Applications of Predictive Microbiology in Seafood Safety

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

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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)

A database of microbial

behaviour in food

environments

http://www.combase.cc

ComBase Browser

ComBase Predictor

A database of microbial

behaviour in food

environments

http://www.combase.cc

University of Tasmania

Muchas gracias!

• Dr Andrea Moreno & Dr Fernando Mardones• Organizing Committee• Sponsors, and• for the opportunity to meet new colleagues