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.

Development of a European system for identification of emerging mycotoxins in wheat supply chains

This paper describes the results of a research project aimed at the conceptual development of an identification system for emerging mycotoxins in European feed and food supply chains of wheat. Basic requirements for such a system were addressed, including the selection of indicators, locating information sources, development of underlying model, and investigation of stakeholders' needs. The selection and ranking of key indicators for identifying emerging mycotoxins was based on a literature review, followed by a structured expert judgment study. The expert study was based on the Delphi technique and used a panel of 43 European experts. The Delphi procedure resulted in a selected set of 12 key indicators for each of three relevant stages of the wheat chain (cultivation; transport and storage; processing). For wheat cultivation, these were: relative humidity/rainfall, crop rotation, temperature, tillage practice, water activity in the kernels, crop variety, harvest conditions, changes in fungal populations, fungicide use, plant health, regional infection pressure and food safety awareness. For the majority of the selected indicators, information sources were identified in specific European countries. However, most sources are not readily accessible and particular data is lacking. A theoretical concept for a model underlying the envisioned emerging mycotoxin identification system has been developed. The model links the key indicators and their information sources to aid and promote identification of emerging mycotoxins. It manages different types of information sources and levels of available information. The needs of various stakeholder groups regarding the system were investigated by means of two empirical elicitation sessions. The results showed that all stakeholders considered that such a system would be beneficial. One major challenge expected in establishing the system is stakeholder commitment to sharing data and information. This requires great efforts in improving communication and interaction between the key stakeholders.

A conceptual model for identification of emerging risks, applied to mycotoxins in wheat-based supply chains

The research described in this paper focuses on identification of the most important indicators for emerging mycotoxins, starting from those produced by Fusarium fungi, in wheat-based feed and food supply chains, as well as the development of a conceptual model to predict the occurrence of these emerging toxins, based on the selected indicators. The selection of the most important indicators was based on a literature review and evaluation of the resulting indicators for their relevance. Each indicator selected was appointed to relevant stage(s) of the supply chain to which it is related and, for each indicator, a suggestion for a potential information source is given. The selected indicators cover various influential sectors, amongst others, weather conditions, agronomical practices, trade and legislation, as well as a variety of information sources, e.g. on-farm records and statistical organisations. The conceptual model developed is aimed at predicting the occurrence of emerging mycotoxins – based on the selected indicators – in a particular unit of wheat. The model takes a supply chain approach and can handle various types of indicators and various levels of detail of information on origin of the unit of wheat. The proposed model could be useful in the development of an identification system for emerging risks related to mycotoxins in wheat-based supply chains. Ultimately, such a system will help industry and policy makers in their decision-making process with regard to prevention and control of upcoming risks.