Major carbohydrate component Major protein component Decision support tool for producers of advanced ready-to-eat foods to ensure safe, tasty and nutritious products Taran Skjerdal1, Girum Tadesse Tessema1, Tone Mathisen Fagereng1, and Cecilie From2 1Norwegian Veterinary Institute, Oslo, Norway; 2Matbørsen, Stokke, Norway Background Food business operators must make strategic and daily tradeoffs where food safety and quality are only some of the criteria. Results and discussions Decision support was desired by industry within topics related to multidisciplinary dilemmas like: The need of a high productivity per square meter, leading to a need of precooked materials to save space and time. The customer demand of different salads versus the probability of mistakes by differentiated production. The laboratory experiments indicated that the salad compositions, process and storage scenarios, could be used to estimate the pathogen level categories (growth, no growth and decline) by using the pH, the lactic acid bacteria content in the dressing, the storage temperature and the content of meat as input parameters (Figure 2). The data visualized how the salad composition could be adapted to improve the safety, how replacement of an ingredient could be done without compromising quality or safety, and which deviations that lead to a change of food safety category and thereby a need for corrective action (Table 1). Purposes To map where decision support is needed in industry To develop the food quality and safety parts of a prototype of a multidisciplinary decision support tool. Methods Three companies located in Norway, Italia and Ireland were visited by researchers for several days to identify the real industry challenges, how they dealt with the complexity in production, learn where deviations occurred, as well as how and when decisions were made in production of deli salads. Experiments were carried out in the lab with simulated industry products using potato salads with more than 40 variations of ingredient composition and storage scenarios, (Figure 1), inoculated with Listeria monocytogenes and Staphylococcus aureus. Major carbohydrate component Potato Spices Salt, Pepper, Sugar Vegetables Fresh chives, Pickled cucumber Processing (i) removing one or more of ingredients (ii) reducing the amount of ingredients (iii) substituting with other ingredients (iv) adding new ingredients Dressing Mayonnaise, Sour cream, Crème fraîche, Quark, Yoghurt, Buttermilk, Cottage cheese, Pesto, Oil, Vinegar Major protein component Chicken meat Storage Abuse time-temperature conditions Ingredients Table 1. Examples of scenarios compromising quality or safety with possible corrective actions. Scenarios Safety/quality/ effect Possible corrective actions Case 1-supply chain Precooked instead of fresh ingredient Rely on the supplier in terms of quality and safety - More focus on supply chain management - Adaptation of in-house HACCP Case 2-process/ composition Dressing with low fat dairy products or less amount of dressing - pH heterogeneity - more rapid growth of Lm due to less content of preserving LAB* - Mould formation on the potato at abuse temperature - Add more dressing - Add LAB or include parts of the sauce with dairy products containing preserving LAB - Shorten the shelf life - Pack in MAP Case 3-storage conditions Storage at abuse temperature - Increased growth potential of Lm in some cases - Less growth potential of Lm in case of presence of reserving LAB* - Mould formation - Add dressing with preserving LAB - Shorten shelf life - None - See above Figure 1. Process map for productions of 40 varieties of potato salad in the lab. Salad types Fate of pathogens at different storage conditions Salad 1 Partial inactivation ND Salad 2 100% inactivation Salad 3 Salad 4 Growth Salad 5 Salad 6 Salad 7 Salad 8 Salad 9 Survival Salad 10 Salad 11 Salad 12 Salad 13 Salad 14 Salad 15 Salad 16 Salad 17 Salad 18 Salad 19 Salad 20 Salad 21 Salad 22 Salad 23 Salad 24 Salad 25 Salad 26 Salad 27 Salad 28 Salad 29 Salad 30 Salad 31 Salad 32 Salad 33 Salad 34 Salad 35 Salad 36 Salad 37 Salad 38 Salad 39 Salad 40 Salad 41 Salad 42 *found in this study, may not be valid for other products and LABs. Significance The main significance of the work is identification of scenarios that may lead to changed growth patterns pathogens and some quality deviations, how they can be categorised and presented in an easy and rapid way for people with limited training in microbiology who have to make decisions. Green 100% inactivation Yellow Survival or Partial inactivation Red Growth Acknowledgments STARTEC (www.startec-eu.info) is funded by the European Commission FP7-KBBE.2011.2.4-01-safety and quality of ready-to-eat foods under grant agreement no 289262. Figure 2. User friendly visualization of salad formulations and pathogen levels during storages categorized in red (significant growth), yellow (no growth r slight reduction) and green (100 % inactivation) for the decision support tool.