TNO Quality of Life

Modern techniques to assessshelf life and safety

Erik Hoornstra

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Introduction

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• Principles of risk assessment• Quantitative risk assessment• Example• Risk assessment in TTI• Safety and shelf-life

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TNO Department Microbiology• Research during product and process innovation• Novel preservation strategies

• Testing and Validation• European Hygienic Engineering and Design Group

(EHEDG) certified test laboratory

• Trouble-shooting and emergencies (24 hour service)• Rapid detection tools• New developments: genomics & metabolomics

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The challenge for food companies

SafetyFreshness

ConvenienceShelf-life

Government FSO’sCustomer requirements

Consumer demandsCompany objectives

Criteria for:• Product• Process• Storage

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Question

What is safe food ?

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Why define safe foodEU• “The free movement of safe and wholesome food …”• “Food shall not be placed on the market if it is unsafe”• “A high level of protection of human life and health …”• “… free movement … only if food safety requirements do not differ

from Member State to Member State”• “The Community has chosen a high level of health protection …”

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Other aspects of defining safe foodEU• “It is necessary to ensure that consumers, other stakeholders and

trading partners have confidence in the decision making process …”• “… risk analysis provides a systematic methodology for the

determination of effective, proportionate and targeted measures to protect health …”

• “… scientific risk assessment alone cannot provide all the information on which a risk management decision should be based …societal, economic, traditional, ethical and environmental factors and thefeasibility of controls”

• Precautionary principle: risk is identified but unknown / uncertainty

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Who should define food safetyEU• “… the European Food Safety Authority should provide a

comprehensive independent scientific view of the safety of the food …”• “ EFSA should be able to commission scientific studies …”

Remember• Defining food safety is a political issue

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Safe foodEU• Unsafe = “Injurious to health”• Unsafe = “Unfit for human consumption”• “… normal conditions of use of the food by the consumer and at each stage

of production, processing and distribution”• “… information provided to the consumer … information on label …”• “… according to its intended use.”• “… any food which is unsafe is part of a batch … all food in that batch is

unsafe, unless … detailed assessment …”• “Traceability: Identify any person from whom they have been supplied …”

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Safe food – ALOP and FSO• ALOP = appropriate level of protection• FSO is maximum level of a hazard at the moment of consumption

Illness per million

log N / g

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0 1 2 3

ALOP

FSO

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Food safety policy

Appropriate level of protection

RA RM Risk analysisRC

Food Safety Objectives

Codes of practise, HACCP

Critical limits, specifications

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Safe food – Derived concepts from FSOPerformance objectives (in the food chain)• “Salmonella shall not exceed 1 cfu / 10 ml before distribution”• ‘Listeria absent per 25 gram in chilled foods (where it is able to grow)’

Performance criteria (change during a step)• “Assure a 12 log reduction of C. botulinum in low acid canned foods”• “Juice process should achieve 5 log reduction of E. coli and Salmonella”• “Avoid more than 3 log increase of S. aureus during meat fermentation”

Process criteria• 2.4 minutes 121°C for sterilisation• 15 seconds 71°C for commercial pasteurisation of milk• Storage temperature < 7 C

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Risk assessment at two levelsGovernments, EU

Public HealthPolicy

Food SafetyObjectives

Risk Assessment

Food companies

control measurescritical limits

own specificationsown targets

Company QualityPolicy

Hazard Analysis

Clients : requirements

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Risk analysis

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Hazard identificationGovernment• Epidemiology• (Public) opinion

Food company• Raw materials• Production process• Product: complaints, history 0

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Hazard characterizationGovernment• Dose-response relation

• Virulence of microorganism• Consumer sensitivity

Food company• Criteria

• Food Safety Objectives• Legislation• Specifications

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Exposure assessment

• Risk factors• Occurrence in raw materials• Growth• Inactivation• Mixing, portioning• Recontamination

• Quantification• Single point: worst-case• Single point: what if• Probability distribution functions

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Risk characterization

• Risk estimate: 0………1• Risk classes: f (occurrence, severity)• Risk profile• Prioritize control measures• Scenario analyses• Uncertainty and variability

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Learnings from first years of experience using QRA (negative)

• Quantitative risk assessment may be time consuming• Sometimes trivial results• Uncertainty about risk factors• Results difficult to understand by managers• Lack of data• Do we really understand what is going on

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Learnings from first years of experience using QRA (positive)

• Zero risk does not exist• Quantification gives a lot of insight• Uncertainty and variability• List most important risk factors• Scenario analysis for efficient improvements• Meaningful sampling

QRA

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Single point estimation vs.Probability distribution functions

10 x 10 = 100

1 x 10 = 10

5 x 10 = 50

20 x 10 = 200

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0,2

0,4

0,6

0,8

1

50 65 80 95

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0,02

0,04

0,06

0,08

0,1

0,12

0,14

4 6 8 10 12 14 16 18 20 22

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Single point calculationsExample: total travel time = bicycle + train + bus

minimum most likely maximum

5 10 20

35 40 60

10 15 20

Result 50 min. 65 min. 100 min.best case most likely worst case

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Probability distribution functions

