Information systems in food safety management

A processing-type active real-time traceable certification system

In addition to substituting low-price and low-quality materials for high-quality materials at the food processing stage, many dishonest businesses risk adulterating chemical materials in products to reduce production costs or increase product flavor with chemical synthesis spices. As a result, the risks to food safety are increased. Most safety management and certification regulations proceed with on-site examination or sampling inspection. As current certification systems lack complete tracking and real-time certification processes, they cannot comprehensively check foods' processing and production processes and contents. Hence, food safety problems sway consumers' trust and confidence in certification systems. This study intends to improve the agricultural processing end's current food traceability certification system. Adding the design of raw material total quantity control provides a complete and sound real-time certification mechanism for citizens and businesses to assure consumer rights.

Global evolutional trend of safety in coal mining industry: a bibliometric analysis

Mining safety is recognized as one of the factors influencing the mining industry's long-term viability. Therefore, we did a bibliometric analysis to take stock of safety management in the coal mining industry. This study suggests a three-step strategy, comprising literature extraction and screening, bibliometric analysis, and discussion, to provide an in-depth understanding of the present state and development trend of mine safety research. The findings raise additional concerns which include the following: (i) Coal dust pollution has a direct and indirect impact on the environment. (ii) Most research projects have prioritized technology innovation and development over safety norms. (iii) Most works have come from advanced countries such as China, the USA, the UK, and Australia to the neglect of developing nations, leaving a significant vacuum in the literature. (iv) There are more major safety principles in the food business than in the mining industry, indicating a weak safety culture in the mining industry. Additionally, future research goals are provided, such as creating safer policy guidelines to support technological advancements, constructing effective safety mines, and creating solutions to dust pollution and human errors.

Organizational unlearning: A risky food safety strategy?

Strategically unlearning specific knowledge, behaviors, and practices facilitates product and process innovation, business model evolution, and new market opportunities and is essential to meet emergent supply chain and customer requirements. Indeed, addressing societal concerns such as climate change and net zero means elements of contemporary practice in food supply chains need to be unlearned to ensure new practices are adopted. However, unlearning is a risky process if crucial knowledge is lost, for example, if knowledge is situated in the supply base not the organization itself, or there is insufficient organizational food safety knowledge generation, curation, and management when new practices/processes are designed and implemented. An exploratory, critical review of management and food safety academic and gray literature is undertaken that aims to consider the cycle of unlearning, learning, and relearning in food organizations and supply chains with particular emphasis on organizational innovation, inertia, and the impact on food safety management systems and food safety performance. Findings demonstrate it is critical with food safety practices, such as duration date coding or refrigeration practices, that organizations "unlearn" in a way that does not increase organizational, food safety, or public health risk. This paper contributes to extant literature by highlighting the organizational vulnerabilities that can arise when strategically unlearning to promote sustainability in a food supply context. Mitigating such organizational, food safety, and public health risk means organizations must simultaneously drive unlearning, learning, and relearning as a dynamic integrated knowledge acquisition and management approach. The research implications are of value to academics, business managers, and wider industry.

Application of sodium diacetate, potassium lactate and calcium lactate as a microbial decontaminant during processing and storage of the traditional meatballs (rista)

The traditional meat products undergo microbial spoilage and lipid oxidation like other meat products because of their suitability as a medium for microbial growth, which in turn affect safety, quality and shelf life. In the case of traditional meat products like rista, ghostabha, kebab and several other products prepared in Kashmiri wazwan, organic acids or their salts have not been used to date. The present study was therefore aimed to evaluate the effect of different organic acid salts and storage conditions on various quality parameters of traditional meat products. Sodium diacetate (0.25%), potassium lactate (2.5%) and calcium lactate (2.5%) were used in three batches of traditional meat products. Sodium diacetate (SDA) treated samples showed lower total plate count (TPC) under refrigerated storage as compared to ambient storage. The SDA-treated sample retained the lowest value for TBARS (1.9 mg MDA/kg) and free fatty acid (3.2%) on the 15th day of refrigerated storage. Significantly (P ≤ 0.05) higher values for L*, a* and b* were maintained by SDA treated samples as compared to control, potassium lactate (PL) and calcium lactate (CL) treated samples under refrigeration conditions throughout the storage period. Organic acid treatment improved the quality attributes of the meat product during the storage period. The use of organic acids as a preservative in these products will help in reducing the use of synthetic agents. Further increase in the shelf life of traditional meat products by application of organic acids will help in their commercialization, marketing and round the year availability.

