The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies.

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk-based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano-specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.

Big data in food safety: An overview.

Technology is now being developed that is able to handle vast amounts of structured and unstructured data from diverse sources and origins. These technologies are often referred to as big data, and open new areas of research and applications that will have an increasing impact in all sectors of our society. In this paper we assessed to which extent big data is being applied in the food safety domain and identified several promising trends. In several parts of the world, governments stimulate the publication on internet of all data generated in public funded research projects. This policy opens new opportunities for stakeholders dealing with food safety to address issues which were not possible before. Application of mobile phones as detection devices for food safety and the use of social media as early warning of food safety problems are a few examples of the new developments that are possible due to big data.

Regulatory aspects of nanotechnology in the agri/feed/food sector in EU and non-EU countries.

Nanotechnology has the potential to innovate the agricultural, feed and food sectors (hereinafter referred to as agri/feed/food). Applications that are marketed already include nano-encapsulated agrochemicals or nutrients, antimicrobial nanoparticles and active and intelligent food packaging. Many nano-enabled products are currently under research and development, and may enter the market in the near future. As for any other regulated product, applicants applying for market approval have to demonstrate the safe use of such new products without posing undue safety risks to the consumer and the environment. Several countries all over the world have been active in examining the appropriateness of their regulatory frameworks for dealing with nanotechnologies. As a consequence of this, different approaches have been taken in regulating nano-based products in agri/feed/food. The EU, along with Switzerland, were identified to be the only world region where nano-specific provisions have been incorporated in existing legislation, while in other regions nanomaterials are regulated more implicitly by mainly building on guidance for industry. This paper presents an overview and discusses the state of the art of different regulatory measures for nanomaterials in agri/feed/food, including legislation and guidance for safety assessment in EU and non-EU countries.

Development and validation of single particle ICP-MS for sizing and quantitative determination of nano-silver in chicken meat.

The application of nanomaterials is leading to innovative developments in industry, agriculture, consumer products, and food and related sectors. However, due to the special properties of these materials there are concerns about their safety, especially because of our limited knowledge of human health effects and the fact that constantly new nanomaterials and applications thereof are being produced. The development of analytical techniques is a key element to understand the benefits as well as the risks of the application of such materials. In this study, a method is developed and validated for sizing and quantifying nano-silver in chicken meat using single particle inductive coupled plasma mass spectrometry (ICP-MS). Samples are processed using an enzymatic digestion followed by dilution of the digest and instrumental analysis of the diluted digest using single particle ICP-MS. Validation of the method in the concentration of 5-25 mg/kg 60-nm silver nanoparticles showed good performance with respect to trueness (98-99% for size, 91-101% for concentration), repeatability (<2% for size, <11% for concentration), and reproducibility (<6% for size, <16% for concentration). The response of the method is linear, and a detection limit as low as 0.1 mg/kg can be obtained. Additional experiments showed that the method is robust and that digests are stable for 3 weeks at 4 °C. Once diluted for single particle ICP-MS analysis, the stability is limited. Finally, it was shown that nano-silver in chicken meat is not stable. Silver nanoparticles dissolved and were transformed into silver sulfide. While this has implications for the form in which nano-silver will be present in real-life meat samples, the developed method will be able to determine the presence and quantity of nanoparticle silver in such samples.

Sub-chronic toxicity study in rats orally exposed to nanostructured silica.

BACKGROUND: Synthetic Amorphous Silica (SAS) is commonly used in food and drugs. Recently, a consumer intake of silica from food was estimated at 9.4 mg/kg bw/day, of which 1.8 mg/kg bw/day was estimated to be in the nano-size range. Food products containing SAS have been shown to contain silica in the nanometer size range (i.e. 5-200 nm) up to 43% of the total silica content. Concerns have been raised about the possible adverse effects of chronic exposure to nanostructured silica. METHODS: Rats were orally exposed to 100, 1000 or 2500 mg/kg bw/day of SAS, or to 100, 500 or 1000 mg/kg bw/day of NM-202 (a representative nanostructured silica for OECD testing) for 28 days, or to the highest dose of SAS or NM-202 for 84 days. RESULTS: SAS and NM-202 were extensively characterized as pristine materials, but also in the feed matrix and gut content of the animals, and after in vitro digestion. The latter indicated that the intestinal content of the mid/high-dose groups had stronger gel-like properties than the low-dose groups, implying low gelation and high bioaccessibility of silica in the human intestine at realistic consumer exposure levels. Exposure to SAS or NM-202 did not result in clearly elevated tissue silica levels after 28-days of exposure. However, after 84-days of exposure to SAS, but not to NM-202, silica accumulated in the spleen. Biochemical and immunological markers in blood and isolated cells did not indicate toxicity, but histopathological analysis, showed an increased incidence of liver fibrosis after 84-days of exposure, which only reached significance in the NM-202 treated animals. This observation was accompanied by a moderate, but significant increase in the expression of fibrosis-related genes in liver samples. CONCLUSIONS: Although only few adverse effects were observed, additional studies are warranted to further evaluate the biological relevance of observed fibrosis in liver and possible accumulation of silica in the spleen in the NM-202 and SAS exposed animals respectively. In these studies, dose-effect relations should be studied at lower dosages, more representative of the current exposure of consumers, since only the highest dosages were used for the present 84-day exposure study.

