Inter-strain variability on the cardinal parameters (pH and Aw) of clinical and food isolates of Listeria monocytogenes using turbidimetric measurements.

Listeria monocytogenes is a foodborne pathogen which, in 2021, was considered the fifth most commonly reported zoonosis in humans in the European Union (EU). Ready-to-eat (RTE) fishery products, deli meats or soft cheeses have been mostly involved in food safety alerts and outbreaks in the last years. Hurdle technology by food industries has been widely used to enhance the safety of foods. Among the barriers, the application of acid and osmotic stress during processing is extensively used worldwide. This study aims to gain knowledge about the inter-strain variability of twenty-six clinical and food L. monocytogenes isolates with the estimation of their cardinal parameters using turbidimetric measurements. To analyse the data and to obtain the estimated cardinal values, a common statistical procedure was set up. The estimation of cardinal parameters showed a high inter-strain variability of L. monocytogenes, and no correlation was observed between Aw min and pHmin values for the studied strains. By grouping the strains in clinical, meat and fish origin, it was observed that strains from the meat group presented the lowest average pHmin values (4.57), thus showing potential acid adaptation. This work contributes to gain knowledge of the inter-strain variability of L. monocytogenes in relation with pH and Aw cardinal values, as well as provide a starting point for future validation studies in fish and meat food matrices.

Effects of carbon dioxide and oxygen on the growth rate of various food spoilage bacteria.

The growth of six bacterial species (Carnobacterium maltaromaticum, Bacillus weihenstephanensis, Bacillus cereus, Paenibacillus spp., Leuconostoc mesenteroides and Pseudomonas fragi) was studied in various gas compositions. Growth curves were obtained at various oxygen concentrations (between 0.1 and 21%), or various carbon dioxide concentrations (between 0 and 100%). Decreasing the O2 concentration from 21% to about 3-5% has no effect on the bacterial growth rates, which are only affected by low oxygen levels. For each strain studied, the growth rate decreased linearly with carbon dioxide concentration, except for L. mesenteroides which remained insensible to this gas. Conversely, the most sensitive strain was totally inhibited by 50% of carbon dioxide in the gas phase at 8 °C. Predictive models were fitted, and the parameters characterizing the inhibitory effect of these two gases were estimated. This study provides new tools to help the food industry design suitable packaging for MAP storage.

Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content during Lactiplantibacillus plantarum ITM21B Fermentation in Liquid Sourdough.

The application of mathematical modeling to study and characterize lactic acid bacterial strains with pro-technological and functional features has gained attention in recent years to solve the problems relevant to the variabilities of the fermentation processes of sourdough. Since the key factors contributing to the sourdough quality are relevant to the starter strain growth and its metabolic activity, in this study, the cardinal growth parameters for pH, temperature (T), water activity (aw), and undissociated lactic acid of the sourdough strain Lactiplantibacillus plantarum ITM21B, were determined. The strain growth, pH, organic acids (lactic, acetic, phenyllactic, and hydroxy-phenyllactic), total free amino acids, and proteins were monitored during fermentation of a liquid sourdough based on wheat flour and gluten (Bio21B) after changing the starting T, pH, and inoculum load. Results demonstrated that the different fermentation conditions affected the strain growth and metabolite pattern. The organic acid production and growth performance were modeled in Bio21B, and the resulting predictive model allowed us to simulate in silico the strain performances in liquid sourdough under different scenarios. This mathematical predictive approach can be useful to optimize the fermentation conditions needed to obtain the suitable nutritional and technological characteristics of the L. plantarum ITM21B liquid sourdough.

A Predictive Growth Model for Pro-technological and Probiotic Lacticaseibacillus paracasei Strains Fermenting White Cabbage.

Bacterial strains belonging to Lacticaseibacillus paracasei species are generally used as starters in food fermentations and/or as probiotics. In the current study, the growth cardinal parameters of four L. paracasei strains (IMPC2.1, IMPC4.1, P40 and P101), isolated from table olives or human source, were determined. Strains were grown in liquid medium and incubated at several temperatures (10 values from 5.5°C-40°C) and pH (15 values from 3.2 to 9.1) along the growth range. The cardinal temperature model was used to describe temperature effects on the maximum specific growth rate of L. paracasei whereas new equations were developed for the effect of pH. The estimated Tmin values ranged between -0.97°C and 1.95°C and were lower than 0°C for strains IMPC4.1 and P101. Strain P40 was able to grow in the most restricted range of temperature (from 1.95°C to 37.46°C), while strain IMPC4.1 was estimated to survive at extreme conditions showing the lowest pHmin . Maximum specific growth rates of L. paracasei IMPC2.1 in white cabbage (Brassica oleracea var. capitata) were used to calculate the correction factor (Cf ) defined as the bias between the bacterial maximum specific growth rate in broth and in the food matrix. A simple bi-linear model was also developed for the effect of temperature on the maximum population density reached in white cabbage. This information was further used to simulate the growth of L. paracasei strains in cabbage and predict the time to reach the targeted probiotic level (7 log10 CFU/g) using in silico simulations. This study demonstrates the potential of the predictive microbiology to predict the growth of beneficial and pro-technological strains in foods in order to optimize the fermentative process.