Graphical Abstract

PRIAS: An Intelligent Analysis System for Pesticide Residue Detection Data and Its Application in Food Safety Supervision

Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.

Dimensional Analysis Model Predicting the Number of Food Microorganisms

Predicting the number of microorganisms has excellent application in the food industry. It helps in predicting and managing the storage time and food safety. This study aimed to establish a new, simple, and effective model for predicting the number of microorganisms. The dimensional analysis model (DAM) was established based on dimensionless analysis and the Pi theorem. It was then applied to predict the number of Pseudomonas in Niuganba (NGB), a traditional Chinese fermented dry-cured beef, which was prepared and stored at 278 K, 283 K, and 288 K. Finally, the internal and external validation of the DAM was performed using six parameters including R ², R ² _adj , root mean square error (RMSE), standard error of prediction (%SEP), A _f , and B _f . High R ² and R ² _adj and low RMSE and %SEP values indicated that the DAM had high accuracy in predicting the number of microorganisms and the storage time of NGB samples. Both A _f and B _f values were close to 1. The correlation between the observed and predicted numbers of Pseudomonas was high. The study showed that the DAM was a simple, unified and effective model to predict the number of microorganisms and storage time.

Dignity in Food Aid Logistics Is Also a Knowledge Management and Digital Matter: Three Inspiring Initiatives in France

Throughout the world, including in developed countries, the COVID-19 crisis has revealed and accentuated food insecurity. The Food and Agriculture Organization of the United Nations clearly defines food security as a situation of not only availability and accessibility but also social acceptability (i.e., adequacy and sustainability). In developed countries, food security remains non-achieved at all. Notably, the so-called “little deprivation” leads the working poor to rely on food aid. We argue that even doing so, they remain food insecure: food aid is socially unacceptable because, despite their work, they are kept away from classical food access paths. In this article, we present the specificities of food aid in France and state some of its limits, namely those associated with the supply chain of donated foodstuffs. We propose a monographic study relying on a mix of firsthand material (six years of fieldwork from students with associations) and secondhand material (analysis of specialized, legal, and activity reports). We describe inspiring initiatives from three French associations and mobilize the recently published analysis of dignity construction in food aid in the United States of America to argue that dignity in food aid logistics is also a knowledge management and digital matter. Indeed, the initiatives of the three considered associations show concretely how knowledge management and digital systems can enhance dignity in food aid logistics.

Digital Technologies and Open Data Sources in Marine Biotoxins' Risk Analysis: The Case of Ciguatera Fish Poisoning

