Introducing the Rapid Alert Supply Network Extractor (RASNEX) tool to mine supply chain information from food and feed contamination notifications in Europe

Background

During food or feed contamination events, it is of utmost importance to ensure their rapid resolution to minimize impact on human health, animal health and finances. The existing Rapid Alert System for Food and Feed (RASFF) is used by the European Commission, national competent authorities of member countries and the European Food Safety Authority to report information on any direct or indirect human health risk arising from food or feed, or serious risks to animal health or the environment in relation to feed. Nevertheless, no methods exist to to collectively evaluate this vast source of supply chain information.

Methods

To aid in the extraction, evaluation and visualization of the data in RASFF notifications, we present the Rapid Alert Supply Network Extractor (RASNEX) open-source tool available from https://doi.org/10.5281/zenodo.4322555 freely. Among RASNEX’s functions is the graphical mapping of food and feed supply chain operators implicated in contamination events. RASNEX can be used during ongoing events as a support tool for risk analysis using RASFF notifications as input.

Results

In a first use case, we showcase the functionality of RASNEX with the RASFF notification on a 2017/2018 contamination event in eggs caused by the illegal use of fipronil. The information in this RASFF notification is used to visualize nine different flows of main and related food products. In a second use case, we combine RASFF notifications from different types of food safety hazards (Salmonella spp., fipronil and others) to obtain wider coverage of the visualized egg supply network compared to the first use case. Actors in the egg supply chain were identified mainly for Italy, Poland and Benelux. Other countries (although involved in the egg supply chain) were underrepresented.

Conclusions

We hypothesize that biases may be caused by inconsistent RASFF reporting behaviors by its members. These inconsistencies may be counteracted by implementing standardized decision-making tools to harmonize decisions whether to launch a RASFF notification, in turn resulting in a more uniform future coverage across European food and feed supply chains with RASNEX.

Toxicity Assay for Citrinin, Zearalenone and Zearalenone-14-Sulfate Using the Nematode Caenorhabditis elegans as Model Organism

To keep pace with the rising number of detected mycotoxins, there is a growing need for fast and reliable toxicity tests to assess potential threats to food safety. Toxicity tests with the bacterial-feeding nematode Caenorhabditis elegans as the model organism are well established. In this study the C. elegans wildtype strain N2 (var. Bristol) was used to investigate the toxic effects of the food-relevant mycotoxins citrinin (CIT) and zearalenone-14-sulfate (ZEA-14-S) and zearalenone (ZEA) on different life cycle parameters including reproduction, thermal and oxidative stress resistance and lifespan. The metabolization of the mycotoxins by the nematodes in vivo was investigated using HPLC-MS/MS. ZEA was metabolized in vivo to the reduced isomers α-zearalenol (α-ZEL) and β-ZEL. ZEA-14-S was reduced to α-/β-ZEL-14-sulfate and CIT was metabolized to mono-hydroxylated CIT. All mycotoxins tested led to a significant decrease in the number of nematode offspring produced. ZEA and CIT displayed negative effects on stress tolerance levels and for CIT an additional shortening of the mean lifespan was observed. In the case of ZEA-14-S, however, the mean lifespan was prolonged. The presented study shows the applicability of C. elegans for toxicity testing of emerging food mycotoxins for the purpose of assigning potential health threats.

Current development of microfluidic immunosensing approaches for mycotoxin detection via capillary electromigration and lateral flow technology

Mycotoxin contamination in the food chain has caused serious health issues in humans and animals. Thus, a rapid on-site and lab-independent detection method for mycotoxins, such as aflatoxins (AFTs), is desirable. Microfluidic chip based immunosensor technology is one of the most promising methods for fast mycotoxin assays. In this review, we cover the major microfluidic immunosensors used for mycotoxin analysis, via flow-through (capillary electromigration) and lateral flow technology. Sample preparation from different matrices of agricultural products and foodstuffs is summarized. The choice of materials, fabrication strategies, and detection methods for microfluidic immunosensors are further discussed in detail. The sensors application in mycotoxin determination is also outlined. Finally, future challenges and opportunities are discussed.

Interactive network analytical tool for instantaneous bespoke interrogation of food safety notifications

BACKGROUND

The globalization of food supply necessitates continued advances in regulatory control measures to ensure that citizens enjoy safe and adequate nutrition. The aim of this study was to extend previous reports on network analysis relating to food notifications by including an optional filter by type of notification and in cases of contamination, by type of contaminant in the notified foodstuff.

METHODOLOGY/PRINCIPAL FINDINGS

A filter function has been applied to enable processing of selected notifications by contaminant or type of notification to i) capture complexity, ii) analyze trends, and iii) identify patterns of reporting activities between countries. The program rapidly assesses nations' roles as transgressor and/or detector for each category of contaminant and for the key class of border rejection. In the open access demonstration version, the majority of notifications in the Rapid Alert System for Food and Feed were categorized by contaminant type as mycotoxin (50.4%), heavy metals (10.9%) or bacteria (20.3%). Examples are given demonstrating how network analytical approaches complement, and in some cases supersede, descriptive statistics such as frequency counts, which may give limited or potentially misleading information. One key feature is that network analysis takes the relationship between transgressor and detector countries, along with number of reports and impact simultaneously into consideration. Furhermore, the indices that compliment the network maps and reflect each country's transgressor and detector activities allow comparisons to be made between (transgressing vs. detecting) as well as within (e.g. transgressing) activities.

CONCLUSIONS/SIGNIFICANCE

This further development of the network analysis approach to food safety contributes to a better understanding of the complexity of the effort ensuring food is safe for consumption in the European Union. The unique patterns of the interplay between detectors and transgressors, instantly revealed by our approach, could supplement the intelligence gathered by regulatory authorities and inform risk based sampling protocols.

An indicator based 'traffic light' model to pro-actively assess the occurrence of mycotoxins in tree nuts

This paper proposes an indicator based 'traffic light' model as a tool to pro-actively assess the occurrence of mycotoxins in tree nuts. The model is built using a holistic approach and, consequently, uses indicators from inside and outside the tree nut production chain as the basic elements. Temperature and relative humidity (pre-harvest), harvest and drying, storage and transport conditions, crop variety and crop damage were selected as indicators. For these indicators, measurable sub-indicators (model parameters) and risk categories were defined. With these insights, a 'traffic light' model was proposed that indicates the possible risk of occurrence of mycotoxins with colour signals; 'red' indicates high risk, 'yellow' medium risk, and 'green' low risk. The current model is specified for aflatoxins in hazelnuts but can easily be adapted for other mycotoxins and/or tree nuts. Governmental organisations and the commercial sector may use such a model to anticipate on the potential presence of mycotoxins by proactive risk management.