Applying federated learning to combat food fraud in food supply chains.

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data.

Bayesian Networks modeling of diarrhetic shellfish poisoning in Mytilus edulis harvested in Bantry Bay, Ireland.

Diarrhetic Shellfish Poisoning (DSP) results from the human consumption of contaminated shellfish with marine biotoxins, which are produced by some species of marine dinoflagellates, mainly belonging to the genus Dinophysis. Shellfish contamination with marine biotoxins not only pose a threat to human health, but also lead to financial loss to aquaculture operations from the temporary closure of production areas when toxin concentrations exceed regulatory levels. In this study, we developed a Bayesian Network (BN) model for forecasting the short-term variations of DSP toxins in blue mussels (Mytilus edulis) from Bantry Bay, Southwest Ireland. Data inputs to a BN model from 10 production sites in Bantry Bay included plankton cell densities in sea water, DSP toxin concentration in mussels and sea surface temperature. The model was trained with data from 2014 to 2018, and validated with data of 2019. Validation consisted of predicting the DSP toxin concentration at one production site using the model parameters from the other locations as input values. Model validation showed that the prediction accuracy was higher than 86%. Sensitivity analysis indicated that in general, DSP toxin concentration was more relevant than plankton abundance. This initial work has demonstrated the usefulness of BN modeling as an approach to short term forecasting. Further work is ongoing to use the model for scenario testing and to increase the number of environmental parameters used as inputs to the model.

Automatic classification of literature in systematic reviews on food safety using machine learning.

Systematic reviews are used to collect relevant literature to answer a research question in a way that is clear, thorough, unbiased and reproducible. They are implemented as a standard method in the domain of food safety to obtain a literature overview on the state-of-the-art research related to food safety topics of interest. A disadvantage to systematic reviews, however, is that this process is time-consuming and requires expert domain knowledge. The work reported here aims to reduce the time needed by an expert to screen all possible relevant articles by applying machine learning techniques to classify the articles automatically as either relevant or not relevant. Eight different machine learning algorithms and ensembles of all combinations of these algorithms were tested on two different systematic reviews on food safety (i.e. chemical hazards in cereals and leafy greens). The results showed that the best performance was obtained by an ensemble of naive Bayes and a support vector machine, resulting in an average decrease of 32.8% in the amount of articles the expert has to read and an average decrease in irrelevant articles of 57.8% while keeping 95% of the relevant articles. It was concluded that automatic classification of the literature in a systematic literature review can support experts in their task and save valuable time without compromising the quality of the review.

Exploring the role of green and Industry 4.0 technologies in achieving sustainable development goals in food sectors.

In recent years, the rapid increase in the global population, the challenges associated with climate change, and the emergence of new pandemics have all become major threats to food security worldwide. Consequently, innovative solutions are urgently needed to address the current challenges and enhance food sustainability. Green technologies have gained significant attention for many food applications, while the technologies of the fourth industrial revolution (Industry 4.0) are reshaping different production and consumption sectors, such as food and agriculture. In this review, a general overview of green and Industry 4.0 technologies from a food perspective will be provided. Connections between green food technologies (e.g., green preservation, processing, extraction, and analysis) and Industry 4.0 enablers (e.g., artificial intelligence, big data, smart sensors, robotics, blockchain, and the Internet of Things) and the Sustainable Development Goals (SDGs) will be identified and explained. Green and Industry 4.0 technologies are both rapidly becoming a valuable part of meeting the SDGs. These technologies demonstrate high potential to foster ecological and digital transitions of food systems, delivering societal, economic, and environmental outcomes. A range of green technologies has already provided innovative solutions for major food system transformations, while the application of digital technologies and other Industry 4.0 technological innovations is still limited in the food sector. It is therefore expected that more green and digital solutions will be adopted in the coming years, harnessing their full potential to achieve a healthier, smarter, more sustainable and more resilient food future.

