New software solutions for microbiological food safety assessment and management

A web-based microbiological hazard identification tool for infant foods

An integrated approach to identify and assess Microbiological Hazards (MHs) and mitigate risks in infant food chains is crucial to ensure safe foods for infants and young children. A systematic procedure was developed to identify MHs in specific infant foods. This includes five major steps: 1) relevant hazard-food pairing, 2) process inactivation efficiency, 3) recontamination possibility after processing, 4) MHs growth opportunity, and 5) MHs-food association level. These steps were integrated into an online tool called the Microbiological Hazards IDentification (MiID) decision support system (DSS), targeting food companies, governmental agencies and academia users, and is accessible at https://foodmicrobiologywur.shinyapps.io/Microbial_hazards_ID/. The MiID DSS was validated in four case studies, focussing on infant formula, fruit puree, cereal-based meals, and fresh fruits, each representing distinct products and processing characteristics. The results obtained through the application of the MiID DSS, compared with identification by food safety experts, consistently identified the top MHs in these food products. This process affirms its effectiveness in systematic hazard identification. The introduction of the MiID DSS helps to structure the first steps in HACCP (hazard analysis) and in risk assessment (hazard identification) to follow a structured and well-documented procedure, balancing the risk of overlooking relevant MHs or including too many irrelevant MHs. It is a valuable addition to risk analysis/assessment in infant food chains and has the potential for future extension. This includes the incorporation of newly acquired data related to infant foods via a semi-publicly hosted platform, or it can be adapted for hazard identification in general food products using a similar framework.

Training in modern statistical methodologies and software tools for the definition and analysis of (stochastic) quantitative microbial risk assessment models with a comparison between the Hungarian and Spanish food supply chains

Human pathogenic Salmonella enterica strains have been infecting people since historical times. The original human pathogens, typhoid Salmonella strains (e.g. S. Typhi) played a huge role in the previous centuries but nowadays in the developed world the number of cases or outbreaks caused by these serotypes deceased due to the development of personal and public hygiene. Nowadays in these regions the animal-borne zoonotic serotypes (e.g. S. Enteritidis) became more important because of their high prevalence in intensive animal husbandry. But these bacteria can also appear in fruits and vegetables. The fellow joined the scientific work of the Polytechnic University of Cartagena, Spain about the safety of plant-based products, where he could gain experience in microbiological laboratory exercises and theoretical calculations of statistics and modelling. The activities in the laboratory were part of the research lines already established at the host institution, being based on the protocols they have already implemented. Nonetheless, the fellow had the opportunity to design his own experiment, do the experimental work required and analysed the data within the context of a qualitative microbiological risk assessment. The main focus was on the heat resistance of two strains of zoonotic Salmonella spp. at different temperatures. Experiments were done using a reference strain and an extremely resistant variant to evaluate this rare phenotype. The experiments were executed using a Mastia thermoresistometer, a device patented by the host institution that provides more control when studying thermal treatments than traditional methods. The data was analysed using the principles of predictive microbiology, using the D-value as an estimate of heat resistance that provides insight into the bacterial behaviour. For this, the fellow used the bioinactivation software, developed within the host group. Through the work and results the fellow learned the principles of quantitative microbiological risk assessment (QMRA) and predictive microbiology, which was the aim for the EU-FORA programme.

Training in tools to develop quantitative microbial risk assessment of ready-to-eat food with a comparison between the Romanian and Spanish food supply chains

The prevention and control of bacterial contamination on ready-to-eat (RTE) fresh produce is an essential task to ensure food safety. Therefore, the development of novel and effective decontamination technologies to ensure microbiological safety of fruits and vegetables has gained considerable attention and new sanitisation methods are needed. The antimicrobial activity of essential oils (EOs) is well documented, but their application in fresh produce remains a challenge due to their hydrophobic nature. Thus, nanoemulsions efficiently contribute to support the use of EOs in foods by enhancing their dispersibility, their contact area and facilitating the introduction into bacterial cells. The combination of these factors ultimately increases their antimicrobial activity. Quantitative microbial risk assessment (QMRA) is gaining more attention as an effective tool to assess and prevent potential risks associated with food-borne pathogens. In this context, the current project aims to study the effectiveness of different washing methods based on nanoemulsified EOs, comparing them against traditional methods, using a QMRA model for Escherichia coli O157:H7 on cherry tomatoes. Different simulations within a stochastic risk assessment model were implemented using the biorisk package for R, aiming to describe microbial behaviour and biological risk along the Romanian and Spanish food supply chains of RTE fresh produce. Nanoemulsions were prepared using oregano and rosemary EOs, each from Romania and Spain. The four nanoemulsions were evaluated as decontamination treatments to control the growth of E. coli O157:H7 on artificially contaminated cherry tomatoes. The decontamination treatments showed encouraging results, comparable to commonly used chlorine solutions. Therefore, oregano and rosemary nanoemulsions are promising and could be a feasible alternative for chlorine solutions in the reduction of microbiological contaminants.

