Projecttitel: Safer Food with Big Data: Use of microbiome markers to monitor contamination risks in pork processing
Projectnummer: AF-16160
Missie: Gewaardeerd, gezond en veilig voedsel
MMIP: Veilige en duurzame primaire productie (D3)
Looptijd: 2017 – 2020
Projectleider: Paul van den Wijngaard
Betrokken partijen: LTO Nederland, Vion, IBM, Thermo Fischer Scientific Prionics, Wageningen University and Research.
HACCP (Hazard Analysis and Critical Control Point)-based methods are successfully used in food production systems to prevent, reduce and eliminate identified hazards for microbial contaminations. Nevertheless, contaminations with food-borne pathogens, such as salmonella, STEC (shiga toxigene Escherichia coli), and Listeria monocytogenes, still occur. The environmental factors leading to the emergence and multiplication of these pathogens, even under HACCP regimes, are not known yet. The project ‘Safer Food with Big Data‘ aims to identify such environmental factors using an innovative approach based on microbial ecology measurements using high throughput DNA/RNA deep-sequencing technologies and big data-driven analyses. Ultimately these spatio-temporal correlation analyses will yield a set of microbial biomarkers that act as early warning signals. Such biomarkers can subsequently be measured by suitable high throughput and low-cost tests that can be applied in food processing plants (among others; sets of qPCRs and micro-arrays). To demonstrate the utility and effectiveness of such an approach in animal-derived food production systems, we will focus in this project on a specific part of this production chain which is known to be susceptible for microbial contamination, namely meat processing facilities (for pork and beef). The project will develop innovative tools that predict whether during meat processing, conditions arise that are favourable for the emergence and multiplication of food-borne pathogens. With this knowledge meat production plants can take early action to limit the risks for contaminations with food-borne pathogens by developed effective intervention strategies to prevent the actual contaminations of end-products from happening. The results of the project will also demonstrate the utility and effectiveness of the proposed “big data driven decision making” approach for other parts of the (animal-derived) food production chain, including the primary production sector. Ultimately, this may lead to the production of safer food, a more cost efficient food production, strengthening of the Dutch competitiveness in the global food market, and a possible reduction in the occurrence of food-borne infections.
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