Modelling the thermal inactivation of spores from different phylogenetic groups of Bacillus cereus.

The objective of this work is to match available phylogenetic information for Bacillus cereus strains with published thermal resistance parameters (D90°C, z) and to use this information to develop refined inactivation models for B. cereus sensu lato. To do so, the thermal resistance parameters were retrieved for 57 strains of B. cereus that could be assigned to a phylogenetic group. This information was used to build specific distributions for D90°C and z for the different phylogenetic groups of B. cereus to build refined thermal inactivation models for B. cereus. For validation purposes, thermal parameters were also retrieved for additional strains of unknown groups, but which had been classified as psychrotrophic or mesophilic. Monte Carlo simulations were first performed assuming that the model parameters D90°C and z are independent. However, based on the observation that combinations of very high D90°C and high z-values were not reported, an alternative Monte Carlo simulation set was explored for the phylogenetic Groups with very high z-values (i.e.i.e. Groups IV and VI). With both simulation sets, the predicted lower and upper limits of the D-values are close to the lowest and highest D-values reported in two previous meta-analysis studies. However, a better correspondence between the predicted and observed limits is obtained when using the alternative simulation set.

The effect of pH on the growth rate of Bacillus cereus sensu lato: Quantifying strain variability and modelling the combined effects of temperature and pH.

In this study, the effect of pH, alone or in combination with temperature, on the maximum growth rate (µmax) of B. cereus sensu lato was investigated. In phase 1, the effect of pH at 30 °C was studied for 16 mesophilic strains and 2 psychrotrophic strains of Bacillus cereus sensu lato. The µmax vs. pH relationship was found to show a similar pattern for all the strains. Several pH models from literature were evaluated and the best performing 'growth rate vs. pH' model selected. A stochastic model was then developed to predict the maximum specific growth rate of mesophilic B. cereus at 30 °C as a function of pH, the intra-species variability being incorporated via considering the model parameters (e.g. pHmin) randomly distributed. The predicted maximum specific growth rates were acceptably close to independent published data. In phase 2, the combined effects of temperature and pH were studied. Growth rates were also generated at 15, 20 and 40 °C for a selection of strains and the pH model was fitted at each temperature. Interestingly, the results showed that the estimates for the pHmin parameter for mesophilic strains were lower at 20-30 °C than near the optimum temperature (40 °C), suggesting that experiments for the determination of this parameter should be conducted at lower-than-optimum temperatures. New equations were proposed for the relationship between temperature and the minimum pH-values, which were also consistent with the experimental growth boundaries. The parameters defining this equation quantify the minimum temperature for growth observed experimentally, the temperature of maximum enzyme stability and the maximum temperature for growth. Deviations from the Gamma hypothesis (multiplicative effects of environmental factors on the maximum specific growth rate) were observed near the growth limits, especially at 40 °C. To improve model performance, two approaches, one based on a minimum pH-term (doi: https://doi.org/10.3389/fmicb.2019.01510) and one based on an interaction term (doi: http://dx.doi.org/10.1016/S0168-1605(01)00640-7) were evaluated.

A stochastic approach for modelling the effects of temperature on the growth rate of Bacillus cereus sensu lato.

A stochastic model that predicts the maximum specific growth rate (µmax) of Bacillus cereus sensu lato as a function of temperature was developed. The model integrates the intra-species variability by incorporating distributions of cardinal parameters (Tmin, Topt, Tmax) in the model. Growth rate data were generated for 22 strains, covering 5 major phylogenetic groups of B. cereus, and their cardinal temperatures identified. Published growth rate data were also incorporated in the model fitting, resulting in a set of 33 strains. Based on their cardinal temperatures, we identified clusters of Bacillus cereus strains that show similar response to temperature and these clusters were considered separately in the stochastic model. Interestingly, the µopt values for psychrotrophic strains were found to be significantly lower than those obtained for mesophilic strains. The model developed within this work takes into account some correlations existing between parameters (µopt, Tmin, Topt, Tmax). In particular, the relationship highlighted between the b-slope of the Ratkowsky model and Tmin (doi: https://doi.org/10.3389/fmicb.2017.01890) was adapted to the case of the popular Cardinal Temperature Model. This resulted in a reduced model in which µopt is replaced by a function of Tmin, Topt and 2 strain-independent parameters. A correlation between the Tmin parameter and the experimental minimal growth temperature was also highlighted and integrated in the model for improved predictions near the temperature growth limits. Compared to the classical approach, the model developed in this study leads to improved predictions for temperatures around Tmin and more realistic tails for the predicted distributions of µmax. It can be useful for describing the variability of the Bacillus cereus Group in Quantitative Microbial Risk Assessment (QMRA). An example of application of the stochastic model to Reconstituted Infant Formulae (RIF) was proposed.

