Risk management tool to define a corrective storage to enhance Salmonella inactivation in dry fermented sausages

Abstract

The resistance of Salmonella to the harsh conditions occurring in shelf-stable dry fermented sausages (DFS) poses a food safety challenge for producers. The present study aimed to model the behaviour of Salmonella in acid (with starter culture) and low-acid (without starter culture) DFS as a function of aw and storage temperature in order to build a decision supporting tool supporting the design of a corrective storage strategy to enhance the safety of DFS. Salmonella spp. were inoculated in the raw meat batter at ca. 6 Log cfu/g with a cocktail of 3 strains (CTC1003, CTC1022 and CTC1754) just before mixing with the other ingredients and additives. After stuffing, sausages were fermented and ripened following industrial processing conditions. Different drying-times were applied to obtain three batches with different aw (0.88, 0.90 and 0.93). Afterwards, DFS were stored at 4, 8, 15 and 25 °C for a maximum of three months and Salmonella spp. were periodically enumerated. The Weibull model was fitted to Log counts data to estimate inactivation kinetic parameters. The impact of temperature and aw on the primary inactivation parameters was evaluated using a polynomial equation. The results of the challenge tests showed that Salmonella spp. levels decreased during storage at all the assayed conditions, from 0.8 Log (in low-acid DFS at 4 °C) up to 6.5 Log (in acid DFS at 25 °C). The effect of both aw and temperature was statistically significant. Delta (δ) parameter decreased by decreasing aw and increasing temperature, while the shape (p) parameter ranged from above 1 (concave) at 10 °C to below 1 at 25 °C (convex). A common secondary model for the p parameter was obtained for each type of DFS, acid and low-acid, indicating that acidification during the production of DFS affected the time for the first Log reduction (δ) during the subsequent storage, but not the overall shape (p parameter) of the inactivation. The developed models covered representative of real conditions, such as Salmonella contamination in the raw materials and its adaptation to the harsh processing conditions. The good predictive performance shown when applying the models to independent data (i.e. up to 80% of the predictions within the ‘Acceptable Simulation Zone’ for acid sausages) makes them a suitable and reliable risk management tool to support manufacturers to assess and design a lethality treatment (i.e. corrective storage) to enhance the Salmonella inactivation in the product before DFS are released to the market.