Seafood Spoilage and Safety Predictor (SSSP) Version 3 from 2008
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1 Cold-Chain Management, 2-3 June, Bonn, Germany Seafood Spoilage and Safety Predictor (SSSP) Version 3 from 2008 Paw Dalgaard, Ole Mejlholm, Jette Emborg and Brian J. Cowan Seafood & Predictive Microbiology, National Institute of Aquatic Resources () Technical University of Denmark (DTU) - pad@aqua.dtu.dk
2 Seafood Spoilage and Safety Predictor (SSSP) Outline: Key features of SSSP v. 2 from Flexible time-temperature integration tool for spoilage and shelf-life evaluation Histamine formation in seafood - Morganella psychrotolerans (0-20 C) - M. psychrotolerans and M. morganii (0-44 C) Listeria monocytogenes and lactic acid bacteria - New growth and growth boundary models Conclusions and perspectives
3 Key features of SSSP relative rates of spoilage (RRS)
4 Key features of SSSP relative rates of spoilage (RRS) Shelf-life can be predicted at different temperatures when: 1. Shelf-life at a constant temperature is known 2. RRS at different temperatures can be predicted Shelf - life (T C) = Shelf - life at T RRS at ref T ( C) ( C) RRS-models can be used: Although spoilage microorganisms are not known Over wide ranges of storage temperatures Spencer og Baines (1964), Olley og Ratkowsky (1973)
5 Key features of SSSP relative rates of spoilage (RRS) RRS: Shelf-life at T ref ( C) divided by shelf-life at T C Ln (Relative rates of spoilage) Cooked and brined MAP shrimps E A a Fresh seafood - Tropical waters ~ 100 ~ 0.15 Fresh seafood - Cold waters Packed cold-smoked salmon Hot smoked and packed fish ~ 80 ~ 0.12 T min = -10 C ~ 61 ~ 0.09 ~ 20 ~ Temperature ( C) - Exponential model - Square-root model - Arrhenius model Dalgaard (2000)
6 Key features of SSSP microbial spoilage models
7 Key features of SSSP microbial spoilage models
8 Key features of SSSP time-temperature integration SSSP predicts the effect of product temperature profiles: Series of constant temperatures Data recorded by temperature loggers
9 Key features of SSSP an extensive help-menu SSSP help-menu: How to use SSSP (e.g. for time-temperature integration) Information about individual predictive models: - Model development and equations - Model evaluation and validation - Range of applicability (products, storage conditions) Data formats (import, export) Other predictive microbiology software
10 SSSP users SSSP has been available since January 1999 New versions in 2004, 2005 and 2008 SSSP is used by more than 3000 people/institutions from more than 100 different countries: Production and distribution of seafood : 33 % Seafood inspection : 19 % Research : 17 % Teaching : 13 % SSSP is free and available in 12 different languages
11 Seafood Spoilage and Safety Predictor (SSSP) Outline: Key features of SSSP v. 2 from Flexible time-temperature integration tool for spoilage and shelf-life evaluation Histamine formation in seafood - Morganella psychrotolerans (0-20 C) - M. psychrotolerans and M. morganii (0-44 C) Listeria monocytogenes and lactic acid bacteria - New growth and growth boundary models Conclusions and perspectives
12 Histamine formation in seafood Histamine fish poisoning (HFP) occurs world wide and is the most common finfish-borne human disease HFP is due to marine finfish with > mg histamine/kg Specific bacteria produce histamine in seafood - At above 0 C: Morganella psychrotolerans, Photobacterium phosphoreum - At above 7-10 C: Morganella morganii, Raoultella planticola EU regulation: mg histamine/kg Dalgaard et al. 2008
13 Histamine formation in seafood Models for histamine formation have been available for years: C: Frank et al. (1983) C: Frank & Yoshinaga (1987) Models for bacterial growth and histamine formation are new Histamine mg/kg Log (CFU/g) Histamine mg/kg Storage period (Days) Emborg & Dalgaard 2007 Storage period (Days)
14 Histamine formation in seafood SSSP v. 3 includes models for histamine formation in seafood
15 Histamine formation in seafood Chilled tuna Chilled cold smoked tuna
16 Histamine formation in seafood A combined M. psychrotolerans and M. morganii model allows growth and histamine formation to be evaluated for a wide range of temperatures 9 25 C 5 C 8 Log (cfu/g) Dashed lines : Histamine Solid lines : Bacteria Histamine (mg/kg) Storage period (Days) Emborg & Dalgaard submitted
17 Seafood Spoilage and Safety Predictor (SSSP) Outline: Key features of SSSP v. 2 from Flexible time-temperature integration tool for spoilage and shelf-life evaluation Histamine formation in seafood - Morganella psychrotolerans (0-20 C) - M. psychrotolerans and M. morganii (0-44 C) Listeria monocytogenes and lactic acid bacteria - New growth and growth boundary models Conclusions and perspectives
18 Listeria monocytogenes and lactic acid bacteria EU-regulation (EC 2073/2005) distinguish between ready-to-eat foods able or unable to support growth of Listeria monocytogenes Ready-to-eat foods Support growth Critical limit Comments None in 25 g - When produced Support growth 100 cfu/g - It must be documented that 100 cfu/g is not exceeded within the storage period Unable to support growth 100 cfu/g - Documentation - ph 4,4 or a w 0,92 - ph 5,0 and a w 0,94 - Shelf-life below 5 days
19 Listeria monocytogenes and lactic acid bacteria SSSP v. 3 includes extensive growth and growth boundary models for Listeria monocytogenes and lactic acid bacteria Mejlholm and Dalgaard (2007a,b) - J. food Prot, 70, and
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24 Conclusions and perspectives SSSP has stimulated the use of predictive models and time-temperature integration within the seafood sector SSSP v. 3 includes new seafood safety models for: - Time-temperature integration - Product development and quality control - Documentation of seafood safety Future developments of SSSP may include: - Product specific e-learning modules - Improved links to other freeware - pad@aqua.dtu.dk
25 Thanks Colleagues at Financial support Industry collaboration Last but not least I thank you for your attention
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