Currently, digital technologies influence information dissemination in all business sectors, with great emphasis put on exploitation strategies. Public administrations often use information systems and establish open data repositories, primarily supporting their operation but also serving as data providers, facilitating decision-making. As such, risk analysis in the public health sector, including food safety authorities, often relies on digital technologies and open data sources. Global food safety challenges include marine biotoxins (MBs), being contaminants whose mitigation largely depends on risk analysis. Ciguatera Fish Poisoning (CFP), in particular, is a MB-related seafood intoxication attributed to the consumption of fish species that are prone to accumulate ciguatoxins. Historically, CFP occurred endemically in tropical/subtropical areas, but has gradually emerged in temperate regions, including European waters, necessitating official policy adoption to manage the potential risks. Researchers and policy-makers highlight scientific data inadequacy, under-reporting of outbreaks and information source fragmentation as major obstacles in developing CFP mitigation strategies. Although digital technologies and open data sources provide exploitable scientific information for MB risk analysis, their utilization in counteracting CFP-related hazards has not been addressed to date. This work thus attempts to answer the question, "What is the current extent of digital technologies' and open data sources' utilization within risk analysis tasks in the MBs field, particularly on CFP?", by conducting a systematic literature review of the available scientific and grey literature. Results indicate that the use of digital technologies and open data sources in CFP is not negligible. However, certain gaps are identified regarding discrepancies in terminology, source fragmentation and a redundancy and downplay of social media utilization, in turn constituting a future research agenda for this under-researched topic.

Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

In predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: ‘time’, ‘temperature’, ‘pH’, ‘water activity’, ‘initial cell counts’, ‘whether the viable count is initial cell number’, and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.

Tropane alkaloid contamination of agricultural commodities and food products in relation to consumer health: Learnings from the 2019 Uganda food aid outbreak

Tropane alkaloids (TAs) are secondary plant metabolites derived mainly from Solanaceae plant families, with the most virulent invasive species being Datura stramonium. Datura stramonium commonly grows in cereal fields and produce TAs (e.g., hyoscyamine and scopolamine) which may accidentally contaminate cereals (and cereal-based foods) at occasionally high levels. Dietary exposure to TAs can be toxic and depending on the dose ingested can cause outcomes ranging from anticholinergic effects to acute poisoning and death. In 2019, 315 adults became ill and another five adults died in Uganda following consumption of a "Super Cereal" (a fortified blended food) that was later confirmed to be contaminated by TAs-a scenario which provoked this holistic review on TAs in foodstuffs. Thus, this article provides information on the history, development, occurrences, exposures, and human legislative and health benchmarks for TAs. It describes control strategies for reducing TA contamination of agricultural commodities and resultant health implications following consumption of TA contaminated foodstuffs. Adequate application of food safety control measures (including maximum limits) and good practices, from the start of cereal cultivation through to the final stages of manufacturing of food products can aid in the reduction of seeing toxic plants including D. stramonium in cereal fields.

Application of blockchain technology in food safety control：current trends and future prospects

Blockchain technology is a distributed ledger technology and is expected to face some difficulties and challenges in various industries due to its transparency, decentralization, tamper-proof nature, and encryption security. Food safety has been paid increasing attention in recent years with economic development. Based on a systematic literature critical analysis, the causes of food safety problems and the state-of-the-art blockchain technology overview, including the definition of blockchain, development history, classification, structure, characteristics, and main applications, the feasibility and application prospects of blockchain technology in plant food safety, animal food safety, and processed food safety were proposed in this review. Finally, the challenges of the blockchain technology itself and the difficulties in the application of food safety were analyzed. This study contributes to the extant literature in the field of food safety by discovering the excellent potential of blockchain technology and its implications for food safety control. Our results indicated that blockchain is a promising technology toward a food safety control, with many ongoing initiatives in food products, but many food-related issues, barriers, and challenges still exist. Nevertheless, it is expected to provide a feasible solution for controlling food safety risks.

Control of Biological Hazards in Insect Processing: Application of HACCP Method for Yellow Mealworm (Tenebrio molitor) Powders

Entomophagy has been part of human diets for a long time in a significant part of the world, but insects are considered to be a novel food everywhere else. It would appear to be a strategic alternative in the future of human diet to face the challenge of ensuring food security for a growing world population, using more environmentally sustainable production systems than those required for the rearing of other animals. Tenebrio molitor, called yellow mealworm, is one of the most interesting insect species in view of mass rearing, and can be processed into a powder that ensures a long shelf life for its use in many potential products. When considering insects as food or feed, it is necessary to guarantee their safety. Therefore, manufacturers must implement a Hazard Analysis Critical Control plan (HACCP), to limit risks for consumers' health. The aim of this case study was to develop a HACCP plan for Tenebrio molitor larvae powders for food in a risk-based approach to support their implementation in industry. Specific purposes were to identify related significant biological hazards and to assess the efficiency of different manufacturing process steps when used as Critical Control Points. Then, combinations of four different processes with four potential uses of powders by consumers in burger, protein shake, baby porridge, and biscuits were analyzed with regard to their safety.

