Food Microstructure and Fat Content Affect Growth Morphology, Growth Kinetics, and Preferred Phase for Cell Growth of Listeria monocytogenes in Fish-Based Model Systems

Unravelling the impact of fat content on the microbial dynamics and spatial distribution of foodborne bacteria in tri-phasic viscoelastic 3D models

The aim of the current study is to develop and characterise novel complex multi-phase in vitro 3D models, for advanced microbiological studies. More specifically, we enriched our previously developed bi-phasic polysaccharide (Xanthan Gum)/protein (Whey Protein) 3D model with a fat phase (Sunflower Oil) at various concentrations, i.e., 10%, 20%, 40% and 60% (v/v), for better mimicry of the structural and biochemical composition of real food products. Rheological, textural, and physicochemical analysis as well as advanced microscopy imaging (including spatial mapping of the fat droplet distribution) of the new tri-phasic 3D models revealed their similarity to industrial food products (especially cheese products). Furthermore, microbial growth experiments of foodborne bacteria, i.e., Listeria monocytogenes, Escherichia coli, Pseudomonas aeruginosa and Lactococcus lactis on the surface of the 3D models revealed very interesting results, regarding the growth dynamics and distribution of cells at colony level. More specifically, the size of the colonies formed on the surface of the 3D models, increased substantially for increasing fat concentrations, especially in mid- and late-exponential growth phases. Furthermore, colonies formed in proximity to fat were substantially larger as compared to the ones that were located far from the fat phase of the models. In terms of growth location, the majority of colonies were located on the protein/polysaccharide phase of the 3D models. All those differences at microscopic level, that can directly affect the bacterial response to decontamination treatments, were not captured by the macroscopic kinetics (growth dynamics), which were unaffected from changes in fat concentration. Our findings demonstrate the importance of developing structurally and biochemically complex 3D in vitro models (for closer proximity to industrial products), as well as the necessity of conducting multi-level microbial analyses, to better understand and predict the bacterial behaviour in relation to their biochemical and structural environment. Such studies in advanced 3D environments can assist a better/more accurate design of industrial antimicrobial processes, ultimately, improving food safety.

On the evaluation of the antimicrobial effect of grape seed extract and cold atmospheric plasma on the dynamics of Listeria monocytogenes in novel multiphase 3D viscoelastic models

The demand for products that are minimally processed and produced in a sustainable way, without the use of chemical preservatives or antibiotics have increased over the last years. Novel non-thermal technologies such as cold atmospheric plasma (CAP) and natural antimicrobials such as grape seed extract (GSE) are attractive alternatives to conventional food decontamination methods as they can meet the above demands. The aim of this study was to investigate the microbial inactivation potential of GSE, CAP (in this case, a remote air plasma with an ozone-dominated RONS output) and their combination against L. monocytogenes on five different 3D in vitro models of varying rheological, structural, and biochemical composition. More specifically, we studied the microbial dynamics, as affected by 1 % (w/v) GSE, CAP or their combination, in three monophasic Xanthan Gum (XG) based 3D models of relatively low viscosity (1.5 %, 2.5 % and 5 % w/v XG) and in a biphasic XG/Whey Protein (WPI) and a triphasic XG/WPI/fat model. A significant microbial inactivation (comparable to liquid broth) was achieved in presence of GSE on the surface of all monophasic models regardless of their viscosity. In contrast, the GSE antimicrobial effect was diminished in the multiphasic systems, resulting to only a slight disturbance of the microbial growth. In contrast, CAP showed better antimicrobial potential on the surface of the complex multiphasic models as compared to the monophasic models. When combined, in a hurdle approach, GSE/CAP showed promising microbial inactivation potential in all our 3D models, but less microbial inactivation in the structurally and biochemically complex multiphasic models, with respect to the monophasic models. The level of inactivation also depended on the duration of the exposure to GSE. Our results contribute towards understanding the antimicrobial efficacy of GSE, CAP and their combination as affected by robustly controlled changes of rheological and structural properties and of the biochemical composition of the environment in which bacteria grow. Therefore, our results contribute to the development of sustainable food safety strategies.

Spatially localised expression of the glutamate decarboxylase gadB in Escherichia coli O157:H7 microcolonies in hydrogel matrices

Functional diversity within isogenic spatially organised bacterial populations has been shown to trigger emergent community properties such as stress tolerance. Considering gadB gene encoding a key glutamate decarboxylase involved in E. coli tolerance to acidic conditions, we investigated its expression in hydrogels mimicking the texture of some structured food matrices (such as minced meat or soft cheese). Taking advantage of confocal laser scanning microscopy combined with a genetically-engineered dual fluorescent reporter system, it was possible to visualise the spatial patterns of bacterial gene expression from in-gel microcolonies. In E. coli O157:H7 microcolonies, gadB showed radically different expression patterns between neutral (pH 7) or acidic (pH 5) hydrogels. Differential spatial expression was determined in acidic hydrogels with a strong expression of gadB at the microcolony periphery. Strikingly, very similar spatial patterns of gadB expression were further observed for E. coli O157:H7 grown in the presence of L. lactis. Considering the ingestion of contaminated foodstuff, survival of E. coli O157:H7 to acidic stomachal stress (pH 2) was significantly increased for bacterial cells grown in microcolonies in acidic hydrogels compared to planktonic cells. These findings have significant implications for risk assessment and public health as they highlight inherent differences in bacterial physiology and virulence between liquid and structured food products. The contrasting characteristics observed underscore the need to consider the distinct challenges posed by these food types, thereby emphasising the importance of tailored risk mitigation strategies.

