Tail or artefact? Illustration of the impact that uncertainty of the serial dilution and cell enumeration methods has on microbial inactivation

Simplified and Detailed Evaluations of the Uncertainty of the Measurement of Microbiological Contamination of Pharmaceutical Products

BACKGROUND

The control of the microbial contamination of pharmaceutical products (PP) is crucial to ensure their safety and efficacy. The validity of the monitoring of such contamination depends on the uncertainty of this quantification. Highly uncertain quantifications due to the variability of determinations or the magnitude of systematic effects affecting microbial growth or other analytical operations make analysis unfit for the intended use. The quantification of the measurement uncertainty expressing the combined effects of all random and systematic effects affecting the analysis allows for a sound decision about quantification adequacy for their intended use. The complexity of the quantification of microbial analysis uncertainty led to the development of simplified ways of performing this evaluation.

OBJECTIVE

This work assesses the adequacy of the simplified quantification of the uncertainty of the determination of the microbial contamination of PP by log transforming microbial count and dilution factor of the test sample whose uncertainty is combined in a log scale using the uncertainty propagation law.

METHODS

This assessment is performed by a parallel novel bottom-up and accurate evaluation of microbial analysis uncertainty involving the Monte Carlo method simulation of the Poisson log-normal distribution of counts and of the normally distributed measured volumes involved in the analysis. Systematic effects are assessed and corrected on results to compensate for their impact on the determinations. Poisson regression is used to predict precision affecting determinations on unknown test samples.

RESULT

Simplified and detailed models of the uncertainty of the measurement of the microbial contamination of PP are provided, allowing objective comparisons of several determinations and those with a maximum contamination level.

CONCLUSIONS

This work concludes that triplicate determinations are required to produce results with adequately low uncertainty and that simplified uncertainty quantification underevaluates or overevaluates the uncertainty from determinations based on low or high colony numbers, respectively. Therefore, detailed uncertainty evaluations are advised for determinations between 50 and 200% of PP's maximum admissible contamination value.

HIGHLIGHT

User-friendly tools for detailed and simplified evaluations of the uncertainty of the measurement of microbial contamination of PP are provided together with the understanding of when simplifications are adequate.

Modeling strain variability in Campylobacter jejuni thermal inactivation by quantifying the number of strains required

There is a limited understanding of the survival responses of Campylobacter jejuni during thermal processing, which must be investigated for appropriate risk assessment and processing. Therefore, we aimed to elucidate the survival response of C. jejuni and develop a predictive model considering strain variability and uncertainty, which are important for quantitative microbial risk assessment (QMRA) or risk-based processing control measures. We employed the most probable curve (MPC) method to consider the uncertainty in cell concentrations. Further, the multivariate normal (MVN) distribution served as a model for strain variability in bacterial survival behavior. The prediction curves from the MVN successfully captured the parameter variability of the most probable curves of each strain. More than ten reference strains effectively described the strain variability in parameters using the MVN distribution. The findings indicated that, with sufficient strain data, the MVN could estimate the strain variability, including unknown strains. The multi-level model for strain variability can potentially become a specialized tool for QMRA and risk-based processing controls. The combined approach of MPC and MVN provides valuable insights into strain variability, emphasizing the importance of accounting for variability and uncertainty in predictive models for QMRA and risk-based processing control measures.

Quantitative microbial spoilage risk assessment caused by fungi in sports drinks through multilevel modelling

The risk of fungal spoilage of sports drinks produced in the beverage industry was assessed using quantitative microbial spoilage risk assessment (QMSRA). The most relevant pathway was the contamination of the bottles during packaging by mould spores in the air. Mould spores' concentration was estimated by longitudinal sampling for 6 years (936 samples) in different production areas and seasons. This data was analysed using a multilevel model that separates the natural variability in spore concentration (as a function of sampling year, season, and area) and the uncertainty of the sampling method. Then, the expected fungal contamination per bottle was estimated by Monte Carlo simulation, considering their settling velocity and the time and exposure area. The product's shelf life was estimated through the inoculation of bottles with mould spores, following the determination of the probability of visual spoilage as a function of storage time at 20 and 30 °C using logistic regression. The Monte Carlo model estimated low expected spore contamination in the product (1.7 × 10-6 CFU/bottle). Nonetheless, the risk of spoilage is still relevant due to the large production volume and because, as observed experimentally, even a single spore has a high spoilage potential. The applicability of the QMSRA during daily production was made possible through the simplification of the model under the hypothesis that no bottle will be contaminated by more than one spore. This simplification allows the calculation of a two-dimensional performance objective that combines the spore concentration in the air and the exposure time, defining "acceptable combinations" according to an acceptable level of spoilage (ALOS; the proportion of spoiled bottles). The implementation of the model at the operational level was done through the representation of the simplified model as a two-dimensional diagram that defines acceptable and unacceptable areas. The innovative methodology employed here for defining and simplifying QMSRA models can be a blueprint for future studies aiming to quantify the risk of spoilage of other beverages with a similar scope.

