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Applications of Predictive Microbiology
in Seafood Safety
Mark Tamplin
University of TasmaniaTasmanian Institute of Agriculture
Food Safety Centre
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
Global sources of food (and contamination)
and Martin Cole
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)
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
New Emerging Hazards
Climate Change
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
• 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
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)
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
Research problem
Experimental design
Data analysis
Research publication
Technical Steps in Predictive Modelling
Data generation
GR (log cfu/h)=-0.0146+0.0098T -0.0206L-0.2220D – 0.0013TL-
0.0392TD+0.0143LD +0.0001T2+0.0053L2+2.9529D2
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
Case Study: Oyster supply chains
Vibrio parahaemolyticus
Crassostrea gigas (Pacific oyster)
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).
Techniques
Domestic
104
102
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
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
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
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
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
~$23,000 ~$1.6 million
Refrigeration vs Spoilage Cost Scenarios
http://vibrio.foodsafetycentre.com.au/
Sydney Rock Oyster
(Saccostrea glomerata)
Pacific Oyster (Crassostrea gigas)
Unexpected discovery
Case Study: Salmon supply chain
Tasmanian salmon industry
• Tasmania provides 95% of salmonid products in Australia.• Domestic market access criteria for salmon products.
Experimental Design – Spoilage (Microbial)
• Head-on Gutted
• 0 - 15°C
• Total Viable Count (TVC)
Salmo salar (Atlantic salmon)
Experimental Design – Spoilage (Sensory)
Quality Index Metric(QIM)
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
Secondary plot of TVC growth rates
√growth rate = 0.0071 x (temperature + 21.86) R2= 0.768
Churchill et al., Food Microbiol. 2015
Secondary plot of QIM rates
√QIM rate = 0.019 x (temperature + 0.165) R2= 0.919
Churchill et al., Food Microbiol. 2015
Secondary plot of Lm growth rates
√growth rate = 0.015 x (temperature + 4.1) R2= 0.995
Churchill et al., Food Microbiol. 2015
Application of predictive models for consumer-direct delivery of salmon products
Integrating Predictive Models in
Supply Chains
Smart-Trace tag
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
refrigeratedstorage
country importwholesalestorage
retailstorage
consumer
Log Vp/g=-2.05+ 0.097*tempwater+0.2*sal-0.0055*SAL2√growth rate = 0.0303 x (temp-13.37)
ComBase Browser
ComBase Predictor
University of Tasmania
Muchas gracias!
• Dr Andrea Moreno & Dr Fernando Mardones• Organizing Committee• Sponsors, and• for the opportunity to meet new colleagues
