applications of predictive microbiology in seafood safety
TRANSCRIPT
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Applications of Predictive Microbiology
in Seafood Safety
Mark Tamplin
University of TasmaniaTasmanian Institute of Agriculture
Food Safety Centre
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Global Food Drivers
• Chronic illness
• Immunodeficiency
• Consumer behaviour difficult to change
Nutrition/Health
• Transformational in biology & nutrition
• Novel processing technologies
• Functional ingredients
• Nanotechnology
Science & Technology
• Contaminants
• Climate Change
• Resource conservation
Environment
• Complex global supply chains
• Traceability
• Physical contaminants
• Microbial contamination
• Chemical contaminants
• Economic adulterants
• Allergens
• GMOs
• Emerging hazards
• Biosecurity
• Nano safety
Safety
• 2050, 9 billion population
• Urbanisation
• Aging population
• Increased ability to pay for value-added products
Demographics
• Global sourcing
• Global sourcing of R&D
Globalization
• Increased scrutiny
• National vs International Standards
• New risk management approaches
Regulatory
• Larger than the biggest food processors
• Buying power
• Reduced margins affect systems downstream
Retailers
• Food safety
• Converging trends
• health
• convenience
• premium
• ethics
• Animal welfare
Consumer
Courtesy – Martin Cole
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Global sources of food (and contamination)
and Martin Cole
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4
0 200 10000Betweenness centrality
19981999200020012002200320042005200720072008
Food import-export ($-value) fluxes “The highway” József Baranyi, Zoltán Lakner, Mária M. Ercsey-Ravasz and Zoltán Toroczkai (personal Communication)
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Seafood Hazards
Source
Agents of Disease from Fish and Fish Products
Aquatic
toxinsParasites Virus Bacteria
Biogenic
aminesChemicals
Aquatic
environment
Ciguatera
Tetrodotoxin
PSP
ASP
DSP
Nematodes
Cestodes
Trematodes
C. botulinum E
(B and F)
V. parahaemolyticus
V. cholerae
V. vulnificus
Aeromonas spp.
Plesiomonas
Histamine
General
environment
L. monocytogenes
C. botulinum A
and B
Animal-
man-
resevoir
norovirus
hepatitis
A, B
rotavirus
S. aureus
Salmonella
Shigella
E. coli
Heavy
metals;
Pesticides
Antibiotics
EB: Enterobacteriaceae
Courtesy – Jeff Farber
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New Emerging Hazards
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Climate Change
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Climate Change
• Pushes species to their physiological limits
• Reduces host resistance to pathogensExample - increase in oyster Dermo disease
Tamplin and Karunsagar, 2013
Shifts in pathogen load
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• Rate of reactions doubles or triples for every 10oC
• Methylmercury uptake increases with temperature
(3-5% for every 1oC increase)
• Rate of mutation and other forms of genetic transfer
Climate change - temperature
water temp = bacteria = mutations = genetic transfer
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Vibrio species
0.01
0.1
1
10
100
1000
10000
100000
-5 0 5 10 15 20 25 30 35 Water temperature ( C)
V. p
ara
haem
oly
ticu
s d
en
sit
y
in o
ys
ter
(Vp
/g)
• Highly responsive to temperature (and salinity)
• Vibrio diseases are increasing, globally
• Outbreaks of V. parahaemolyticus• Example: 2004-2007- outbreak in Puerto Montt, Chile
• >7,000 cases
• O3:K6 serotype
• El Nino Southern Oscillation (ENSO)
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Predictive models are condensed knowledge - estimate microbial levels in the environment- predict growth/death of microbes after harvest- manage risk in supply chains
V. cholerae
<1% salt
V. vulnificus
1-2% salt
V. parahaemolyticus
2->3% salt
)()(
11)(
)(
max
max txx
tx
tq
tq
dt
dxm
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Research problem
Experimental design
Data analysis
Research publication
Technical Steps in Predictive Modelling
Data generation
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GR (log cfu/h)=-0.0146+0.0098T -0.0206L-0.2220D – 0.0013TL-
0.0392TD+0.0143LD +0.0001T2+0.0053L2+2.9529D2
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Interact with all end-users (define intended outcomes)
Determine necessary resources
Conduct the research
Social Steps in Predictive Modelling
Communicate with end-users
US Food Safety Modernization Act
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Case Study: Oyster supply chains
Vibrio parahaemolyticus
Crassostrea gigas (Pacific oyster)
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Vibrio parahaemolyticus
• Causes mild to moderate gastroenteritis
• Cold chain management is critical to ensure safety, quality and
market access.