A challenge in washing water with the sun: 24h of SODIS fails to inactivate Acanthamoeba castellanii cysts and internalized Pseudomonas aeruginosa under strong real sun conditions

Despite access to drinking water being a basic human right, the availability of safe drinking water remains a privilege that many do not have and as a result, many lives are lost each year due to waterborne diseases associated with the consumption of biologically unsafe water. To face this situation, different low-cost household drinking water treatment technologies (HDWT) have been developed, and among them is solar disinfection (SODIS). Despite the effectiveness of SODIS and the epidemiological gains being consistently documented in the literature, there is a lack of evidence of the effectiveness of the batch-SODIS process against protozoan cysts as well as their internalized bacteria under real sun conditions. This work evaluated the effectiveness of the batch-SODIS process on the viability of Acanthamoeba castellanii cysts, and internalized Pseudomonas aeruginosa. Dechlorinated tap water contaminated with 5.6 × 103 cysts/L, contained in PET (polyethylene terephthalate) bottles, was exposed for 8 h a day to strong sunlight (531-1083 W/m2 of maximum insolation) for 3 consecutive days. The maximum water temperature inside the reactors ranged from 37 to 50 °C. Cyst viability was assessed by inducing excystment on non-nutrient agar, or in water with heat-inactivated Escherichia coli. After sun exposure for 0, 8, 16 and 24 h, the cysts remained viable and without any perceptible impairment in their ability to excyst. 3 and 5.5 log CFU/mL of P. aeruginosa were detected in water containing untreated and treated cysts, respectively, after 3 days of incubation at 30 °C. The batch-SODIS process is unable to inactivate A. castellanii cysts as well as its internalized bacteria. Although the use of batch SODIS by communities should continue to be encouraged, SODIS-disinfected water should be consumed within 3 days.

The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the "true" microbial concentration according to the model, the "actual" concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.

Assessment and Prediction of Fish Freshness Using Mathematical Modelling: A Review

Fish freshness can be considered as the combination of different nutritional and organoleptic attributes that rapidly deteriorate after fish capture, i.e., during processing (cutting, gutting, packaging), storage, transport, distribution, and retail. The rate at which this degradation occurs is affected by several stress variables such as temperature, water activity, or pH, among others. The food industry is aware that fish freshness is a key feature influencing consumers’ willingness to pay for the product. Therefore, tools that allow rapid and reliable assessment and prediction of the attributes related to freshness are gaining relevance. The main objective of this work is to provide a comprehensive review of the mathematical models used to describe and predict the changes in the key quality indicators in fresh fish and shellfish during storage. The work also briefly describes such indicators, discusses the most relevant stress factors affecting the quality of fresh fish, and presents a bibliometric analysis of the results obtained from a systematic literature search on the subject.

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.

Inactivation kinetics and cell envelope damages of foodborne pathogens Listeria monocytogenes and Salmonella Enteritidis treated with cold plasma

In recent years, more attention has been paid to the application of cold plasma (CP) in eliminating foodborne pathogenic bacteria. This work investigated CP effects on inactivation kinetics and cell envelopes of Listeria monocytogenes (L. monocytogenes) and Salmonella Enteritidis (S. Enteritidis). Bacterial suspensions were treated with dielectric barrier discharge atmospheric CP at 75 kV for different treatment time. Three regression models were tested for estimating inactivation kinetics. Reactive species generated in plasma, the appearance and integrity of bacterial cells, the activity and secondary structure of enzymes in the cell envelope, and molecular docking, were measured for evaluating the envelope damages. Results indicated that Log-linear model was suitable for L. monocytogenes and the Weibull model was suitable for S. Enteritidis. S. Enteritidis was more sensitive to short-lived reactive species (such as OH radicals) in plasma than L. monocytogenes, and the cell envelope of S. Enteritidis was more severely damaged (the increased membrane permeability and leakage of intracellular substances) after plasma treatment. Interestingly, compared with S. Enteritidis, the decrease in the activity of enzymes existing in the cell envelope of L. monocytogenes did not contribute significantly to the death of bacteria. Molecular docking further suggested that the decrease in the enzyme activity might be due to the modification of the enzyme, by the interaction between reactive species in plasma (H₂O₂) and amino acid residues of the enzyme through the hydrogen bond.