Mild Heat Treatment and Biopreservatives for Artisanal Raw Milk Cheeses: Reducing Microbial Spoilage and Extending Shelf-Life through Thermisation, Plant Extracts and Lactic Acid Bacteria

The microbial quality of raw milk artisanal cheeses is not always guaranteed due to the possible presence of pathogens in raw milk that can survive during manufacture and maturation. In this work, an overview of the existing information concerning lactic acid bacteria and plant extracts as antimicrobial agents is provided, as well as thermisation as a strategy to avoid pasteurisation and its negative impact on the sensory characteristics of artisanal cheeses. The mechanisms of antimicrobial action, advantages, limitations and, when applicable, relevant commercial applications are discussed. Plant extracts and lactic acid bacteria appear to be effective approaches to reduce microbial contamination in artisanal raw milk cheeses as a result of their constituents (for example, phenolic compounds in plant extracts), production of antimicrobial substances (such as organic acids and bacteriocins, in the case of lactic acid bacteria), or other mechanisms and their combinations. Thermisation was also confirmed as an effective heat inactivation strategy, causing the impairment of cellular structures and functions. This review also provides insight into the potential constraints of each of the approaches, hence pointing towards the direction of future research.

Variability in Cold Tolerance of Food and Clinical Listeria monocytogenes Isolates

The aim of this study was to investigate the level of strain variability amongst food and clinical Listeria monocytogenes isolates growing at low temperatures (4 and 7 °C) in both laboratory media and real food matrices. Isolates (n = 150) grown in laboratory media demonstrated a large variation in growth profiles measured using optical density. Overall, it was noted that clinical isolates exhibited a significantly higher growth rate (p ≤ 0.05) at 7 °C than the other isolates. Analysis of variance (ANOVA) tests of isolates grouped using Multi Locus Sequence Typing (MLST) revealed that clonal complex 18 (CC18) isolates were significantly (p ≤ 0.05) faster growing at 4 °C than other CC-type isolates while CC101, CC18, CC8, CC37 and CC14 were faster growing than other CC types at 7 °C. Euclidean distance and Ward method-based hierarchical clustering of mean growth rates classified 33.33% of isolates as faster growing. Fast and slow growing representative isolates were selected from the cluster analysis and growth rates were determined using plate count data in laboratory media and model food matrices. In agreement with the optical density experiments, CC18 isolates were faster and CC121 isolates were slower than other CC types in laboratory media, UHT milk and fish pie. The same trend was observed in chocolate milk but the differences were not statistically significant. Moreover, pan-genome analysis (Scoary) of isolate genome sequences only identified six genes of unknown function associated with increased cold tolerance while failing to identify any known cold tolerance genes. Overall, an association that was consistent in laboratory media and real food matrices was demonstrated between isolate CC type and increased cold tolerance.

Potential of multiparametric characterization of foodstuffs by nuclear magnetic resonance to better predict microbial behavior

Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (a_w ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10-20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.

Guidance on date marking and related food information: part 2 (food information)

A risk‐based approach was used to develop guidance to be followed by food business operators (FBOs) when deciding on food information relating to storage conditions and/or time limits for consumption after opening a food package and thawing of frozen foods. After opening the package, contamination may occur, introducing new pathogens into the food and the intrinsic (e.g. pH and a_w), extrinsic (e.g. temperature and gas atmosphere) and implicit (e.g. interactions with competing background microbiota) factors may change, affecting microbiological food safety. Setting a time limit for consumption after opening the package (secondary shelf‐life) is complex in view of the many influencing factors and information gaps. A decision tree (DT) was developed to assist FBOs in deciding whether the time limit for consumption after opening, due to safety reasons, is potentially shorter than the initial ‘best before’ or ‘use by’ date of the product in its unopened package. For products where opening the package leads to a change of the type of pathogenic microorganisms present in the food and/or factors increasing their growth compared to the unopened product, a shorter time limit for consumption after opening would be appropriate. Freezing prevents the growth of pathogens, however, most pathogenic microorganisms may survive frozen storage, recover during thawing and then grow and/or produce toxins in the food, if conditions are favourable. Moreover, additional contamination may occur from hands, contact surfaces or contamination from other foods and utensils. Good practices for thawing should, from a food safety point of view, minimise growth of and contamination by pathogens between the food being thawed and other foods and/or contact surfaces, especially when removing the food from the package during thawing. Best practices for thawing foods are presented to support FBOs.