The consistent differential expression of genetic pathways following exposure of an industrial Pseudomonas aeruginosa strain to preservatives and a laundry detergent formulation.

Pseudomonas aeruginosa is a common contaminant associated with product recalls in the home and personal care industry. Preservation systems are used to prevent spoilage and protect consumers, but greater knowledge is needed of preservative resistance mechanisms used by P. aeruginosa contaminants. We aimed to identify genetic pathways associated with preservative exposure by using an industrial P. aeruginosa strain and implementing RNA-Seq to understand gene expression changes in response to industry relevant conditions. The consistent differential expression of five genetic pathways during exposure to multiple industrial growth conditions associated with benzisothiazolone (BIT) and phenoxyethanol (POE) preservatives, and a laundry detergent (LD) formulation, was observed. A MexPQ-OpmE Resistance Nodulation Division efflux pump system was commonly upregulated in response to POE, a combination of BIT and POE, and LD together with BIT. In response to all industry conditions, a putative sialic acid transporter and isoprenoid biosynthesis gnyRDBHAL operon demonstrated consistent upregulation. Two operons phnBA and pqsEDCBA involved in Pseudomonas quinolone signaling production and quorum-sensing were also consistently downregulated during exposure to all the industry conditions. The ability to identify consistently differentially expressed genetic pathways in P. aeruginosa can inform the development of future targeted preservation systems that maintain product safety and minimise resistance development.

Does proximity to neighbours affect germination of spores of non-proteolytic Clostridium botulinum?

It is recognised that inoculum size affects the rate and extent of bacterial spore germination. It has been proposed that this is due to spores interacting: molecules released from germinated spores trigger germination of dormant neighbours. This study investigated whether changes to the total number of spores in a system or proximity to other spores (local spore density) had a more significant effect on interaction between spores of non-proteolytic Clostridium botulinum strain Eklund 17B attached to defined areas of microscope slides. Both the number of spores attached to the slides and local spore density (number of spores per mm(2)) were varied by a factor of nine. Germination was observed microscopically at 15 °C for 8 h and the probability of, and time to, germination calculated from image analysis measurements. Statistical analysis revealed that the effect of total spore number on the probability of germination within 8 h was more significant than that of proximity to neighbours (local spore density); its influence on germination probability was approximately four-times greater. Total spore number had an even more significant affect on time to germination; it had a nine-fold greater influence than proximity to neighbours. The applied models provide a means to characterise, quantitatively, the effect of the total spore number on spore germination relative to the effect of proximity to neighbouring spores.

Establishing equivalence for microbial-growth-inhibitory effects ("iso-hurdle rules") by analyzing disparate listeria monocytogenes data with a gamma-type predictive model.

Preservative factors act as hurdles against microorganisms by inhibiting their growth; these are essential control measures for particular food-borne pathogens. Different combinations of hurdles can be quantified and compared to each other in terms of their inhibitory effect ("iso-hurdle"). We present here a methodology for establishing microbial iso-hurdle rules in three steps: (i) developing a predictive model based on existing but disparate data sets, (ii) building an experimental design focused on the iso-hurdles using the model output, and (iii) validating the model and the iso-hurdle rules with new data. The methodology is illustrated with Listeria monocytogenes. Existing data from industry, a public database, and the literature were collected and analyzed, after which a total of 650 growth rates were retained. A gamma-type model was developed for the factors temperature, pH, a(w), and acetic, lactic, and sorbic acids. Three iso-hurdle rules were assessed (40 logcount curves generated): salt replacement by addition of organic acids, sorbic acid replacement by addition of acetic and lactic acid, and sorbic acid replacement by addition of lactic/acetic acid and salt. For the three rules, the growth rates were equivalent in the whole experimental domain (γ from 0.1 to 0.5). The lag times were also equivalent in the case of mild inhibitory conditions (γ ≥ 0.2), while they were longer in the presence of salt than acids under stress conditions (γ < 0.2). This methodology allows an assessment of the equivalence of inhibitory effects without intensive data generation; it could be applied to develop milder formulations which guarantee microbial safety and stability.

Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract.