The COM-Poisson Process for Stochastic Modeling of Osmotic Inactivation Dynamics of Listeria monocytogenes

Controlling harmful microorganisms, such as Listeria monocytogenes, can require reliable inactivation steps, including those providing conditions (e.g., using high salt content) in which the pathogen could be progressively inactivated. Exposure to osmotic stress could result, however, in variation in the number of survivors, which needs to be carefully considered through appropriate dispersion measures for its impact on intervention practices. Variation in the experimental observations is due to uncertainty and biological variability in the microbial response. The Poisson distribution is suitable for modeling the variation of equi-dispersed count data when the naturally occurring randomness in bacterial numbers it is assumed. However, violation of equi-dispersion is quite often evident, leading to over-dispersion, i.e., non-randomness. This article proposes a statistical modeling approach for describing variation in osmotic inactivation of L. monocytogenes Scott A at different initial cell levels. The change of survivors over inactivation time was described as an exponential function in both the Poisson and in the Conway-Maxwell Poisson (COM-Poisson) processes, with the latter dealing with over-dispersion through a dispersion parameter. This parameter was modeled to describe the occurrence of non-randomness in the population distribution, even the one emerging with the osmotic treatment. The results revealed that the contribution of randomness to the total variance was dominant only on the lower-count survivors, while at higher counts the non-randomness contribution to the variance was shown to increase the total variance above the Poisson distribution. When the inactivation model was compared with random numbers generated in computer simulation, a good concordance between the experimental and the modeled data was obtained in the COM-Poisson process.

Guidance on validation of lethal control measures for foodborne pathogens in foods

Food manufacturers are required to obtain scientific and technical evidence that a control measure or combination of control measures is capable of reducing a significant hazard to an acceptable level that does not pose a public health risk under normal conditions of distribution and storage. A validation study provides evidence that a control measure is capable of controlling the identified hazard under a worst-case scenario for process and product parameters tested. It also defines the critical parameters that must be controlled, monitored, and verified during processing. This review document is intended as guidance for the food industry to support appropriate validation studies, and aims to limit methodological discrepancies in validation studies that can occur among food safety professionals, consultants, and third-party laboratories. The document describes product and process factors that are essential when designing a validation study, and gives selection criteria for identifying an appropriate target pathogen or surrogate organism for a food product and process validation. Guidance is provided for approaches to evaluate available microbiological data for the target pathogen or surrogate organism in the product type of interest that can serve as part of the weight of evidence to support a validation study. The document intends to help food manufacturers, processors, and food safety professionals to better understand, plan, and perform validation studies by offering an overview of the choices and key technical elements of a validation plan, the necessary preparations including assembling the validation team and establishing prerequisite programs, and the elements of a validation report.

Tunisian Pistachio Hull Extracts: Phytochemical Content, Antioxidant Activity, and Foodborne Pathogen Inhibition

The present study aimed to discriminate pistachio (Pistacia vera L.) hulls belonging to three different Tunisian geographical origins and extracted separately by hexane, acetone, acetonitrile, and water in terms of phytochemicals and antioxidant and antibacterial activities using multivariate analysis. Significant differences ( $P<0.05$ ) in the phytochemical content, antioxidant, and antifoodborne bacterial activities were detected among the pistachio hulls populations. Pearson correlation, principal component analysis (PCA), hierarchical cluster analysis (HCA), and heat map were used to distinguish the relationship between the different regions on the basis of the biological activities. It was found that the twelve (4 extracts × 3 geographical sources) pistachio hulls extracts could be classified geographically into four distinct groups. To explore the mode of action of the aqueous pistachio hull extract against L. monocytogenes and S. enterica, polymyxin acriflavine lithium chloride ceftazidime aesculin mannitol (PALCAM) and xylose lysine deoxycholate (XLD) broth media were artificially contaminated at 104 CFU/mL. Using linear and general linear models, aqueous pistachio hull extract was demonstrated to control the two dominant food-borne pathogens by suppressing the bacterial growth.