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0,02

0,04

0,06

0,08

0,1

0,12

0,14

4 6 8 10 12 14 16 18 20 22

00,010,020,030,040,050,060,070,080,09

0,1

30 35 40 45 50 55 60 65

Bicycle Train

+ +0

0,020,040,060,08

0,10,120,140,160,18

0,2

8 10 12 14 16 18 20 22

Bus

Time in min

00,01

0,020,03

0,040,050,06

0,070,08

50 65 80 95

Total time

=

Average time 68 min

Time < 60 min 5.1 %

Time > 80 min 11 %

Time > 90 min 0.01 %

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Probability distribution functions

Cumulative probability function

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0,8

1

50 65 80 95

Total time

11% > 80 minutes

89 % ≤ 80 minutes

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0,010,02

0,030,040,050,060,070,08

50 65 80 95

89%

11%

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Sensitivity analysis

Train + 0.79

Bicycle + 0.50

Bus + 0.35

-1 -0.5 0 + 0.5 +1

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Monte Carlo simulation in @Risk

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Exposure assessment of E. coli O157:H7

0

0,005

0,01

0,015

0,02

0,025

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 0

0,005

0,01

0,015

0,02

0,025

0,03

17 18 19 20 21 22 23 24 250

0,005

0,01

0,015

0,02

0,025

0 2 4 6 8 10 12 14

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

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0,045

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Initial level N0 (log cfu/g)

Heating time (sec)

D-values (sec)

Numbers of E.coliafter heat treatment (log cfu / g)

Worst case situation

‘Average’ situation

_ :

=

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Risk characterisation of E. coli O157:H7

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0,005

0,01

0,015

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0,025

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0,035

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0,045

-20 -15 -10 -5 0 5

Log numbers of E.coli in amount of

consumption

Dose-response E.coli

=

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1 10 100 1000 10000 100000Dose (exposure)

Prob

abili

ty o

f illn

ess

Probability of illness after consumption

- Average, most likely, 90% confidence interval

- Probability of exceeding ALOP

X

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Risk assessment of E. coli O157:H7 in raw fermented sausage

- Pathogens can be present in raw meat

- Fermentation results in reduction of pathogens

- No additional heating step

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Risk assessment of E. coli O157:H7 in raw fermented sausage

contamination performance end product

• worst-case 1000 / g 5 D 1 / 100 g

• “what if” 1 / g 2 D 1 / 100 g3 D 1 on 10 with 1 / 100 g

If 1000 / g occurs in 0.1% of cases

If 1 / 100 g results in probability of illness of 0.1%5D results in risk of 1 in million

ACCEPTABLE ???

3D results in risk of 1 in 10,000

ACCEPTABLE ???

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Risk assessment in TTI

Initial level

- % positive

- numbers

Growth

Level at time of consumption

- % positive

- numbers

Faeces Carcass Meat

contamination

Slaughterhouse

contamination contamination

Storage

growth

Chill chain (distribution and retail)

Storage

growth

Heating Consumption

Consumer

inactivation

recontamination

intake

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Life animalsM

Slaughter plantTM

Carcasses

Distrib. centre

T

Microbiological data available

Temperature data available

Transport temperature not availableTT

M

ProportioningT

T

Retail/shop Refrig. consumerT TM

Tt

Cons. preparation

Ready to eat

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Occurrence of pathogens on raw meat

0 1 10 100

P (%)

Distribution 1: probability of positive

Distribution 2: numbers in case positive

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Temperature distribution in retail

Chain 1Chain 2Chain 3Chain 4

Chain 5

01234567

Ave

rage

tem

pera

ture

(°C

)Average temperatures in displays for fresh meat or

meat products in different supermarket chains

Fresh meatMeat products

Cooling display B1

-2

0

2

4

6

810

12

14

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9-08-038:00

9-08-0310:24

9-08-0312:48

9-08-0315:12

9-08-0317:36

9-08-0320:00

°C

right back side middle left front side

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Temperature distribution in households

1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10

10-11

11-12

0

2

4

6

8

10

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n re

frig

erat

ors

°C

Average temperatures consumer refrigeratorsn = 45

Lower trayUpper tray

Refrigerators with average temperatures above 7°C

-Upper tray 60%

-Lower tray 33%

-Overall 42%

-Upper and lower tray 33%

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Modelling to predict safety and shelf-life

Experimental Design Generation of data

Curve fitting Modelling

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From TTI to SMASTTI• Responds independent of initial level• Prediction: SSO increase 0 log, 3 log, 5 log• Prediction: pathogens increase not, factor 2, factor 10

SMAS• Remaining shelf-life is …• (Risk of spoilage is … % (depending on initial level))• Risk of illness is increased by factor ... • (Risk of illness is … % (depending on initial level and cooking, etc.)

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Life animalsM

Slaughter plantTM

CarcassesT

PortioningT

Distrib. centre

Retail/shop Refrig. consumerM

TtT T

Cons. preparation

Ready to eat

Temperature/time controlled byTTI-system

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Discussion about risk assessment• Define scope and objectives before risk assessment

• Use experts• product/process, microbiology, statistics

• Limit the models to relevant factors• raw materials, process, storage, (consumer)

• Verification of results with available data• microbiology, epidemiology