In the present study six probiotic Lactobacillus rhamnosus strains were investigated for their ability to survive in the human upper gastrointestinal tract through a dynamic gastric model of digestion. MRS broth was used as delivery vehicle and survival was investigated during in vitro gastric and gastric plus duodenal digestion. Results highlighted that all tested strains showed good survival rate during both gastric and duodenal digestion. In particular, three strains exhibited a great survival showing a recovery percentage in the range between 117 and 276%. In agreement with survival data, high lactic acid production was detected for all strains, confirming their metabolic activity during digestion.

Modelling spoilage of fresh turbot and evaluation of a time-temperature integrator (TTI) label under fluctuating temperature.

Kinetic models were developed to predict the microbial spoilage and the sensory quality of fresh fish and to evaluate the efficiency of a commercial time-temperature integrator (TTI) label, Fresh Check(R), to monitor shelf life. Farmed turbot (Psetta maxima) samples were packaged in PVC film and stored at 0, 5, 10 and 15 degrees C. Microbial growth and sensory attributes were monitored at regular time intervals. The response of the Fresh Check device was measured at the same temperatures during the storage period. The sensory perception was quantified according to a global sensory indicator obtained by principal component analysis as well as to the Quality Index Method, QIM, as described by Rahman and Olley [Rahman, H.A., Olley, J., 1984. Assessment of sensory techniques for quality assessment of Australian fish. CSIRO Tasmanian Regional Laboratory. Occasional paper n. 8. Available from the Australian Maritime College library. Newnham. Tasmania]. Both methods were found equally valid to monitor the loss of sensory quality. The maximum specific growth rate of spoilage bacteria, the rate of change of the sensory indicators and the rate of change of the colour measurements of the TTI label were modelled as a function of temperature. The temperature had a similar effect on the bacteria, sensory and Fresh Check kinetics. At the time of sensory rejection, the bacterial load was ca. 10(5)-10(6) cfu/g. The end of shelf life indicated by the Fresh Check label was close to the sensory rejection time. The performance of the models was validated under fluctuating temperature conditions by comparing the predicted and measured values for all microbial, sensory and TTI responses. The models have been implemented in a Visual Basic add-in for Excel called "Fish Shelf Life Prediction (FSLP)". This program predicts sensory acceptability and growth of spoilage bacteria in fish and the response of the TTI at constant and fluctuating temperature conditions. The program is freely available at http://www.azti.es/muestracontenido.asp?idcontenido=980&content=15&nodo1=30&nodo2=0.

Methods to determine the growth domain in a multidimensional environmental space.

Data from a database on microbial responses to the food environment (ComBase, see www.combase.cc) were used to study the boundary of growth several pathogens (Aeromonas hydrophila, Escherichia coli, Listeria monocytogenes, Yersinia enterocolitica). Two methods were used to evaluate the growth/no growth interface. The first one is an application of the Minimum Convex Polyhedron (MCP) introduced by Baranyi et al. [Baranyi, J., Ross, T., McMeekin, T., Roberts, T.A., 1996. The effect of parameterisation on the performance of empirical models used in Predictive Microbiology. Food Microbiol. 13, 83-91.]. The second method applies logistic regression to define the boundary of growth. The combination of these two different techniques can be a useful tool to handle the problem of extrapolation of predictive models at the growth limits.

Modelling the influence of single acid and mixture on bacterial growth.

Depending on environmental factors, the prediction of bacterial growth is made difficult by the complexity of foodstuff. Although the influence of temperature, pH, and water activity are usually taken into account, models have to be completed with the influence of acid mixture. Nine strains of Listeria spp., four Salmonella spp., one Staphylococcus aureus, one Escherichia coli, and Listeria innocua ATCC 33090 were used for this study to extend model proposed by [Le Marc, Y., Huchet, V., Bourgeois, C., Guyonnet, J., Mafart, P., Thuault, D., 2002. Modelling the growth kinetics of Listeria as a function of temperature, pH and organic acid concentration. International Journal of Food Microbiology 73, 219-237]. Derived from data of [Houtsma, P.C., Kusters, B.J., De Wit, J.C., Rombouts, F.M., Zwietering, M.H., 1994. Modelling growth rates of Listeria innocua as a function of lactate concentration. International Journal of Food Microbiology 24, 113-123] and our own data, the extended model described accurately different effects of addition of acid salts in the medium (decrease of water activity and pH, variation of undissociated weak acid form, and variation of synergetic effect between environmental factors). This previous model was implemented to describe the observed variability of behaviour of the different studied strains. alpha reflected the general behaviour of species (sensitiveness to low or high undissociated acid concentration), and MIC(U) reflected the various resistances of strains. From this simple model, a new model was built for describing the effects of concentrations of several mixed acids on bacterial growth rates. Simulations of growth were carried out from three acids mixtures by inputting parameter estimates previously obtained. Despite a very variable effect of investigated acids on growth, the new model yielded fair predictions.