Microbiological and process variability using biological indicators of inactivation (BIIs) based on Bacillus cereus spores of food and fish-based animal by-products to evaluate microwave heating in a pilot plant

Microwave processing can be a valid alternative to conventional heating for different types of products. It enables a more efficient heat transfer in the food matrix, resulting in higher quality products. However, for many food products a uniform temperature distribution is not possible because of heterogeneities in their physical properties and non-uniformtiy in the electric field pattern. Hence, the effectiveness of microwave inactivation treatments is influenced by both intrinsic (differences between cells) and extrinsic variability (non-uniform temperature). Interpreting the results of the process and considering its impact on microbial inactivation is essential to ensure effective and efficient processing. In this work, we quantified the variability in microbial inactivation attained in a microwave pasteurization treatment with a tunnel configuration at pilot-plant scale. The configuration of the equipment makes it impossible to measure the product temperature during treatment. For that reason, variability in microbial counts was measured using Biological Inactivation Indicators (BIIs) based on spherical particles of alginate inoculated with spores of Bacillus spp. The stability of the BIIs and the uncertainty associated to them was assessed using preliminary experiments in a thermoresistometer. Then, they were introduced in the food product to analyse the microbial inactivation in different points of the products during the microwave treatment. Experiments were made in a vegetable soup and a fish-based animal by-product (F-BP). The results show that the variation in the microbial counts was higher than expected based on the biological variability estimated in the thermoresistometer and the uncertainty of the BIIs. This is due to heterogeneities in the temperature field (measured using a thermographic camera), which were higher in the F-BP than in the vegetable soup. Therefore, for the process studied, extrinsic variability was more relevant than intrinsic variability. The methodology presented in this work can be a valid method to evaluate pasteurization treatments of foods processed by heating, providing valuable information of the microbial inactivation achieved. It can contribute to design microwave processes for different types of products and for product optimization.

Biosand Filter as a Point-of-Use Water Treatment Technology: Influence of Turbidity on Microorganism Removal Efficiency

The number of people living without access to clean water can be reduced by the implementation of point-of-use (POU) water treatment. Among POU treatment systems, the domestic biosand filter (BSF) stands out as a viable technology. However, the performance of the BSF varies with the inflow water quality characteristics, especially turbidity. In some locations, people have no choice but to treat raw water that has turbidity above recommended levels for the technology. This study aimed to measure the efficiency with which the BSF removes microorganisms from well water and from fecal-contaminated water with turbidity levels of 3, 25, and 50 NTU. Turbidity was controlled by the addition of kaolin to water. Turbidity removal varied from 88% to 99%. Reductions in total coliform (TC) and Escherichia coli ranged from 0.54–2.01 and 1.2–2.2 log removal values (LRV), respectively. The BSF that received water with a higher level of turbidity showed the greatest reduction in the concentration of microorganisms. Additional testing with water contaminated with four bacterial pure cultures showed reductions between 2.7 and 3.6 LRV. A higher reduction in microorganisms was achieved after 30–35 days in operation. Despite the filter’s high efficiency, the filtrates still had some microorganisms, and a disinfection POU treatment could be added to increase water safety.

Inactivation kinetics modeling of Escherichia coli in concentrated urine for implementing predictive environmental microbiology in sanitation safety planning

Urine concentration (condensation) leads to the inactivation of pathogens in urine owing to a hyperosmotic environment. This study proposed an inactivation kinetic model of Escherichia coli (E. coli), a surrogate of human bacterial pathogens, in concentrated synthetic urine. The model parameters were obtained under an assumption that the inactivation rate of E. coli followed a binomial distribution, which made it possible to accurately simulate the time-course decay of E. coli in synthetic urine. The inactivation rate constant values obtained in concentrated urine samples, ammonium buffer solutions and carbonate buffer solutions indicated that the osmotic pressure was a relatively predominant cause for the inactivation of E. coli. The appropriate storage time was estimated using the approach of quantitative microbial risk assessment, which indicated that the 5-fold concentrated urine could be safely collected after 1-day storage when urea was hydrolyzed, whereas 91-hour storage was required for non-concentrated urine. The occupational risk was not negligible even with 6-month storage at 20 °C when urea was not hydrolyzed, which suggested that the urine storage styles should be clarified more minutely. The present study highlights the importance of "predictive environmental microbiology," which deals with inactivation kinetic models of microorganisms under varied environmental conditions to fully implement the hazard analysis and critical control point (HACCP) approach for the safe use of human excreta in agriculture.

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.

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.

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.

Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation

Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R²) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 10⁴ CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth.

Guidelines for the design of (optimal) isothermal inactivation experiments

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. mathematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package. We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.