Analyzing app-based food information services: the case of Olive Oil sector

Purpose

The provision of information about food to consumers is well recognized to be problematic and complex. Novel approaches tend to adopt app-based services as a way for consumers to get better food information. Potentially, these services could provide relevant information to consumers because they could address particular needs by leveraging on context-awareness, ubiquity and pervasiveness. Do current app-based services actualize these potentialities? The purpose of this paper is twofold: to propose a methodological approach that can be easily adapted to conduct reviews of app-based services and provide consumers with information about food and to answer this question on a specified food information domain (namely, olive oil).

Design/methodology/approach

A literature review was performed to investigate characteristics of food information services under a consumer’s value perspective. Then, a qualitative framework was built, which allowed a multidimensional analysis of the food information provided by a service through a mobile app. Moreover, an application of that framework on “olive oil” mobile apps was carried out, with the twofold objective of providing an example of application of the proposed framework in a real context and analyzing the role of mobile apps in reducing consumers’ information and knowledge gap in this particular domain. The review covers commercial applications on “olive oil,” available on the Apple App Store and Google Play Store.

Findings

A framework of multidimensional analysis of the two-way information flow in the interaction between a consumer/user and a food app-based service is proposed. The framework represents a useful tool to analyze the offer of mobile-based services in a given sector. The framework is then applied to perform a situation analysis of app-based services in the olive oil market. The analysis highlights that olive oil information providers do not seem to fully exploit recent advances in “Internet of food” technologies. Hence, results from our survey suggest some direction to conceive and develop innovative mobile olive oil apps that better exploit mobiquitous technology features.

Practical implications

Consumers perceive a growing need of information about food. This need is mainly due to recent cases of adulterations, allegations of fraud and subterfuges that have invested food sector (in particular, olive oil sector). This research provides a useful framework to analyze some aspects of management of food information provision through mobile apps. Such a framework represents a practical tool that provides fruitful insights for the design of a new generation of food-app based services within the so-called “internet of food” domain.

Originality/value

Although there are countless mobile apps aimed to assist consumers with their everyday food practices, little research has been devoted to investigate the impact of mobiquitous technologies on the management of food information provision to consumers. The available research lacks in defining an approach capable to analyze the relevance of information provided to targeted audiences by mobile food apps. This paper tries to fill this knowledge gap by proposing a framework that is based on a characterization of food information flows in terms of categorization, source and service features.

Implementation of Time Temperature Indicators to Improve Temperature Monitoring and Support Dynamic Shelf Life in Meat Supply Chains

Based on the well-investigated OnVu™ TTI kinetics, models were developed to adjust the label to different food products and predict the discolouration process under dynamic temperature conditions. After the successful validation under laboratory conditions, the applicability of the time temperature indicator (TTI) as shelf life indicator was tested in a national poultry chain. The TTI accurately reflected the temperature fluctuations occurring under real chain conditions. Shelf life predictions based on the discolouration of the TTIs were in accordance with the microbial shelf life of the product. The models were integrated in an online software tool to check for the compliance of the cold chain and predict the remaining shelf life of the product. The implementation of TTI and the software result in a valuable tool to support the decision-making process in the cold chain. The application of flexible shelf life enables the reduction of food waste in the meat supply chain.

