Food quality


Predictive modelling

Behavior of Listeria

Statistical process control


Shelf-life studies

Cardinal Parameters

Bayesian analysis

Microbiological criteria


Essential Oils


In order to design effective public health strategies, and, in particular, effective food safety interventions to reduce the burden of foodborne disease, the most important sources of enteric illnesses should be identified. Both case-control and cohort observational studies have for long been powerful approaches among epidemiologists to investigate the association of exposure and illness. In the literature, there are numerous case-control and cohort studies reporting results on risk factors and routes of transmission of sporadic foodborne infections. The objective of this article is to describe, in depth, the strategies implemented for systematic review and meta-analysis of the associations between multiple risk factors and eleven food and waterborne diseases, namely, non-typhoidal salmonellosis, campylobacteriosis, Shiga-toxin E. coli infection, listeriosis, yersiniosis, toxoplasmosis, norovirus infection, hepatitis A, hepatitis E, cryptosporidiosis and giardiasis. First, this article describes the procedures of systematic searches in five bibliographic engines, screening of relevance and assessment of methodological quality according to pre-set criteria. It proceeds with the explanation of a broad data categorisation scheme established to hierarchically group the risk factors into travel, host-specific factors and pathways of exposure (i.e., person-to-person, animal, environment and food routes), with views to harmonising and supporting the integration of outcomes from studies investigating a variety of potential determinants of disease. Next, the article describes the four meta-analysis models that were devised in order to calculate: (i) overall odds-ratios of acquiring the disease due to a specific risk factor by geographical region; (ii) overall odds-ratios of acquiring the disease from the different risk factors; (iii) overall risks of disease from consumption of ready-to-eat and barbecued foods; and (iv) overall effects of food handling (i.e., consuming food in raw, undercooked or unwashed state) and food preparation setting (i.e., eating food prepared outside the home) on risk of disease. The procedures for sensitivity analysis and removal of any influential and potentially-biased odds-ratio; and two methods for publication bias assessment are outlined. Finally, details are given on deviations from the standard risk categorisation scheme for specific foodborne hazards.

This study compares dynamic tertiary and competition models for L. monocytogenes growth as a function of intrinsic properties of a traditional Brazilian soft cheese and the inhibitory effect of lactic acid bacteria (LAB) during refrigerated storage. Cheeses were prepared from raw or pasteurized milk with or without the addition of selected LAB with known anti-listerial activity. Cheeses were analyzed for LAB and L. monocytogenes counts, pH and water activity (aw) throughout cold storage. Two approaches were used to describe the effect of LAB on L. monocytogenes: a Huang-Cardinal model that considers the effect of pH and aw variation in a dynamic kinetic analysis framework; and microbial competition models, including Lotka-Volterra and Jameson-effect variants, describing the simultaneous growth of L. monocytogenes and LAB. The Jameson-effect with γ and the Lotka-Volterra models produced models with statistically significant coefficients that characterized the inhibitory effect of selected LAB on L. monocytogenes in Minas fresh cheese. The Huang-Cardinal model [pH] outperformed both competition models. Taking aw change into account did not improve the fit quality of the Huang-Cardinal [pH] model. These models for Minas soft cheese should be valuable for future microbial risk assessments for this culturally important traditional cheese.

Previous research showed that meat of optimal tenderness is produced when rigor mortis temperature falls between 12-35 °C. This study aimed to classify beef carcasses quality according to the ideal window rule using pH/temperature decay descriptors and animal characteristics. Seventy-four Mirandesa breed and 52 Crossbreds, with an average age of 10.1 ± 2.32 months, were slaughtered at one abattoir located in the Northeast of Portugal. Carcass temperature and pH, logged during 24 h post-mortem, were modelled by exponential decay equations that estimated temperature (kT) and pH (kpH) decay rates. Additionally, other pH/temperature descriptors were estimated from the fitted models. From linear models adjusted to each descriptor, it was found that hot carcass weight, age, breed, gender, age class, fat cover, conformation and transport and lairage time had influence (P < 0.05) on pH and temperature decay rates. Thus, combining the variables kT and kpH, and selected animal/carcass characteristics as linear predictors, a system to classify quality of carcasses was developed.