Tracking Microbial Diversity and Hygienic-Sanitary Status during Processing of Farmed Rainbow Trout (Oncorhynchus mykiss)

Aquaculture is becoming a strategic sector for many national economies to supply the increasing demand for fish from consumers. Fish culture conditions and processing operations can lead to an increase in microbial contamination of farmed fish that may shorten the shelf-life of fish products and byproducts, and ready-to-eat fishery products. The objective of this study was to evaluate the hygienic-sanitary status of water, environment, and processing of fresh-farmed rainbow trout (Oncorhynchus mykiss) fillets produced in a local fish farm in Andalusia, Spain. To achieve this, a longitudinal study was carried out by collecting environmental (air and food-contact surfaces), water from fish ponds, and rainbow trout samples. Thereby, seven sampling visits were performed between February 2021 and July 2022, where foodborne pathogens and spoilage microorganisms, together with physicochemical parameters, were analysed in the collected samples. Further, microbial identification of microbiota was achieved through a culture-dependent technique using blast analysis of 16S RNA gene sequencing. The results showed that Listeria monocytogenes and Salmonella were not detected in the analysed samples. Regarding the hygienic-sanitary status of the fish farm, the slaughtering bath, the eviscerating machine and the outlet water from fish ponds presented the highest counts of coliforms, Enterobacteriaceae, and Aerobic Mesophilic Bacteria. Staphylococcus aureus and sulphite-reducing Clostridium were identified in the conveyor belts, fish flesh, and viscera. The 16S RNA identification confirmed the presence of viable spoilage bacteria such as Citrobacter gillenii, Macrococcus caseolyticus, Hafnia paralvei, Lactococcus lactis, Lactococcus cremoris, Klebsiella, Escherichia coli, Morganella morganii, and Shewanella. Three of these genera (Citrobacter, Hafnia, and Pseudomonas) were present in all types of samples analysed. The results evidenced potential transmission of microbial contamination from contaminated packaging belts and boxes, evisceration and filleting machines to flesh and viscera samples, thus the establishment of control measures should be implemented in fish farm facilities to extend the shelf-life of farmed fishery products.

Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods

Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations. In order to achieve this, a total of 5618 data points for Pseudomonas spp. present in food products (beef, pork, and poultry) and culture media were gathered from the ComBase database. The machine learning algorithms were applied to predict the growth or survival behaviour of Pseudomonas spp. in food products and culture media by considering predictor variables such as temperature, salt concentration, water activity, and acidity. The suitability of the algorithms was assessed using statistical measures such as coefficient of determination (R²), root mean square error (RMSE), bias factor (Bf), and accuracy (A_f). Each of the regression algorithms showed appropriate estimation capabilities with R² ranging from 0.886 to 0.913, RMSE from 0.724 to 0.899, B_f from 1.012 to 1.020, and A_f from 1.086 to 1.101 for each food product and culture medium. Since the predictive capability of RFR was the best among the algorithms, externally collected data from the literature were used for RFR. The external validation process showed statistical indices of B_f ranging from 0.951 to 1.040 and A_f ranging from 1.091 to 1.130, indicating that RFR can be used for predicting the survival and growth of microorganisms in food products. Therefore, machine learning approaches can be considered as an alternative to conventional modelling methods in predictive microbiology. However, it is important to highlight that the prediction power of the machine learning regression method directly depends on the dataset size, and it requires a large dataset to be employed for modelling. Therefore, the modelling work of this study can only be used for the prediction of Pseudomonas spp. in specific food products (beef, pork, and poultry) and culture medium with certain conditions where a large dataset is available.

Ropiness in Bread—A Re-Emerging Spoilage Phenomenon

As bread is a very important staple food, its spoilage threatens global food security. Ropy bread spoilage manifests in sticky and stringy degradation of the crumb, slime formation, discoloration, and an odor reminiscent of rotting fruit. Increasing consumer demand for preservative-free products and global warming may increase the occurrence of ropy spoilage. Bacillus amyloliquefaciens, B. subtilis, B. licheniformis, the B. cereus group, B. pumilus, B. sonorensis, Cytobacillus firmus, Niallia circulans, Paenibacillus polymyxa, and Priestia megaterium were reported to cause ropiness in bread. Process hygiene does not prevent ropy spoilage, as contamination of flour with these Bacillus species is unavoidable due to their occurrence as a part of the endophytic commensal microbiota of wheat and the formation of heat-stable endospores that are not inactivated during processing, baking, or storage. To date, the underlying mechanisms behind ropy bread spoilage remain unclear, high-throughput screening tools to identify rope-forming bacteria are missing, and only a limited number of strategies to reduce rope spoilage were described. This review provides a current overview on (i) routes of entry of Bacillus endospores into bread, (ii) bacterial species implicated in rope spoilage, (iii) factors influencing rope development, and (iv) methods used to assess bacterial rope-forming potential. Finally, we pinpoint key gaps in knowledge and related challenges, as well as future research questions.

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.