Multiple attribute decision making model and application to food safety risk evaluation

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

Comparative Phenotypic and Genotypic Analysis of Edwardsiella Isolates from Different Hosts and Geographic Origins, with Emphasis on Isolates Formerly Classified as E. tarda, and Evaluation of Diagnostic Methods

Edwardsiella spp. are responsible for significant losses in important wild and cultured fish species worldwide. Recent phylogenomic investigations have determined that bacteria historically classified as Edwardsiella tarda actually represent three genetically distinct yet phenotypically ambiguous taxa with various degrees of pathogenicity in different hosts. Previous recognition of these taxa was hampered by the lack of a distinguishing phenotypic character. Commercial test panel configurations are relatively constant over time, and as new species are defined, appropriate discriminatory tests may not be present in current test panel arrangements. While phenobiochemical tests fail to discriminate between these taxa, data presented here revealed discriminatory peaks for each Edwardsiella species using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) methodology, suggesting that MALDI-TOF can offer rapid, reliable identification in line with current systematic classifications. Furthermore, a multiplex PCR assay was validated for rapid molecular differentiation of the Edwardsiella spp. affecting fish. Moreover, the limitations of relying on partial 16S rRNA for discrimination of Edwardsiella spp. and advantages of employing alternative single-copy genes gyrB and sodB for molecular identification and classification of Edwardsiella were demonstrated. Last, sodB sequencing confirmed that isolates previously defined as typical motile fish-pathogenic E. tarda are synonymous with Edwardsiella piscicida, while atypical nonmotile fish-pathogenic E. tarda isolates are equivalent to Edwardsiella anguillarum Fish-nonpathogenic E. tarda isolates are consistent with E. tarda as it is currently defined. These analyses help deconvolute the scientific literature regarding these organisms and provide baseline information to better facilitate proper taxonomic assignment and minimize erroneous identifications of Edwardsiella isolates in clinical and research settings.

Mathematical modelling of growth of Listeria monocytogenes in raw chilled pork

The aim of this study was to analyse the growth kinetics of Listeria monocytogenes in naturally contaminated chilled pork. A cocktail of 26 meat-borne L. monocytogenes was inoculated to raw or sterile chilled pork to observe its growth at 4, 10, 16, 22 and 28°C respectively. The growth data were fitted by the Baranyi model and Ratkowsky square-root model. Results showed that the Baranyi model and Ratkowsky square-root model could describe the growth characteristics of L. monocytogenes at different temperatures reasonably well in raw chilled pork (1·0 ≤ Bf ≤ Af ≤ 1·1). Compared with the growth of L. monocytogenes in sterile chilled pork, the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The microbial predictive models developed in this study can be used to predict the growth of L. monocytogenes during natural spoilage, and construct quantitative risk assessments in chilled pork.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study simulated the actual growth of Listeria monocytogenes in chilled pork to the maximum extent, and described its growth characteristics of L. monocytogenes during natural spoilage. This study showed that the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The models developed in this study can be used to predict the growth of L. monocytogenes during refrigerated storage.

Methicillin-resistant Staphylococcus aureus: a controversial food-borne pathogen

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of severe healthcare-associated (HA) infections. Although during the last decade the incidence of HA invasive infections has dropped, the incidence of community-associated MRSA (CA-MRSA) infections has risen among the general population. Moreover, CA-MRSA, livestock-associated MRSA (LA-MRSA) and HA-MRSA (HA-MRSA) can be found in foods intended for human consumption. Several studies from different geographical areas have reported the presence of enterotoxin genes in several MRSA food isolates. Molecular typing studies have revealed genetic relatedness of these enterotoxigenic isolates with isolates incriminated in human infections. The contamination sources for foods, especially animal-origin foods, may be livestock as well as humans involved in animal husbandry and food-processing. Under favourable environmental conditions for growth and enterotoxin production, enterotoxigenic S. aureus isolates present in foods can cause staphylococcal food poisoning (SFP), irrespective of the contamination origin. Owing to the typically moderate clinical manifestations of SFP, the S. aureus strains responsible for SFP (cases or outbreaks) are frequently either not identified or not further characterized. Antimicrobial susceptibility testing is rarely performed, because administration of antimicrobial therapy is not required in the vast majority of cases. Staphylococcal food poisoning is the result of consumption of foods with preformed enterotoxins. Hence, similar to methicillin-sensitive enterotoxigenic S. aureus, enterotoxigenic MRSA can also act as food-borne pathogens upon favourable conditions for growth and enterotoxin production. The severity of the intoxication is not related to the antimicrobial resistance profile of the causative S. aureus strain and therefore MRSA food-borne outbreaks are not expected to be more severe.

