cold chain management tools for the optimization of...

COLD CHAIN MANAGEMENT TOOLS FOR THE OPTIMIZATION OF READY-TO-EAT FOOD

PRODUCTS COLD CHAIN

Petros Taoukis, Eleni Gogou, Graciela Alvarez

Laboratory of Food Chemistry and Technology, School of Chemical Engineering, National Technical

University of Athens, Greece

National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA),

Antony, France

Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens

6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany



Food cold chain in … numbers

Cold chain management tools & Temperature control throughout the cold chain

Reducing food food waste in post harvest/post processing of foods

To minimize perishable foods that are lost before consumption

Post-harvest/processing food waste:

about 25% of the food production worldwide

The main shelf-life determining post-processing parameter in

chilled/frozen food products is temperature

60% the food we consume is chilled

10% the food we consume is frozen



Cold Chain Challenges

Food product temperature abuse

Shelf-life labelling

Food waste

Energy consuming technologies

Environmental impact



Taking cold chain’s temperature

Assumptions

“What if” scenarios

Regulations

How weak is the cold chain

Which stage is the weakest link

What is the impact on food quality

and shelf life



Develop a comprehensive database of the cold chain in Europe

Monitoring the food cold chain through focused field tests in Europe

Assess food quality at different stages of the supply chain

Develop cold chain management tools

Cold Chain Database development

FRISBEE: European Union funded 4-year Project (2010-2014)Food Refrigeration Innovations for Safety, consumers’ Benefit, Environmental impact and Energy optimization along the cold chain in Europe



Cold Chain Database development

Cold Chain Data Collection

Data from all stages of the cold chain (from production to consumption) were collected along the

supply chain for products in different European regions.

Consortium own data

Published data

Industry and cold chain parties (distributors, retailers)

Associations

Research projects

Data collection progress…

10/2011 04/2012

3.230 profiles

4.430 profiles

On going…

5.560 profiles

11/2012

8.000 profiles

03/2013

10.200profiles

01/2014

16.200profiles

05/2016



Where can I find the Cold Chain Database?

www.frisbee-project.eu

www.frisbee-project.eu/coldchaindb

http://www.frisbee-project.eu/

http://www.frisbee-project.eu/coldchaindb



Cold Chain Database



Cold Chain Database….in numbers!

Vegetables

(3%) Fruit and fruit

products

(2%)

Mixed

(3%)

Meat and

meat products

(43%)

Fish and fish

products

(3%)

Milk and milk

products

(26%)

Not defined

(20%)



Cold Chain Database….in numbers!

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35

40

% o

f th

e n

um

be

r o

f re

co

rds



Field test evaluation of the cold chain

France, Greece, Hungary, The Netherlands, UK

Focusing on perishable Ready-To-Eat (RTE)

chilled products

Vacuum packed smoked turkey slices, MAP or

cooked ham slices, other RTE

Field test evaluation of the Cold Chain in Europe

France

Greece

Hungary

UKThe Netherlands



FOOD PRODUCT

Product: Sliced cooked ham

Shelf life: 30 days

Two packages of 4 slices of ham/per package (sold together)

DATALOGGER

Mini Nomad RFID temperature logger

Omega Engineering Inc.

The data logger is placed between the two batches of

4 slices of ham and all is filmed (the recorder is hidden)

FIELD TEST CONSUMER REWARDRewarding the consumers with a 5 € supermarket voucher

Field Test Design-France



Field Test Photos-France



Field Test Design-France

Production/Production Warehouse

Supermarket

Transport by consumer

Domestic refrigerator

2

2

64

5

11

211

12

48

5

4Hypermarket


Transport by consumer

Conducted in November 2012

223 recorders were used for the field test

Distribution platform for supermarket

Distribution platform for hypermarket



FOOD PRODUCT

Product: Smoked turkey slices

Shelf life: 2 months

Packaging: An outside plastic transparent container within which the slices are placed

in vacuum packed (skin packed) in a second film

DATALOGGER

Mini Nomad RFID temperature logger

Omega Engineering Inc.

Field Test Design-Greece

FIELD TEST GIFT COUPONRewarding the consumers with a free product like the one they purchased and contained the logger.



Field Test Photos-Greece



Field Test Design-Greece

240 products, 24 supermarket stores, 12 cities in Greece

Field Test Cold Chain Stages

Production/Production Warehouse~12 hours

Consumer transport

Consumer domestic refrigerator

Supermarket Warehouse and Display24 supermarkets stores in 12 cities

Distribution Warehouse2 distribution centers



Transportation by consumer

Supermarketdisplay

Consumer domestic refrigerator

Transportation to supermarket

Distribution Platform

Field test results-Retrieved time-temperature profile

Return rate: ~40%Number of retrieved dataloggers in total (all countries): 350

Transportation to Distribution Center

Production Warehouse

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Time (days)

Tem

pe

ratu

re (

oC

)



Field test time temperature retrieved profiles

Temperature distributions

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0 2 4 6 8 10 12 14 16

Effective temperature (oC)

% P

ofi

les

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Production/Production warehouse