Role of analytical testing for food fraud risk mitigation - A commentary on implementation of analytical fraud testing: Role of analytical testing for food fraud mitigation.

Food fraud is of high concern to the food industry. A multitude of analytical technologies exist to detect fraud. However, this testing is often expensive. Available databases detailing fraud occurrences were systematically examined to determine how frequently analytical testing triggered fraud detection. A conceptual framework was developed for deciding when to implement analytical testing programmes for fraud and a framework to consider the economic costs of fraud and the benefits of its early detection. Factors associated with statistical sampling for fraud detection were considered. Choice of sampling location on the overall food-chain may influence the likelihood of fraud detection.

Review of health safety aspects of nanotechnologies in food production.

Due to new, previously unknown, properties attributed to engineered nanoparticles many new products are introduced in the agro-food area. Nanotechnologies cover many aspects, such as disease treatment, food security, new materials for pathogen detection, packaging materials and delivery systems. As with most new and evolving technologies, potential benefits are emphasized, while little is known on safety of the application of nanotechnologies in the agro-food sector. This review gives an overview of scientific issues that need to be addressed with priority in order to improve the risk assessment for nanoparticles in food. The following research topics are considered to contribute pivotally to risk assessment of nanotechnologies and nanoparticles in food products. Set a definition for NPs to facilitate regulatory discussions, prioritization of research and exchange of study results. Develop analytical tools for the characterization of nanoparticles in complex biological matrices like food. Establish relevant dose metrics for nanoparticles used for both interpretation of scientific studies as well as regulatory frameworks. Search for deviant behavior (kinetics) and novel effects (toxicity) of nanoparticles and assess the validity of currently used test systems following oral exposure. Estimate the consumer exposure to nanoparticles.

GMDD: a database of GMO detection methods.

BACKGROUND: Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. RESULTS: GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. CONCLUSION: GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks.

While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.

A holistic approach to food safety risks: Food fraud as an example.

Production of sufficient, safe and nutritious food is a global challenge faced by the actors operating in the food production chain. The performance of food-producing systems from farm to fork is directly and indirectly influenced by major changes in, for example, climate, demographics, and the economy. Many of these major trends will also drive the development of food safety risks and thus will have an effect on human health, local societies and economies. It is advocated that a holistic or system approach taking into account the influence of multiple "drivers" on food safety is followed to predict the increased likelihood of occurrence of safety incidents so as to be better prepared to prevent, mitigate and manage associated risks. The value of using a Bayesian Network (BN) modelling approach for this purpose is demonstrated in this paper using food fraud as an example. Possible links between food fraud cases retrieved from the RASFF (EU) and EMA (USA) databases and features of these cases provided by both the records themselves and additional data obtained from other sources are demonstrated. The BN model was developed from 1393 food fraud cases and 15 different data sources. With this model applied to these collected data on food fraud cases, the product categories that thus showed the highest probabilities of being fraudulent were "fish and seafood" (20.6%), "meat" (13.4%) and "fruits and vegetables" (10.4%). Features of the country of origin appeared to be important factors in identifying the possible hazards associated with a product. The model had a predictive accuracy of 91.5% for the fraud type and demonstrates how expert knowledge and data can be combined within a model to assist risk managers to better understand the factors and their interrelationships.

Different responses of Caco-2 and MCF-7 cells to silver nanoparticles are based on highly similar mechanisms of action.

The mode of action of silver nanoparticles (AgNPs) is suggested to be exerted through both Ag+ and AgNP dependent mechanisms. Ingestion is one of the major NP exposure routes, and potential effects are often studied using Caco-2 cells, a well-established model for the gut epithelium. MCF-7 cells are epithelial breast cancer cells with extensive well-characterized toxicogenomics profiles. In the present study, we aimed to gain a deeper understanding of the cellular molecular responses in Caco-2 and MCF-7 cells after AgNP exposure in order to evaluate whether epithelial cells derived from different tissues demonstrated similar responses. These insights could possibly reduce the size of cell panels for NP hazard identification screening purposes. AgNPs of 20, 30, 60, and 110 nm, and AgNO3 were exposed for 6 h and 24 h. AgNPs were shown to be taken up and dissolve intracellularly. Compared with MCF-7 cells, Caco-2 cells showed a higher sensitivity to AgNPs, slower gene expression kinetics and absence of NP size-dependent responses. However, on a molecular level, no significant differences were observed between the two cell types. Transcriptomic analysis showed that Ag(NP) exposure caused (oxidative) stress responses, possibly leading to cell death in both cell lines. There was no indication for effects specifically induced by AgNPs. Responses to AgNPs appeared to be induced by silver ions released from the AgNPs. In conclusion, differences in mRNA responses to AgNPs between Caco-2 and MCF-7 cells were mainly related to timing and magnitude, but not to a different underlying mechanism.