SIGNIFICANCE AND IMPACT OF THE STUDY

This review evaluates the potential of methicillin-resistant Staphylococcus aureus (MRSA) as food-borne pathogens based on the current knowledge about the epidemiology of MRSA, their prevalence in livestock, foods of animal origin and humans, and their ability to produce enterotoxins.

Identification of microbes from the surfaces of food-processing lines based on the flow cytometric evaluation of cellular metabolic activity combined with cell sorting

In this study the design of a flow cytometry-based procedure to facilitate the detection of adherent bacteria from food-processing surfaces was evaluated. The measurement of the cellular redox potential (CRP) of microbial cells was combined with cell sorting for the identification of microorganisms. The procedure enhanced live/dead cell discrimination owing to the measurement of the cell physiology. The microbial contamination of the surface of a stainless steel conveyor used to process button mushrooms was evaluated in three independent experiments. The flow cytometry procedure provided a step towards monitoring of contamination and enabled the assessment of microbial food safety hazards by the discrimination of active, mid-active and non-active bacterial sub-populations based on determination of their cellular vitality and subsequently single cell sorting to isolate microbial strains from discriminated sub-populations. There was a significant correlation (r = 0.97; p < 0.05) between the bacterial cell count estimated by the pour plate method and flow cytometry, despite there being differences in the absolute number of cells detected. The combined approach of flow cytometric CRP measurement and cell sorting allowed an in situ analysis of microbial cell vitality and the identification of species from defined sub-populations, although the identified microbes were limited to culturable cells.

Extraction and downstream processing of plant-derived recombinant proteins

Plants offer the tantalizing prospect of low-cost automated manufacturing processes for biopharmaceutical proteins, but several challenges must be addressed before such goals are realized and the most significant hurdles are found during downstream processing (DSP). In contrast to the standardized microbial and mammalian cell platforms embraced by the biopharmaceutical industry, there are many different plant-based expression systems vying for attention, and those with the greatest potential to provide inexpensive biopharmaceuticals are also the ones with the most significant drawbacks in terms of DSP. This is because the most scalable plant systems are based on the expression of intracellular proteins in whole plants. The plant tissue must therefore be disrupted to extract the product, challenging the initial DSP steps with an unusually high load of both particulate and soluble contaminants. DSP platform technologies can accelerate and simplify process development, including centrifugation, filtration, flocculation, and integrated methods that combine solid-liquid separation, purification and concentration, such as aqueous two-phase separation systems. Protein tags can also facilitate these DSP steps, but they are difficult to transfer to a commercial environment and more generic, flexible and scalable strategies to separate target and host cell proteins are preferable, such as membrane technologies and heat/pH precipitation. In this context, clarified plant extracts behave similarly to the feed stream from microbes or mammalian cells and the corresponding purification methods can be applied, as long as they are adapted for plant-specific soluble contaminants such as the superabundant protein RuBisCO. Plant-derived pharmaceutical proteins cannot yet compete directly with established platforms but they are beginning to penetrate niche markets that allow the beneficial properties of plants to be exploited, such as the ability to produce 'biobetters' with tailored glycans, the ability to scale up production rapidly for emergency responses and the ability to produce commodity recombinant proteins on an agricultural scale.