• Predictive models can be integrated into supply chains to
evaluate and manage performance.
• No model existed for V. parahaemolyticus in Pacific oysters
(Crassostrea gigas).
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Techniques
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Domestic
104
102
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Producing a predictive model
• V. parahaemolyticus growth was measured from 4 - 30oC
• Growth (>15oC) and death rates (<15oC) determined
• Models tested (validated) against naturally-occurring Vp
0
1
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3
4
5
6
0 200 400 600
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0 50 100 150
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Producing a predictive model
• V. parahaemolyticus growth was measured from 4 - 30oC
• Growth (>15oC) and death rates (<15oC) determined
• Models tested (validated) against naturally-occurring Vp
0
1
2
3
4
5
6
0 200 400 600
0
1
2
3
4
5
6
7
0 50 100 150
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Models for V. parahaemolyticus growth and inactivation, and TVC growth
√growth rate = 0.0303 x (temperature - 13.37) R2= 0.92
ln inactivation rate = ln 1.81×10-9 + 4131.2 × (1/(T+273.15)) R2= 0.78
√growth rate = 0.0102 x (temperature + 6.71) R2= 0.92
Vp growth
Vp inactivation
TVC growth
Fernandez-Piquer et al., Appl. Environ. Microbiol. 2011
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4
6
8
10
12
14
16
18
20
22T
em
pe
ratu
re, °C
-10 0 10 20 30 40 50 60 70Time, hr
Harvest_loc
Storage_farm
transport_truckstorage_domestic
Storage_retail
Transport_domestic
Load Unload
from Madigan 2008
Sensitivity Analysis of Oyster Supply Chains
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4
6
8
10
12
14
16
18
20
22T
em
pe
ratu
re, °C
-10 0 10 20 30 40 50 60 70Time, hr
Harvest_loc
Storage_farm
transport_truckstorage_domestic
Storage_retail
Transport_domestic
Load Unload
from Madigan 2008
Sensitivity Analysis of Oyster Supply Chains
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~$23,000 ~$1.6 million
Refrigeration vs Spoilage Cost Scenarios
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http://vibrio.foodsafetycentre.com.au/
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Sydney Rock Oyster
(Saccostrea glomerata)
Pacific Oyster (Crassostrea gigas)
Unexpected discovery
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Case Study: Salmon supply chain
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Tasmanian salmon industry
• Tasmania provides 95% of salmonid products in Australia.• Domestic market access criteria for salmon products.
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Experimental Design – Spoilage (Microbial)
• Head-on Gutted
• 0 - 15°C
• Total Viable Count (TVC)
Salmo salar (Atlantic salmon)
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Experimental Design – Spoilage (Sensory)
Quality Index Metric(QIM)
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Listeria monocytogenes
• As demand increases for raw salmon (sushi, sashimi), so can the risk of listeriosis.
• Including the effects of microbial interventions that reduce spoilage bacteria (i.e. competitive inhibition).
http://schaechter.asmblog.org/.a/6a00d8341c5e1453ef01348647b483970c-800wi
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Secondary plot of TVC growth rates
√growth rate = 0.0071 x (temperature + 21.86) R2= 0.768
Churchill et al., Food Microbiol. 2015
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Secondary plot of QIM rates
√QIM rate = 0.019 x (temperature + 0.165) R2= 0.919
Churchill et al., Food Microbiol. 2015
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Secondary plot of Lm growth rates
√growth rate = 0.015 x (temperature + 4.1) R2= 0.995
Churchill et al., Food Microbiol. 2015
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Application of predictive models for consumer-direct delivery of salmon products
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Integrating Predictive Models in
Supply Chains
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Smart-Trace tag
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Export case
GPS SatIridium constellation
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
Smart-Trace
InternetInternet
Smart-Trace Server Supplier
Smart-Trace Container Network
• Self organizing, self healing
• Star and Mesh topologies
• 900MHz ISM band spread spectrum
• Close metal barrier tuned antennas
• Using Iridium
• Fully self-sufficient, independent
Smart-TraceSmart-TraceSmart-Trace
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refrigeratedstorage
country importwholesalestorage
retailstorage
consumer
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Log Vp/g=-2.05+ 0.097*tempwater+0.2*sal-0.0055*SAL2√growth rate = 0.0303 x (temp-13.37)
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ComBase Browser
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ComBase Predictor
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University of Tasmania
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Muchas gracias!
• Dr Andrea Moreno & Dr Fernando Mardones• Organizing Committee• Sponsors, and• for the opportunity to meet new colleagues