Characterization of titanium dioxide nanoparticles in food products: analytical methods to define nanoparticles.

Titanium dioxide (TiO2) is a common food additive used to enhance the white color, brightness, and sometimes flavor of a variety of food products. In this study 7 food grade TiO2 materials (E171), 24 food products, and 3 personal care products were investigated for their TiO2 content and the number-based size distribution of TiO2 particles present in these products. Three principally different methods have been used to determine the number-based size distribution of TiO2 particles: electron microscopy, asymmetric flow field-flow fractionation combined with inductively coupled mass spectrometry, and single-particle inductively coupled mass spectrometry. The results show that all E171 materials have similar size distributions with primary particle sizes in the range of 60-300 nm. Depending on the analytical method used, 10-15% of the particles in these materials had sizes below 100 nm. In 24 of the 27 foods and personal care products detectable amounts of titanium were found ranging from 0.02 to 9.0 mg TiO2/g product. The number-based size distributions for TiO2 particles in the food and personal care products showed that 5-10% of the particles in these products had sizes below 100 nm, comparable to that found in the E171 materials. Comparable size distributions were found using the three principally different analytical methods. Although the applied methods are considered state of the art, they showed practical size limits for TiO2 particles in the range of 20-50 nm, which may introduce a significant bias in the size distribution because particles <20 nm are excluded. This shows the inability of current state of the art methods to support the European Union recommendation for the definition of nanomaterials.

Presence of nano-sized silica during in vitro digestion of foods containing silica as a food additive.

The presence, dissolution, agglomeration state, and release of materials in the nano-size range from food containing engineered nanoparticles during human digestion is a key question for the safety assessment of these materials. We used an in vitro model to mimic the human digestion. Food products subjected to in vitro digestion included (i) hot water, (ii) coffee with powdered creamer, (iii) instant soup, and (iv) pancake which either contained silica as the food additive E551, or to which a form of synthetic amorphous silica or 32 nm SiO(2) particles were added. The results showed that, in the mouth stage of the digestion, nano-sized silica particles with a size range of 5-50 and 50-500 nm were present in food products containing E551 or added synthetic amorphous silica. However, during the successive gastric digestion stage, this nano-sized silica was no longer present for the food matrices coffee and instant soup, while low amounts were found for pancakes. Additional experiments showed that the absence of nano-sized silica in the gastric stage can be contributed to an effect of low pH combined with high electrolyte concentrations in the gastric digestion stage. Large silica agglomerates are formed under these conditions as determined by DLS and SEM experiments and explained theoretically by the extended DLVO theory. Importantly, in the subsequent intestinal digestion stage, the nano-sized silica particles reappeared again, even in amounts higher than in the saliva (mouth) digestion stage. These findings suggest that, upon consumption of foods containing E551, the gut epithelium is most likely exposed to nano-sized silica.

Distribution, elimination, and toxicity of silver nanoparticles and silver ions in rats after 28-day oral exposure.

We report the results of a 28-day oral exposure study in rats, exposed to <20 nm noncoated, or <15 nm PVP-coated silver nanoparticles ([Ag] = 90 mg/kg body weight (bw)), or AgNO(3) ([Ag] = 9 mg/kg bw), or carrier solution only. Dissection was performed at day 29, and after a wash-out period of 1 or 8 weeks. Silver was present in all examined organs with the highest levels in the liver and spleen for all silver treatments. Silver concentrations in the organs were highly correlated to the amount of Ag(+) in the silver nanoparticle suspension, indicating that mainly Ag(+), and to a much lesser extent silver nanoparticles, passed the intestines in the silver nanoparticle exposed rats. In all groups silver was cleared from most organs after 8 weeks postdosing, but remarkably not from the brain and testis. Using single particle inductively coupled plasma mass spectrometry, silver nanoparticles were detected in silver nanoparticle exposed rats, but, remarkably also in AgNO(3) exposed rats, hereby demonstrating the formation of nanoparticles from Ag(+)in vivo that are probably composed of silver salts. Biochemical markers and antibody levels in blood, lymphocyte proliferation and cytokine release, and NK-cell activity did not reveal hepatotoxicity or immunotoxicity of the silver exposure. In conclusion, oral exposure to silver nanoparticles appears to be very similar to exposure to silver salts. However, the consequences of in vivo formation of silver nanoparticles, and of the long retention of silver in brain and testis should be considered in a risk assessment of silver nanoparticles.