Transportation Distribution centre

Supermarket outlet Transportation by the consumer




Field tests results uploaded to the Cold Chain Database

www.frisbee-project.eu


http://www.frisbee-project.eu/




Visualize & manage your own data

Search within more than 16.000 profiles

of the Cold Chain Database

Cold Chain Database tools:






Build Cold Chain Scenario Profiles

Cold Chain Database tools: Build Cold Chain Scenario Profiles






Determine food product quality

along the cold chain

Cold Chain Database tools: Determine food product quality

Cold Chain Predictor Software



Cold Chain Predictor software

Cold Chain Predictor (CCP):Designed to simulate a cold chain by “building” a time-temperature history based on real

cold chain data contributed to the Cold Chain Database

Cold Chain Databasewww.frisbee-project.eu/coldchaindb

Real data on temperature conditionsthroughout the cold chain fromproduction to consumption for selectedfood product(s)




Cold Chain Predictor software

Cold Chain Predictor (CCP):Designed to simulate a cold chain by “building” a time-temperature history based on real

cold chain data contributed to the Cold Chain Database

CCP software runs Monte Carlosimulation using retrieved real time-temperature data found in the ColdChain Database

Cold Chain Databasewww.frisbee-project.eu/coldchaindb

Real data on temperature conditionsthroughout the cold chain fromproduction to consumption for selectedfood product(s)

Representative time-temperature profile of the cold chain for selected food products

Estimation of food products remaining shelf life at different stages of the cold chain


Cold Chain Database

&


Demonstration: RTE food products

Cooked ham case studyDetermining the product quality status and shelf life at the

different stages of the cold chain using





Cooked ham kinetic model development

Storage tests experiments

Data generation

Primary modeling

Secondary modeling

Kinetic modeldevelopment

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0 20 40 60 80 100

Storage time (days)

log(

cfu

/ml)

0C

5C

10C

15C

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7

8

9

0 20 40 60 80 100

Storage time (days)

log(

cfu

/ml)

0C

5C

10C

15C

Total microbial count

Lactic acid bacteria

Baranyi model (Baranyi & Roberts, 1994)

Kinetic parameters determination• Growth rate• Lag phase





Data generation

Primary modeling

Secondary modeling


Total microbial count


Determination of Arrhenius kinetic

parameters:

Growth rate at reference storage

temperature

Activation energy value (Ea)

0,00

0,20

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0,80

1,00

1,20

-0,15 -0,1 -0,05 0 0,05 0,1

(1/T-1/Tref)*103

Gro

wth

rat

e (

day

s-1)

0,00

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Gro

wth

rat

e (

day

s-1)





Data generation

Primary modeling

Secondary modeling


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Storage time (days)

log(

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/ml)

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15C


Lactic acid bacteria was identified as the specific spoilage organism (SSO)

Sensory results: spoilage level corresponding to the end of shelf life was 7.5 log(cfu/g)

Spoilage level



Kinetic model predicting the microbial growth of lactic acid bacteria in vacuum packed cooked ham as a function of storage temperature and time

Kinetic model predicting the remaining shelf life of vacuum packed cooked ham for a given storage temperature and time



Data generation

Primary modeling

Secondary modeling


ref

storage

refstorage

arefoF

Rk

tTTR

EkNN

SL

)16,273(

1

)16,273(

1exploglog

storage

refstorage

arefo t

TTR

EkNN

)16,273(

1

)16,273(

1exploglog

Developed kinetic models incorporated in the FRISBEE Tool

Software



Consumer refrigerator

Production Warehouse

Distributioncentre

Production

Supermarket outlet

Transportation to Distribution center


Transportation by the consumer

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Storage time (days)

Sto

rage

tem

epra

ture

(o

C)

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8

9

Log(

cfu

/ml)

Time-temperature controlled storage cabinets simulating the representative time

temperature profile generated by the Cold Chain Predictor Software

Storage of cooked ham samples

Kinetic models validation prior to software implementation



Time-temperature controlled

storage cabinets

Storage of cooked ham samples

Microbiological analysis

performed at predetermined

time intervals simulated to be

the different cold chain stages

Remaining shelf life estimation

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Storage time (days)

Sto

rag

e t

em

ep

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Re

ma

inin

g s

he

lf li

fe (

da

ys)

ref

storage

refstorage

arefoF

Rk

tTTR

EkNN

SL

)16,273(

1

)16,273(

1exploglog

Kinetic models validation prior to software implementation


Cold Chain Database

Retrieve time-temperature

profiles


Build representativeTime-temperature

profile

FRISBEE Cold Chain Management TOOLS

Remaining shelf lifeprediction


Cold Chain Database


profiles



profile

Monte Carlo simulation generates a representative time-temperature profilewhere each cold chain stage is represented by an isothermal step

The temperature of each cold chain stage represents the most probableeffective temperature of the t-T profiles for each stage of cold chain




Cold Chain Database


profiles



profile

Use the Representative t-T profile and predictive kinetic models




Cold Chain Database


profiles



profile

Use the Representative t-T profile and predictive kinetic models Remaining shelf life prediction




Cold Chain Database


profiles



profile

Use the Representative t-T profile and predictive kinetic models Remaining shelf life prediction Using what if scenarios: on temperature and/or time per chain stage




ICEF12 “Engineering challenges: bridging science and food innovations” Québec City, Canada, June 14-18, 2015

Taking into account the equivalent isothermal steps of representative t-T profile…

Shelf Life Remaining at the end of the cold chain: 8.9 days

Production site(2.9 days)

Distribution warehouse

(2 days)

Transportation(0.2 days)


(2 days)


Retail Display(10 days)

Transportation by consumer(0.05 days)


(25 days)

Tem

pe

ratu

reDetermining the product quality status and shelf life at the different stages of the cold chain-MAP cooked ham case study

However….

