Impact of maturation and growth temperature on cell-size distribution, heat-resistance, compatible solute composition and transcription profiles of Penicillium roqueforti conidia

Penicillium roqueforti is a major cause of fungal food spoilage. Its conidia are the main dispersal structures of this fungus and therefore the main cause of food contamination. These stress resistant asexual spores can be killed by preservation methods such as heat treatment. Here, the effects of cultivation time and temperature on thermal resistance of P. roqueforti conidia were studied. To this end, cultures were grown for 3, 5, 7 and 10 days at 25 °C or for 7 days at 15, 25 and 30 °C. Conidia of 3- and 10-day-old cultures that had been grown at 25 °C had D56-values of 1.99 ± 0.15 min and 5.31 ± 1.04 min, respectively. The effect of cultivation temperature was most pronounced between P. roqueforti conidia cultured for 7 days at 15 °C and 30 °C, where D56-values of 1.12 ± 0.05 min and 4.19 ± 0.11 min were found, respectively. Notably, D56-values were not higher when increasing both cultivation time and temperature by growing for 10 days at 30 °C. A correlation was found between heat resistance of conidia and levels of trehalose and arabitol, while this was not found for glycerol, mannitol and erythritol. RNA-sequencing showed that the expression profiles of conidia of 3- to 10-day-old cultures that had been grown at 25 °C were distinct from conidia that had been formed at 15 °C and 30 °C for 7 days. Only 33 genes were upregulated at both prolonged incubation time and increased growth temperature. Their encoded proteins as well as trehalose and arabitol may form the core of heat resistance of P. roqueforti conidia.</p

Observations on quasi-uniform products

AbstractWe prove that any product of quotient maps in the category of quasi-uniform spaces and quasi-uniformly continuous maps is a quotient map. We also show that a quasi-uniformly continuous map from a product of quasi-uniform spaces into a quasi-pseudometric T0-space depends on countably many coordinates.Furthermore we characterize those quasi-uniformities that are unique in their quasi-proximity class and prove that this property is preserved under arbitrary products in the category of quasi-uniform spaces

Hazard control through processing and preservation technologies for enhancing the food safety management of infant food chains

Food safety of infant foods is of paramount importance due to the high vulnerability of this population. Food business operators guarantee safety of the products they put on the market by implementing control measures that prevent, eliminate, reduce, or keep relevant physical, microbiological and/or chemical hazards to an acceptable level. It is essential that the efficacy of control measures is validated during process design and on-line monitoring and periodic verification activities are implemented during the commercial production. Infant foods are usually processed through conservative thermal treatments that guarantee food safety but usually negatively affect the organoleptic properties, reduce vitamin and nutrient contents. Heat treatments can trigger the formation of process induced contaminants.The EU-SAFFI project aims to set and validate new/emerging processing and preservation technologies (i.e. pulse combustion drying, radiofrequency and high pressure processing) to control key contaminants and pathogens as efficiently as classical technologies and to provide a decision support system to manage food safety in infant food. This article describes how the project is addressing the research to control (i) furan, a key process-induced toxicant in infant food whose formation is induced during thermal preservation processes of foods such as infant formulas and jarred baby foods, (ii) tropane alkaloids, natural contaminants found in agricultural crops due to accidental harvesting of weeds whose presence above the maximum regulated levels have been documented in cereal-based foods for infants and children and (iii) different vegetative and spore forming bacterial pathogens, a group of microbiological hazards with product and technology-specific relevance and resistance.info:eu-repo/semantics/publishedVersio

Intraspecific variability in heat resistance of fungal conidia

Microbial species are inherently variable, which is reflected in intraspecies genotypic and phenotypic differences. Strain-to-strain variation gives rise to variability in stress resistance and plays a crucial role in food safety and food quality. Here, strain variability in heat resistance of asexual spores (conidia) of the fungal species Aspergillus niger, Penicillium roqueforti and Paecilomyces variotii was quantified and compared to bacterial variability found in the literature. After heat treatment, a 5.4- to 8.6-fold difference in inactivation rate was found between individual strains within each species, while the strain variability of the three fungal species was not statistically different. We evaluated whether the degree of intraspecies variability is uniform, not only within the fungal kingdom, but also amongst different bacterial species. Comparison with three spore-forming bacteria and two non-spore-forming bacteria revealed that the variability of the different species was indeed in the same order of magnitude, which hints to a microbial signature of variation that exceeds kingdom boundaries.Microbial Biotechnolog

Microbial testing in food safety : Effect of specificity and sensitivity on sampling plans - how does the OC curve move