This review compiles published information concerning the incidence of pathogenic microorganisms — Listeria monocytogenes, Salmonella spp., Staphylococcus aureus and shiga-toxin producing Escherichia coli (STEC) — in goat and sheep raw milk and cheese. Meta-analytical data were extracted from 37 primary studies undertaken in Australia, Brazil, China, Colombia, Costa Rica, Czech Republic, Egypt, Germany, Greece, Iran, Italy, Malaysia, Mexico, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, UK and USA. Pooled frequencies of detection of pathogens were found to be similar for sheep and goat raw milk: Salmonella (1.4–2.4%), L. monocytogenes (2.9–3.6%), STEC (4.3–4.8%) and S. aureus (35–39%). Likewise, in goat cheeses, regardless of being made of raw or heat-treated milk, S. aureus has been the most frequent contaminant (16.0%), whereas in raw milk cheeses, regardless of origin, the pooled prevalence of S. aureus is equally high in hard (34.6%) and soft cheeses (25.7%). L. monocytogenes is another important pathogen in sheep and goat milk cheeses (3.6–12.8%) while E. coli O157 strains with virulence genes (4.3%) also appear to persist during cheese manufacture. As expected, STEC has a higher pooled incidence in raw milk cheeses (10.0%) than in pasteurised milk cheeses (4.7%). Thus, the moderate contamination in raw milk and cheese of sheep and goat origin, revealed by this meta-analysis, advocates the reinforcement of general prevention measures such as close monitoring of hygiene on farms and eradication of disease by sheep and goat dairy farmers. Moreover, for the production of traditional cheeses made of raw milk, preventive measures during processing, namely, regular sterilisation of dairy equipment, process monitoring and hygiene of operators, should be even more stringent.


Using the data set “gomp1.csv”, find the parameters of the reparameterised Gompertz model. \[\begin{equation} y= y_0 + (y_{max} -y_0)*exp(-exp(k*(lag-x)/(y_{max}-y_0) + 1) ) \end{equation}\] Import the data set. dat <- read.csv("gomp1.csv", sep=";", header=T) plot(dat$Time, dat$logN) str(dat) ## 'data.frame': 13 obs. of 2 variables: ## $ Time: int 0 2 4 6 8 10 12 14 16 18 ... ## $ logN: num -0.105 0.108 -0.111 0.734 2.453 ... The next step is define the Gompertz function.

The data set “FirstOrder.csv” contains observations of microbial concentrations (log N) measured at different times (t) at a given environmental condition. Lets fit a first-order growth kinetics model \(log N = log N_0 + k \times t\) to the experimental data. Let’s import the “FirstOrder.csv” dataset, and observe the first five lines. dat <- read.csv("FirstOrder.csv", sep=";", header=TRUE) dat ## Time N ## 1 0 37.298 ## 2 1 56.149 ## 3 2 81.


Modern food science is supported by reliable research outcomes depicted or confirmed by data modelling and probability science. Although different types of models can be developed in food science, their objectives mostly revolve around explaining or representing physical, chemical, or biological phenomena in foods or food processes and/or estimating meaningful parameters that are necessary for simulation, prediction, control applications, or optimization/intervention strategies.

O AgroStat 2021 reunirá investigadores, reconhecidos internacionalmente, e representantes da indústria agro-alimentar para discutir a análise estatística de dados aplicada às ciências agro-alimentares, incluindo: Análise sensorial, Quimiometria, Desenho Experimental, Controlo de Processos, Microbiologia Preditiva e Análise de Risco, Meta-análise, Big Data e software. Está aberta a Submissão de resumos