Temperature in each cold chain step

is actually a distribution of temperature values!

Tem

pe

ratu

re



(2 days)



(2 days)





(25 days)


ICEF12 “Engineering challenges: bridging science and food innovations” Québec City, Canada, June 14-18, 2015

Determining the product quality status and shelf life at the different stages of the cold chain-MAP cooked ham case study

Tem

pe

ratu

re



(2 days)



(2 days)





(25 days)

Taking into account the actual distributionof effective average temperature per stage throughout the cold chain…

The distribution of remaining shelf lifevalues can NOT be overlooked!

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-24

-17

-10 -3 3 10 17

Shelf Life Remaining (days)

Fre

qu

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cy

[%

]

Taking into account the actual distributionof effective average temperature per stage throughout the cold chain…



Cold Chain optimization: Reducing food waste

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Shelf Life Remaining (days)

Fre

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[%

]

Narrowing the distribution

Shifting the distribution to the right

Cold Chain Database

&


Demonstration: RTE food products

Fresh cut salads case studyDetermining the product quality status and shelf life at the

different stages of the cold chain using




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Storage time (days)

Lo

g(c

fu/g

)

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1000

10000

Qu

ino

ne

Te

xtu

re/C

olo

r

Pseudomonas

Texture: Burst strength

Color (DE)

Quinone

Sen

sory acce

ptab

le

10 days

Sensory rejected

Fresh cut salad-case study: Iceberg lettuce

Iceberg lettucePackaging: Modified atmosphere, 15% O2, 5% CO2

Storage temperature: 10°CPredictive models developed within SOPHY project

www.sophy-project.eu


http://www.sophy-project.eu/


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/Te

xtu

re b

urs

t s

tre

ng

th/C

olo

r


Color (DE)

Quinone

Pseudomonas

Sen

sory acce

ptab

le

7 days

Sensory rejected

Fresh cut salad-case study: Romaine lettuce

Romaine lettucePackaging: Modified atmosphere, 5% O2, 15% CO2

Storage temperature: 10°CPredictive models developed within SOPHY project

www.sophy-project.eu




Pseudomonas

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Storage time (days)

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g(c

fu/g

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1000

Te

xtu

re/C

olo

r/V

ita

min

C

Color (DE)


Vitamin C

Sen

sory acce

ptab

le

11days

Sensory rejected

Fresh cut salad-case study: Rocket

RocketPackaging: Aerobic conditionsStorage temperature: 10°C

Predictive models developed within SOPHY projectwww.sophy-project.eu





Retrieving real cold chain data from the Cold Chain Database

Building cold chain successive stages:1. Production site2. Transportation to Distribution warehouse3. Distribution warehouse storage4. Transportation to supermarket 5. Supermarket storage (retail display)6. Transportation (non refrigerated) by the consumer7. Consumer domestic refrigerator



Cold Chain Database: Retrieving temperature profile

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Production

Transportation to distribution warehouse



Supermarket display

Transportation by the consumer


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Cold Chain Database: Retrieving temperature profile



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Texture

Predictive models Spoilage & Quality

indicators



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indicators



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Pseudomonas


Texture

Color


indicators




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Pseudomonas


indicators



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Pseudomonas

Color


indicators



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Color

Pseudomonas

Quinone


indicators



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Texture

Pseudomonas

Color

Quinone


indicators


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Rem

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g sh

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(day

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Remaining shelf life estimation

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Re

ma

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sh

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Remaining shelf life of products stored at recommended temperature conditions

Remaining shelf life of products in the real cold chain

Recommended temperature conditions

Real temperature conditions


indicators



Related publications


Taoukis P.S., Gogou E., Tsironi T., Giannoglou M., Dermesonlouoglou E., Katsaros G. 2016. Chapter 16: Food Cold Chain Management and Optimization.


Contribute your own data to the Cold Chain Database-Join forces!

Organize & Manage your cold chain data

Visualize your data through Cold Chain Database tools

Meta data analysis

Get privileged access to the Database

Get privileged access to the Cold Chain Predictor Software


[email protected]



mailto:[email protected]

COLD CHAIN MANAGEMENT TOOLS FOR THE OPTIMIZATION OF READY-TO-EAT FOOD

PRODUCTS COLD CHAIN

Dr. Eleni Gogou

National Technical University of Athens

School of Chemical Engineering

Laboratory of Food Chemistry and Technology

Athens, Greece

[email protected]



mailto:[email protected]

cold chain management tools for the optimization of...

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