Suchergebnisse

AbstractAn optimization model was used to gain insight into cost‐effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end‐of‐chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost‐effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost‐effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.

AbstractThe presence of hazards (e.g., contaminants, pathogens) in food/feed, water, plants, or animals can lead to major economic losses related to human and animal health or the rejection of batches of food or feed. Monitoring these hazards is important but can lead to high costs. This study aimed to find the most cost‐effective sampling and analysis (S&A) plan in the cases of the mycotoxins deoxynivalenol (DON) in a wheat batch and aflatoxins (AFB1) in a maize batch. An optimization model was constructed, maximizing the number of correct decisions for accepting/rejecting a batch of cereals, with a budget as major constraint. The decision variables were the choice of the analytical method: instrumental method (e.g., liquid chromatography combined with mass‐spectrometry (LC‐MS/MS)), enzyme‐linked‐immuno‐assay (ELISA), or lateral flow devices (LFD), the number of incremental samples collected from the batch, and the number of aliquots analyzed. S&A plans using ELISA showed to be slightly more cost effective than S&A plans using the other two analytical methods. However, for DON in wheat, the difference between the optimal S&A plans using the three different analytical methods was minimal. For AFB1in maize, the cost effectiveness of the S&A plan using instrumental methods or ELISA were comparable whereas the S&A plan considering onsite detection with LFDs was least cost effective. In case of nonofficial controls, which do not have to follow official regulations for sampling and analysis, onsite detection with ELISA for both AFB1in maize and DON in wheat, or with LFDs for DON in wheat, could provide cost‐effective alternatives.

AbstractHistorical data on food safety monitoring often serve as an information source in designing monitoring plans. However, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). Such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre‐set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

AbstractThis article develops a framework for assessing the implementation of standards in a context of multiple negative externalities resulting from meat production. The framework is applied to the introduction of the New Dutch Retail Standard in the Dutch broiler market, a standard on animal welfare initiated by the private sector with national coverage. The results show that this standard did not lower producer, retailer, and consumer welfare; rather, social welfare increased by approximately 300 million euros. The framework provides a sound scientific basis for an ex ante analysis on the market potential of multiple standards.JEL CLASSIFICATIOND62; Q13; Q18

Despite recommendations to release microbial resources to the community post-publication, the reality is far from satisfying. A workshop discussed the need for a coordinated and effective deposition policy for 'key' microbial strains and proposes a set of criteria to facilitate their deposition into public service collections. The majority of authors either contacted directly or during submission of manuscripts to several international, mainly European bacteriology journals agreed to this set of 'key strain' criteria and to the voluntarily deposition of resources into public resource centres. ; The authors thank additional members of the 2011 Braunschweig workshop: Jorg Overmann, Esperanza Garay-Auban, Peter Kampfer, Yohan Lecuona, James I Prosser, Ramon Rosello-Mora, Karl-Heinz Schleifer, and Kornelia Smalla. The workshop was an initiative of the European Consortium of Microbial Resource Centres (EMbaRC), supported by the European Commission's Seventh Framework Programme (FP7, 2007-2013), Research Infrastructures action, under the grant agreement No. FP7-228310. This communication received funding from the European Union's Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no 312251. Additionally, thanks go to all those attending the February 2014 MIRRI Heads of Collections meeting who participated in the discussions, including some of those above plus Pedro Crous, Edward Moore, Oleg Stupar, Chantal Bizet, Dominique Clermont, Rosa Aznar, Paul Devos, and Anna ...