Current food safety management systems in fish-exporting companies require further improvements to adequately cope with contextual pressure: case study

Fish-processing plants still face food safety (FS) challenges worldwide despite the existence of several quality assurance standards and food safety management systems/s (FSMSs). This study assessed performance of FSMS in fish exporting sector considering pressure from the context in which they operate. A FSMS diagnostic tool with checklist was used to assess the context, FSMS, and FS output in 9 Kenyan fish exporting companies. Majority (67%) companies operated at moderate- to high-risk context but with an average performance in control and assurance activities. This situation could be insufficient to deal with ambiguity, uncertainty, and vulnerability issues in the context characteristics. Contextual risk posed by product characteristics (nature of raw materials) and chain environment characteristics was high. Risk posed by the chain environment characteristics, low power in supplier relationships, and low degree of authority in customer relationships was high. Lack of authority in relationship with suppliers would lead to high raw material risk situation. Even though cooling facilities, a key control activity, was at an advanced level, there was inadequate packaging intervention equipment which coupled with inadequate physical intervention equipment could lead to further weakened FSMS performance. For the fish companies to improve their FSMS to higher level and enhance predictability, they should base their FSMS on scientific information sources, historical results, and own experimental trials in their preventive, intervention, and monitoring systems. Specific suggestions are derived for improvements toward higher FSMS activity levels or lower risk levels in context characteristics.

PRACTICAL APPLICATION

Weak areas in performance of control and assurance activities in export fish-processing sector already implementing current quality assurance guidelines and standards were studied taking into consideration contextual pressure wherein the companies operate. Important mitigation measures toward improved contextual risk, core assurance, and control activities irrespective of applied food safety management systems in fish industries were suggested.

Proposal of performance objectives and sampling schemes for Listeria monocytogenes in fresh meat intended to be eaten cooked under different storage practices

The goal of this study was to define a practical approach to derive risk management measures, such as performance objectives (POs), for Listeria monocytogenes in pork cuts intended to be eaten cooked. Moreover, sampling plans to verify the compliance of meat lots to such POs are presented. The POs were estimated as prevalence and/or concentration values that should not be exceeded at time of consumption. To derive possible POs for L. monocytogenes, ten lots of pork cuts, collected within the same slaughterhouse along a one-year period, were tested for the presence and concentration of the pathogen under four different scenarios through the product shelf life. Our results indicated that the median values of the prevalence distributions ranged between 0.41 and 0.68. The number of samples to be tested in order to verify lot compliance ranged between six, for samples tested immediately after packaging, and three, for samples tested at the end of the shelf life. The concentration values ranged between 2.02 log10 CFU/g, for samples tested immediately after packaging, up to 3.14 log10 CFU/g for samples tested after final storage at 14°C. The concentration of L. monocytogenes in the samples contaminated by less than 10 CFU/g was estimated between 7 CFU/10g to 7 CFU/g, after storage at retail and abuse temperature, respectively. Basing on the knowledge of log normal distributions, the maximum contamination level of the lots in order to achieve the suggested POs, was calculated. It was obtained that mean concentration estimated as PO should be between -0.43 and 0.48 log10 CFU/g. Other risk management options are further evaluated and discussed. These results would help food operators and authorities to establish safety targets and corrective actions regarding inhibition of L. monocytogenes in fresh pork meat.

Development and validation of a stochastic model for potential growth of Listeria monocytogenes in naturally contaminated lightly preserved seafood

