example, 0.68 log reductions [0.00 to 1.37] achieved in lettuce) compared to other, nonleafy vegetables (for example, 3.04 mean log reductions [2.32 to 3.76] obtained for carrots). Among the pathogens, E. coli O157:H7 was more resistant to ozone (1.6 mean log reductions), while L. monocytogenes and Salmonella presented high resistance to organic acids, such as citric acid, acetic acid, and lactic acid (∼3.0 mean log reductions). With regard to the sanitizers, it has been found that slightly acidic electrolyzed water, acidified sodium chlorite, and the gaseous chlorine dioxide clustered together, indicating that they possessed the strongest bactericidal effect. The results reported seem to be an important achievement for advancing the global understanding of the effectiveness of sanitizers for microbial safety of fresh produce.

Meta-analysis on the effect of interventions used in cattle processing plants to reduce Escherichia coli contamination

Cattle coming from feedlots to slaughter often harbor pathogenic E. coli that can contaminate final meat products. As a result, reducing pathogenic contamination during processing is a main priority. Unfortunately, food safety specialists face challenges when trying to determine optimal intervention strategies from published literature. Plant intervention literature results and methods vary significantly, making it difficult to implement interventions with any degree of certainty in their effectiveness. To create a more robust understanding of plant intervention effectiveness, a formal systematic literature review and meta-analysis was conducted on popular intervention methods. Effect size or intervention effectiveness was measured as raw log reduction, and modeled using study characteristics, such as intervention type, temperature of application, initial microbial concentration, etc. Least-squares means were calculated for intervention effectiveness separately on hide and on carcass surfaces. Heterogeneity between studies (I2) was assessed and factors influencing intervention effectiveness were identified. Least-squares mean reductions (log CFU/cm2) on carcass surfaces (n=249) were 1.44 [95% CI: 0.73-2.15] for acetic acid, 2.07 [1.48-2.65] for lactic acid, 3.09 [2.46-3.73] for steam vacuum, and 1.90 [1.33-2.47] for water wash. On hide surfaces (n=47), least-squares mean reductions were 2.21 [1.36-3.05] for acetic acid, 3.02 [2.16-3.88] for lactic acid, 3.66 [2.60-4.72] for sodium hydroxide, and 0.08 [-0.94-1.11] for water wash. Meta-regressions showed that initial microbial concentrations and timing of extra water washes were the most important predictors of intervention effectiveness. Unexplained variation remained high in carcass, hide, and lactic acid meta-regressions, suggesting that other significant moderators are yet to be identified. The results will allow plant managers and risk assessors to evaluate plant interventions, variation, and factors more effectively.

An exposure assessment model of the prevalence of Salmonella spp. along the processing stages of Brazilian beef

Beef cattle carrying Salmonella spp. represents a risk for contamination of meat and meat products. This study aimed to build an exposure assessment model elucidating the changes in Salmonella prevalence in Brazilian beef along the processing stages. To this effect, the results of a number of published studies reporting Salmonella incidences were assembled in order to model conversion factors based on beta distributions representing the effect of every production stage on the Salmonella incidence on beef carcasses. A random-effects meta-analysis modelled the hide-to-carcass transfer of Salmonella contamination. The Monte Carlo simulation estimated the Salmonella prevalence in beef cuts from processing plants to be ∼6.1% (95% CI: 1.4–17.7%), which was in reasonable agreement with a pool (n = 105) of surveys’ data of Salmonella in Brazilian beef cuts (mean 4.9%; 95% CI: 1.8–11.5%) carried out in commercial establishments. The results not only underscored the significant increase in Salmonella prevalence that can occur during evisceration/splitting and boning but also reinforced that, when hygienic slaughter procedures are properly implemented, the load of Salmonella can be reduced at dehiding, rinsing and chilling. As the model was based on a systematic review and meta-analysis, it synthesised all available knowledge on the incidence of Salmonella in Brazilian beef.