Obviously the quality of test methods does have an impact on the performance of sampling plans. A low sensitivity means that actual positive samples are tested negative (i.e. false negatives) and moves the Operating Characteristic (OC) curve to the right. Values of sensitivity above 0.70 do not show large effects on the position of the OC curve. A low specificity means that actual negative samples are tested positive (i.e. false positives) and moves the OC curve to the left. The effect of the specificity of the testing method has a very large effect on the performance of a sampling plan, especially for sampling plans with higher numbers of samples. In those cases specificity should be much larger than 0.99 to have a reasonable performance

Multilevel modelling as a tool to include variability and uncertainty in quantitative microbiology and risk assessment. Thermal inactivation of Listeria monocytogenes as proof of concept

Variability is inherent in biology and also substantial for microbial populations. In the context of food safety risk assessment, it refers to differences in the response of different bacterial strains (between-strain variability) and different cells (within-strain variability) to the same condition (e.g. inactivation treatment). However, its quantification based on empirical observations and its incorporation in predictive models is a challenge for both experimental design and (statistical) analysis. In this article we propose the use of multilevel models to quantify (different levels of) variability and uncertainty and include them in the predictions. As proof of concept, we analyse the microbial inactivation of Listeria monocytogenes to thermal treatments including different levels of variability (between-strain and within-strain) and uncertainty. The relationship between the microbial count and time was expressed using a (non-linear) Weibullian model. Moreover, we defined stochastic hypotheses to describe the different types of variation at the level of the kinetic parameters, as well as in the observations (microbial counts). The model parameters (kinetic parameters and variances) are estimated using Bayesian statistics. The multilevel approach was compared against an analogous, single-level model. The multilevel methodology shrinks extreme parameter estimates towards the mean according to uncertainty, thus mitigating overfitting. In addition, this approach enables to easily incorporate different levels of variation (between-strain and/or within-strain variability and/or uncertainty) in the predictions. On the other hand, multilevel (Bayesian) models are more complex to define, implement, analyse and communicate than single-level models. Nevertheless, their ability to incorporate different sources of variability in predictions make them very suitable for Quantitative Microbial Risk Assessment

Glycerol metabolism induces Listeria monocytogenes biofilm formation at the air-liquid interface

Listeria monocytogenes is a food-borne pathogen that can grow as a biofilm on surfaces. Biofilm formation in food-processing environments is a big concern for food safety, as it can cause product contamination through the food-processing line. Although motile aerobic bacteria have been described to form biofilms at the air-liquid interface of cell cultures, to our knowledge, this type of biofilm has not been described in L. monocytogenes before. In this study we report L. monocytogenes biofilm formation at the air-liquid interface of aerobically grown cultures, and that this phenotype is specifically induced when the media is supplemented with glycerol as a carbon and energy source. Planktonic growth, metabolic activity assays and HPLC measurements of glycerol consumption over time showed that glycerol utilization in L. monocytogenes is restricted to growth under aerobic conditions. Gene expression analysis showed that genes encoding the glycerol transporter GlpF, the glycerol kinase GlpK and the glycerol 3-phosphate dehydrogenase GlpD were upregulated in the presence of oxygen, and downregulated in absence of oxygen. Additionally, motility assays revealed the induction of aerotaxis in the presence of glycerol. Our results demonstrate that the formation of biofilms at the air-liquid interface is dependent on glycerol-induced aerotaxis towards the surface of the culture, where L. monocytogenes has access to higher concentrations of oxygen, and is therefore able to utilize this compound as a carbon source

Incorporating strain variability in the design of heat treatments : A stochastic approach and a kinetic approach

For the design of thermal processes, the decimal reduction times (D-values) of target organisms can be used. However, many factors influence the D-value, like inherent organism's characteristics (strain variability), the effect of the history of the cells, as well as product factors and process factors. Strain variability is a very large contributor to the overall variation of the D-value. Hence, the overall reduction of microbial contaminants by a heat treatment is a combination of the occurrence of a strain with a certain heat resistance and its reduction given the prevailing conditions. This reduction can be determined using two approaches: a kinetic analysis based on integral equations or a stochastic approach based on Monte Carlo analysis. In this article, these two approaches are compared using as case studies the inactivation of two microorganisms: Listeria monocytogenes in a pasteurization process and the sporeformer Geobacillus stearothermophilus in a UHT process. Both approaches resulted in similar conclusions, highlighting that the strains with the highest heat resistance are determinant for the overall inactivation, even if the probability of cells having such extreme heat resistance is very low.</p

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values. We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression