Effect of storage conditions in the response of Listeria monocytogenes in a fresh purple vegetable smoothie compared with an acidified TSB medium

Application of High Hydrostatic Pressure in fresh purple smoothie: Microbial inactivation kinetic modelling and qualitative studies

The inactivation kinetics of Listeria monocytogenes during High Hydrostatic Pressure (HHP) treatments was studied in a purple smoothie based of fresh fruit and vegetables. Pressure intensity studied was 300, 350, 400 and 450 MPa. Untreated samples were used as control. Furthermore, the effects on quality attributes (sensory, total soluble solids content, colour, titratable acidity, pH, vitamin C and total phenolics content) were also monitored. Microbial inactivation was modelled as a function of the HHP intensity using the Geeraerd model. Shoulder and tail effects were observed only for the 300 MPa pressure assayed, supporting a multiple hit kinetic inactivation of critical factors. Increasing the HHP intensity resulted in a faster inactivation with tailing. A strong positive correlation was observed between the pressure level and the inactivation rate (k). Hence, a linear model was used to describe the relationship between both variables. Nevertheless, further data are required to confirm this secondary model. Quality was mostly unaffected by the HHP treatments, except for the vitamin C content, which reported reductions of 26 and 21% after 300 and 350 MPa, respectively. In conclusion, HHP can be a viable technology for processing fruit and vegetable-based smoothies to preserve quality and safety. A pressure of 400 MPa is advisable to ensure an efficient microbial inactivation with the best sensory and nutritional quality retention.

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions

The incidence of frequent foodborne disease outbreaks due to Staphylococcus aureus contamination necessitates the urgent searching for effective methods to monitor S. aureus. This research aims to construct model with a dynamic survival curve and some static growth curves to predict the behavior of S. aureus in raw pork. Lack of research about S. aureus kinetics in pork under fluctuating temperature conditions across freezing and thawing necessitates this study. One-step analysis was used to determine the model parameters, which was more efficient than conventional model analysis with two steps. The results of kinetic analysis showed that Tmin (minimum growth temperature) was 6.85°C, which is close to the estimated values in previous reports. Subsequently, validation results indicated the integrated model can accurately predict the behavior of S. aureus regardless of isothermal or nonisothermal conditions with the root-mean-square errors (RMSE < 0.44 log CFU/g, 73.9% of the errors of prediction falls within ±0.5 log CFU/g), accuracy factors Af and bias factors Bf were both close to 1. This work may offer an effective method for the assessment of microbial security related to S. aureus in pork.

Assessing the pressure resistance of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella enterica to high pressure processing (HPP) in citric acid model solutions for process validation

Despite the commercial success of high pressure processing (HPP) in the juice industry, some regulatory agencies still require process validation. However, there is a lack of consensus on various aspects regarding validation protocols, including the selection of representative strains to be used in challenge tests. This study characterized the variable response of Escherichia coli O157:H7 (34 strains), Listeria monocytogenes (44 strains) and Salmonella enterica (45 strains) to HPP, and identified potential candidates to use in process validation. Stationary phase cells were submitted to 500 MPa for 1 min at 10 °C in model solutions consisting of tryptic soy broth + 0.6% yeast extract (TSBYE) adjusted to pH 4.5 and 6.0 with citric acid. At pH 6.0, pressure resistance widely varied between species and within strains of the same species. E. coli O157:H7 and L. monocytogenes were the most pressure resistant and showed high variability at strain level, as the total count range given by minimum and maximum counts spread between 2.0 and 6.5 log₁₀ CFU/ml. S. enterica was the least resistant pathogen with more than 82% of the isolates displaying non-detectable counts after HPP. Recovery through storage at 12 °C was also variable for all pathogens, but eventually most strains recovered with median counts on day 14 between 8.3 and 8.9 log₁₀ CFU/ml. For pH 4.5 solutions, 26 E. coli O157:H7 strains displayed survivors after HPP but did not adapt, registering non-detectable counts in the next sampling dates. None of the L. monocytogenes and S. enterica strains survived HPP or incubation at pH 4.5 (<2.0 log₁₀ CFU/ml), suggesting that citric acid at 4.16 g/l is a safe barrier for pathogen control under moderate HPP conditions. Principal component and cluster analyses served to propose strain cocktails for each species based on their pressure resistant and adaptation phenotypes. Additionally, S. enterica was identified as less pressure resistant and less prone to recover following HPP than E. coli O157:H7 and L. monocytogenes, so its relevance in process validation for juices should be questioned. Future work will validate the proposed strain cocktails on real food systems.

An outbreak of human listeriosis associated with frozen sweet corn consumption: Investigations in the UK

The use of Whole genome sequencing (WGS) identified a multi-country outbreak of human listeriosis associated with consumption of frozen sweet corn produced in Hungary. The purpose of this report was to summarise information on the cases occurring in the UK which were part of this outbreak and outline investigations on the presence of Listeria monocytogenes in the affected food chain. Prior to the international recall of this product in 2018, 12 UK cases of listeriosis were identified as infected by the outbreak strain between 2015 and 18. Epidemiological and microbiological investigations confirmed these cases as belonging to the outbreak. A further case occurred in 2019 and a contaminated frozen pack from one of the implicated batches of sweet corn was recovered from the patient's domestic freezer. The outbreak strain was also detected in products from a sandwich manufacturer in 2018 which added frozen sweet corn directly to sandwich fillings. The sandwich manufacturer's sweet corn was supplied by a distributor in England which obtained frozen products from the Hungarian manufacturer implicated in the outbreak. Within the distributor's premises, 208 food and environmental samples were taken: L. monocytogenes was detected in 44% of 70 samples of frozen sweet corn and 5% of 79 other foods. The outbreak strain was detected in the frozen sweet corn, in one other frozen food (mixed vegetables) and in the factory environment. The outbreak strain was also recovered from frozen beans on retail sale in the first four months of 2019. Five other L. monocytogenes strains together with two other Listeria species were detected in samples from the importer's premises. One of the L. monocytogenes strains in the importer's factory, which was distinct from the outbreak strain, was also recovered from sweet corn collected from the sandwich manufacturer, sweet corn tested in England in 2013 and 2016 and the blood of two cases of human listeriosis which occurred in England in 2014. This report shows how analysis by WGS provides evidence to understand complex food chains. This report also highlights risks for transmission of human listeriosis from frozen sweet corn and the potential for misuse of this food as a ready-to-eat product.

Growth Potential of Listeria monocytogenes on Refrigerated Spinach and Rocket Leaves in Modified Atmosphere Packaging

Minimally processed ready-to-eat (RTE) vegetables are increasingly consumed for their health benefits. However, they also pose a risk of being ingested with food-borne pathogens. The present study investigated the ability of RTE spinach and rocket to support the growth of Listeria monocytogenes as previous studies provided contradicting evidence. Findings were compared to growth on iceberg lettuce that has repeatedly been shown to support growth. Products were inoculated with a three-strain mix of L. monocytogenes at 10 and 100 cfu g⁻¹ and stored in modified atmosphere (4 kPa O₂, 8 kPa CO₂) at 8 °C over 7–9 days. Spinach demonstrated the highest growth potential rate of 2 to 3 log₁₀ cfu g⁻¹ over a 9-day period with only marginal deterioration in its visual appearance. Growth potential on rocket was around 2 log₁₀ cfu g⁻¹ over 9 days with considerable deterioration in visual appearance. Growth potential of iceberg lettuce was similar to that of rocket over a 7-day period. Growth curves fitted closely to a linear growth model, indicating none to limited restrictions of growth over the duration of storage. The high growth potentials of L. monocytogenes on spinach alongside the limited visual deterioration highlight the potential risks of consuming this raw RTE food product when contaminated.

Variability in the heat resistance of Listeria monocytogenes under dynamic conditions can be more relevant than that evidenced by isothermal treatments

Heterogeneity in the response of microbial cells to environmental conditions is inherent to every biological system and can be very relevant for food safety, potentially being as important as intrinsic and extrinsic factors. However, previous studies analyzing variability in the microbial response to thermal treatments were limited to data obtained under isothermal conditions, whereas in the reality, environmental conditions are dynamic. In this article we analyse both empirically and through mathematical modelling the variability in the microbial response to thermal treatments under isothermal and dynamic conditions. Heat resistance was studied for four strains of Listeria monocytogenes (Scott A, CECT 4031, CECT 4032 and 12MOB052), in three different matrices (buffered peptone water, pH 7 Mcllvaine buffer and semi-skimmed milk). Under isothermal conditions, between-strain and between-media variability had no impact in the heat resistance, whereas it was very relevant for dynamic conditions. Therefore, the differences observed under dynamic conditions can be attributed to the variability in the ability for developing stress acclimation. The highest acclimation was observed in strain CECT 4031 (10-fold increase of the D-value), while the lowest acclimation was observed in strain CECT 4032 (50% increase of the D-value). Concerning the different media, acclimation was higher in buffered peptone water and semi-skimmed milk than in Mcllvaine buffer of pH 7.0. To the knowledge of the authors, this is the first research work that specifically analyses the variability of microbial adaptation processes that take place under dynamic conditions. It highlights that microbial heat resistance under dynamic conditions are sometimes determined by mechanisms that cannot be observed when cells are treated in isothermal conditions (e.g. acclimation) and can also be affected by variability. Consequently, empirical evidence on variability gathered under isothermal conditions should be extrapolated with care for dynamic conditions.

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.

The public health risk posed by Listeria monocytogenes in frozen fruit and vegetables including herbs, blanched during processing

A multi-country outbreak of Listeria monocytogenes ST6 linked to blanched frozen vegetables (bfV) took place in the EU (2015-2018). Evidence of food-borne outbreaks shows that L. monocytogenes is the most relevant pathogen associated with bfV. The probability of illness per serving of uncooked bfV, for the elderly (65-74 years old) population, is up to 3,600 times greater than cooked bfV and very likely lower than any of the evaluated ready-to-eat food categories. The main factors affecting contamination and growth of L. monocytogenes in bfV during processing are the hygiene of the raw materials and process water; the hygienic conditions of the food processing environment (FPE); and the time/Temperature (t/T) combinations used for storage and processing (e.g. blanching, cooling). Relevant factors after processing are the intrinsic characteristics of the bfV, the t/T combinations used for thawing and storage and subsequent cooking conditions, unless eaten uncooked. Analysis of the possible control options suggests that application of a complete HACCP plan is either not possible or would not further enhance food safety. Instead, specific prerequisite programmes (PRP) and operational PRP activities should be applied such as cleaning and disinfection of the FPE, water control, t/T control and product information and consumer awareness. The occurrence of low levels of L. monocytogenes at the end of the production process (e.g. < 10 CFU/g) would be compatible with the limit of 100 CFU/g at the moment of consumption if any labelling recommendations are strictly followed (i.e. 24 h at 5°C). Under reasonably foreseeable conditions of use (i.e. 48 h at 12°C), L. monocytogenes levels need to be considerably lower (not detected in 25 g). Routine monitoring programmes for L. monocytogenes should be designed following a risk-based approach and regularly revised based on trend analysis, being FPE monitoring a key activity in the frozen vegetable industry.

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology.

Mathematical modelling of the stress resistance induced in Listeria monocytogenes during dynamic, mild heat treatments

Modelling of stress acclimation induced by thermal inactivation of Listeria monocytogenes under dynamic conditions is analyzed in this work. A mathematical model that separates the effect of the instantaneous temperature from the one of stress acclimation, was used. The model was trained using isothermal inactivation experiments, and one biphasic dynamic treatment with a heating rate of 1 °C/min and a holding phase of 60 °C. These experiments were performed in laboratory media (Tryptic Soy Broth; TSB). The model parameters estimated through these experiments (D₅₅=12.87±0.82min, z=4.58±0.04°C, a=0.11±0.01min^-1, E=0.50±0.01°C and c=1.23±0.03) were successfully used to predict the microbial inactivation for another seven inactivation profiles, with and without a holding phase. Moreover, similar experiments were performed using milk as heating media, obtaining a good agreement between the model predictions and the empirical observations. The results of this study are compatible with the hypothesis that L. monocytogenes is able to develop a physiological response during dynamic treatments that increases its thermal resistance. Also, that the model used can be used to predict microbial inactivation of this microorganism taking into consideration stress acclimation.