Spoilage of Seafood. Measuring Spoilage

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1 Development and use of models for bacterial growth and spoilage of seafood Shane Powell Spoilage of Seafood Fish products have a relatively short shelf-life due to: High post-mortem ph in the flesh Large amounts of non-protein nitrogen High content of polyunsaturated fatty acids Presence of autolytic enzymes Common Specific Spoilage Organisms (SSOs) include: Pseudomonas spp. Shewanella putrefaciens Photobacterium phosphoreum Serratia liquefacines Enterobacteriaceae spp. Lactic Acid Bacteria (LAB) such as Carnobacterium maltaromaticum 2 Measurements of Microbiological Chemical Physical Sensory characteristics Measuring Spoilage Microbiological methods plate counts total viable and selective agars for particular organisms Common Used as a proxy for fish freshness 10 5 CFU/g = acceptable and levels >10 7 CFU/g = unacceptable. Sensory methods Various schemes Quality Index Method related to days on ice In Australia Total Viable Counts (plate count) most common measurement 3 Models of bacterial growth during storage Food Spoilage and Safety Predictor FishMap ComBase Koutsoumanis Koutsoumanis, Konstantinos P., et al. "Applicability of an Arrhenius model for the combined effect of temperature and CO2 packaging on the spoilage microflora of fish." Applied and environmental microbiology 66.8 (2000): Mejholm Mejlholm, O and Dalgaard P. "Development and validation of an extensive growth and growth boundary model for psychrotolerant Lactobacillus spp. in seafood and meat products." International journal of food microbiology (2013):

2 Available Model Features Model Bacteria Range of conditions FishMap Food Safety & Spoilage Predictor Pathogen Model Program Carnobacterium, Serratia, Shewnella, Yersinia and mix Photobacterium phosphoreum Lactobacillus And other models plus others 0 100% CO 2 in N 2, Air 0 25 C 0 100% CO 2 Aerobic or anaerobic 4 37 C ComBase Aerobic to 100% CO C Koutsoumanis et al Pseudomonas spp. plus others Pseudomonas, Shewanella, lactic acid bacteria, Brochothrix Aerobic 0 15 C 0 100% CO 2 in air Growth medium Long & Hammer broth Generally broth Red mullet Validated in product Horse mackerel Salmon Plaice Cod Red mullet Seabream Boque Graphical interface Microbes and Atlantic Salmon Under aerobic storage conditions Pseudomonas spp Shewanella spp Under modified atmosphere conditions Photobacterium phosphoreum Lactic acid bacteria including Carnobacterium and Lactococcus 5 6 Spoilage, bacteria and seafood quality in Tasmanian salmon Develop models for bacterial growth under a range of storage conditions for different types of product Understand the relationship between bacterial growth and quality 7 Quality Index Method Fast and reliable Sensory attributes rated by panellists 0= Fresh 3= Spoilage Summed to give Quality Index (QI) score Linear relationship between time and QI score Three main attributes Appearance Odour Texture 8 2

3 Models for HOG fish stored in air Salmon kept in polystyrene boxes between 0 and 15 C At 0 C, layer of ice under salmon Temperature over 0 C beakers of water included to maintain moisture Sensory analysis performed first Samples taken at anterior dorsal region for microbiological counts TVC by pour plates on standard plate count agar incubated at 25 C for 48h QI = 0 QI= volunteers from TIA Training sessions Sensory Analysis Model Salmon sampled aseptically 6 panellists used QIM score sheet to judge the freshness of 2-4 fish at each session 1-2 QIM sessions per week Panellists were rewarded Ethics approval from Utas human ethics committee Inoculated with a cocktail of L. monocytogenes strains Incubated at 0, 5, 10 and 15 C and individual pieces removed for enumeration

4 Modified Atmosphere Packed Similar process except using commercially produced skin-on portions of Atlantic salmon QIM not appropriate Relationship between Sensory Scores and Microbial Numbers: aerobic Developed simplified test rating portions on appearance (colour, presence of slime) Odour Texture Acceptability score Relationship between Sensory Scores and Microbial Numbers: MAP Primary growth curves Secondary Model

5 Comparison to other models: aerobic Comparison to other models: Listeria Temperature C Days to reach 10 7 from 10 3 CFU/g This study (TVC) ComBase (Pseudomonas) Koutsoumanis (Pseudomonas) SSSP (MSM model) N/A FSSP Shewanella (air) Temperature ( C) Days for 2 log CFU/g increase Current model ComBase PMP (broth) FSSP (Chilled seafood) N/A N/A N/A Comparisons to other models: MAP Conditions Days to reach 10 7 from 10 3 CFU/g Current model Koutsoumanis Lactic acid bacteria FishMap Mixed 0 C % CO 2 10 C % CO 2 0 C % CO 2 10 C % CO 2 0 C % CO 2 10 C % CO 2 19 FSSP Photobacterium Summary for HOG salmon Strong relationship between sensory scores and bacterial numbers Pseudomonas spp numbers very similar to TVC Sulfide-producers (Shewanella) present although not detected reliably 20 5

6 Summary MAP Relationship between sensory scores and bacterial numbers Growth rate in presence of carbon dioxide lower than that observed in other studies Still not sure what organisms are most important in Tasmanian produced salmon 21 Model Comparisons Model Bacteria Range of conditions FishMap Food Safety & Spoilage Predictor Pathogen Model Program Carnobacterium, Serratia, Shewnella, Yersinia and mix Photobacterium phosphoreum Lactobacillus And other models 0 100% CO 2 in N 2, Air 0 25 C 0 100% CO 2 Aerobic or anaerobic 4 37 C ComBase Aerobic to 100% CO C Koutsoumanis et al Pseudomonas spp. Pseudomonas, Shewanella, lactic acid bacteria, Brochothrix Aerobic 0 15 C 0 100% CO 2 in air Current - MAP Total viable 0 10 C % CO 2 in N 2 Current - aerobic Total viable, Pseudomonas 0 10 C Air Growth medium Long & Hammer broth Red mullet Atlantic salmon Atlantic salmon Validated in product 22 Graphical interface Using Models If the process changes, what is the expected effect Intentional changes Assess effect of process changes Unintentional and Unexpected changes System failures Acknowledgements: Tasmanian Salmonid Growers Assoc Australian Seafood CRC Australian Maritime College All Tasmanian salmon producers