A microscopy-based approach for determining growth probability and lag time of individual bacterial cells

The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well. Yet, these methods are labour intensive and would benefit from higher throughput. In this study, we developed a quantitative experimental method using automated microscopy to determine the single-cell growth probability and lag time. The developed method consists of the use of direct cell observation with phase-contrast microscopy equipped with a 100× objective and a high-resolution device camera. The method is not a time-lapse method but is based on the observation of high numbers of colonies for a given time. Automation of image acquisition and image analysis was used to reach a high throughput. The single-cell growth probabilities and lag times of four strains of Listeria monocytogenes were determined at 4 °C. The microscopic method was shown to be a promising method for the determination of individual lag times and growth probability at the single-cell level.

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.

Confocal Laser Microscopy Analysis of Listeria monocytogenes Biofilms and Spatially Organized Communities

The behavior of Listeria monocytogenes communities in the food chain is closely associated with their spatial organization. Whether as biofilms on industrial surfaces or as microcolonies in food matrices, the resulting physiological diversification combined with the presence of extracellular polymeric substances (EPS) triggers emergent community functions involved in the pathogen survival and persistence (e.g., tolerance to dehydration, biocides, or preservatives). In this contribution, we present a noninvasive confocal laser microscopy (CLM) protocol allowing exploration of the spatial organization of L. monocytogenes communities on various inert or nutritive materials relevant for the food industry.

Limonene nanoemulsified with soya lecithin reduces the intensity of non-isothermal treatments for inactivation of Listeria monocytogenes

Consumers' demands for ready-to-eat, fresh-like products are on the rise during the last years. This type of products have minimal processing conditions that can enable the survival and replication of pathogenic microorganisms. Among them, Listeria monocytogenes is of special concern, due to its relatively high mortality rate and its ability to replicate under refrigeration conditions. Previous research works have shown that nanoemulsified essential oils in combination with thermal treatments are effective for inactivating L. monocytogenes. However, previous research works were limited to isothermal conditions, whereas actual processing conditions in industry are dynamic. Under dynamic conditions, microorganism can respond unexpectedly to the thermal stress (e.g. adaptation, acclimation or increased sensitivity). In this work, we assess the combination of nanoemulsified D-limonene with thermal treatments under isothermal and dynamic conditions. The nanoemulsion was prepared following an innovative methodology using soya lecithin, a natural compound as well as the essential oil. Under isothermal heating conditions, the addition of the antimicrobial enables a reduction of the treatment time by a factor of 25. For time-varying treatments, dynamic effects were relevant. Treatments with a high heating rate (20 °C/min) are more effective than those with a slow heating rate (1 °C/min). This investigation demonstrates that the addition of nanoemulsified D-limonene can greatly reduce the intensity of the thermal treatments currently applied in the food industry. Hence, it can improve the product quality without impacting its safety.

The Complex Effect of Food Matrix Fat Content on Thermal Inactivation of Listeria monocytogenes: Case Study in Emulsion and Gelled Emulsion Model Systems

Previous studies on the influence of food matrix fat content on thermal inactivation kinetics of food pathogens have shown contradictory results due to the combined influence of fat content and other factors such as composition. Therefore, thermal inactivation of Listeria monocytogenes at 59, 64, and 69°C was systematically investigated in emulsion and gelled emulsion food model systems with various fat content (1, 5, 10, and 20%), such that the effect of fat content was isolated. Thermal conductivity and rheological properties of the model systems were quantified, as well as the effect of these properties on the thermal load of the model systems. Thermal conductivity was complexly related to fat content, the nature of the food matrix (i.e., viscous or gelled), and temperature. For the emulsions, the consistency index K increased with increasing fat content, while the flow behavior index n followed the opposite trend. For the gelled emulsions, the storage modulus G' was always larger than the loss modulus G″ (i.e., measure of elastic and viscous properties, respectively). The phase angle δ [i.e., arctan (G″/G')] was proportional with fat content, but this relation became more complex at higher temperatures. The thermal load of the model systems was not largely affected by food matrix fat content. Thermal inactivation of L. monocytogenes was investigated by means of the maximum specific inactivation rate k max, log reductions, and sublethal injury (SI). Both for emulsions and gelled emulsions, k max decreased with increasing fat content below approximately 60°C, while a more complex behavior was observed at higher temperatures. In the emulsions, log reductions were considerably lower (i.e., 2-3 log) at 1% fat than in systems with higher fat content. In the gelled emulsions, log reductions generally decreased with increasing fat content. SI decreased with increasing fat content, both in emulsions and gelled emulsions. In conclusion, the inactivation rate (i.e., k max) of L. monocytogenes was affected by a complex relation between food matrix fat content, thermal conductivity, rheological properties, and inactivation temperature. Due to the small scale of the model systems, differences in k max did not directly affect the final log reductions in a similar fashion.