A new stochastic model for the simultaneous growth of Listeria monocytogenes and lactic acid bacteria (LAB) was developed and validated on data from naturally contaminated samples of cold-smoked Greenland halibut (CSGH) and cold-smoked salmon (CSS). During industrial processing these samples were added acetic and/or lactic acids. The stochastic model was developed from an existing deterministic model including the effect of 12 environmental parameters and microbial interaction (O. Mejlholm and P. Dalgaard, Food Microbiology, submitted for publication). Observed maximum population density (MPD) values of L. monocytogenes in naturally contaminated samples of CSGH and CSS were accurately predicted by the stochastic model based on measured variability in product characteristics and storage conditions. Results comparable to those from the stochastic model were obtained, when product characteristics of the least and most preserved sample of CSGH and CSS were used as input for the existing deterministic model. For both modelling approaches, it was shown that lag time and the effect of microbial interaction needs to be included to accurately predict MPD values of L. monocytogenes. Addition of organic acids to CSGH and CSS was confirmed as a suitable mitigation strategy against the risk of growth by L. monocytogenes as both types of products were in compliance with the EU regulation on ready-to-eat foods.

Modeling the influence of temperature, water activity and water mobility on the persistence of Salmonella in low-moisture foods

Salmonella can survive in low-moisture foods for long periods of time. Reduced microbial inactivation during heating is believed to be due to the interaction of cells and water, and is thought to be related to water activity (a(w)). Little is known about the role of water mobility in influencing the survival of Salmonella in low-moisture foods. The aim of this study was to determine how the physical state of water in low-moisture foods influences the survival of Salmonella and to use this information to develop mathematical models that predict the behavior of Salmonella in these foods. Whey protein powder of differing water mobilities was produced by pH adjustment and heat denaturation, and then equilibrated to aw levels between 0.19±0.03 and 0.54±0.02. Water mobility was determined by wide-line proton-NMR. Powders were inoculated with a four-strain cocktail of Salmonella, vacuum-sealed and stored at 21, 36, 50, 60, 70 and 80°C. Survival data was fitted to the log-linear, the Geeraerd-tail, the Weibull, the biphasic-linear and the Baranyi models. The model with the best ability to describe the data over all temperatures, water activities and water mobilities (f(test)<F(table)) was selected for secondary modeling. The Weibull model provided the best description of survival kinetics for Salmonella. The influence of temperature, aw and water mobility on the survival of Salmonella was evaluated using multiple linear regression. Secondary models were developed and then validated in dry non-fat dairy and grain, and low-fat peanut and cocoa products within the range of the modeled data. Water activity significantly influenced the survival of Salmonella at all temperatures, survival increasing with decreasing a(w). Water mobility did not significantly influence survival independent of a(w). Secondary models were useful in predicting the survival of Salmonella in various low-moisture foods providing a correlation of R=0.94 and an acceptable prediction performance of 81%. The % bias and % discrepancy results showed that the models were more accurate in predicting survival in non-fat food systems as compared to foods containing low-fat levels (12% fat). The models developed in this study represent the first predictive models for survival of Salmonella in low-moisture foods. These models provide baseline information to be used for research on risk mitigation strategies for low-moisture foods.

Preliminary stochastic model for managing Vibrio parahaemolyticus and total viable bacterial counts in a Pacific oyster (Crassostrea gigas) supply chain

Vibrio parahaemolyticus can accumulate and grow in oysters stored without refrigeration, representing a potential food safety risk. High temperatures during oyster storage can lead to an increase in total viable bacteria counts, decreasing product shelf life. Therefore, a predictive tool that allows the estimation of both V. parahaemolyticus populations and total viable bacteria counts in parallel is needed. A stochastic model was developed to quantitatively assess the populations of V. parahaemolyticus and total viable bacteria in Pacific oysters for six different supply chain scenarios. The stochastic model encompassed operations from oyster farms through consumers and was built using risk analysis software. Probabilistic distributions and predictions for the percentage of Pacific oysters containing V. parahaemolyticus and high levels of viable bacteria at the point of consumption were generated for each simulated scenario. This tool can provide valuable information about V. parahaemolyticus exposure and potential control measures and can help oyster companies and regulatory agencies evaluate the impact of product quality and safety during cold chain management. If coupled with suitable monitoring systems, such models could enable preemptive action to be taken to counteract unfavorable supply chain conditions.