cold chain management tools for the optimization of...
TRANSCRIPT
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
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
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
Cold Chain Challenges
Food product temperature abuse
Shelf-life labelling
Food waste
Energy consuming technologies
Environmental impact
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
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
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
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
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
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
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
Where can I find the Cold Chain Database?
www.frisbee-project.eu
www.frisbee-project.eu/coldchaindb
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
Cold Chain Database
Cold Chain Database
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
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%)
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
Cold Chain Database….in numbers!
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% o
f th
e n
um
be
r o
f re
co
rds
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
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
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 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
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
Field Test Photos-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
Field Test Design-France
Production/Production Warehouse
Supermarket
Transport by consumer
Domestic refrigerator
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2
64
5
11
211
12
48
5
4Hypermarket
Domestic refrigerator
Transport by consumer
Conducted in November 2012
223 recorders were used for the field test
Distribution platform for supermarket
Distribution platform for hypermarket
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 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.
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
Field Test Photos-Greece
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
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
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
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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ratu
re (
oC
)
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
Field test time temperature retrieved profiles
Temperature distributions
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Effective temperature (oC)
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Production/Production warehouse
Transportation Distribution centre
Supermarket outlet Transportation by the consumer
Domestic refrigerator
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
Field tests results uploaded to the Cold Chain Database
www.frisbee-project.eu
www.frisbee-project.eu/coldchaindb
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
Visualize & manage your own data
Search within more than 16.000 profiles
of the Cold Chain Database
Cold Chain Database tools:
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
Visualize & manage your own data
Search within more than 16.000 profiles
of the Cold Chain Database
Build Cold Chain Scenario Profiles
Cold Chain Database tools: Build Cold Chain Scenario Profiles
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
Visualize & manage your own data
Search within more than 16.000 profiles
of the Cold Chain Database
Determine food product quality
along the cold chain
Cold Chain Database tools: Determine food product quality
Cold Chain Predictor Software
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
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)
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
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
&
Cold Chain Predictor Software
Demonstration: RTE food products
Cooked ham case studyDetermining the product quality status and shelf life at the
different stages of the cold chain using
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
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
Cooked ham kinetic model development
Storage tests experiments
Data generation
Primary modeling
Secondary modeling
Kinetic modeldevelopment
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Storage time (days)
log(
cfu
/ml)
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5C
10C
15C
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Storage time (days)
log(
cfu
/ml)
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5C
10C
15C
Total microbial count
Lactic acid bacteria
Baranyi model (Baranyi & Roberts, 1994)
Kinetic parameters determination• Growth rate• Lag phase
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
Cooked ham kinetic model development
Storage tests experiments
Data generation
Primary modeling
Secondary modeling
Kinetic modeldevelopment
Total microbial count
Lactic acid bacteria
Determination of Arrhenius kinetic
parameters:
Growth rate at reference storage
temperature
Activation energy value (Ea)
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(1/T-1/Tref)*103
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wth
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e (
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(1/T-1/Tref)*103
Gro
wth
rat
e (
day
s-1)
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
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)
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5C
10C
15C
Lactic acid bacteria
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
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
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
Cooked ham kinetic model development
Storage tests experiments
Data generation
Primary modeling
Secondary modeling
Kinetic modeldevelopment
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
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
Consumer refrigerator
Production Warehouse
Distributioncentre
Production
Supermarket outlet
Transportation to Distribution center
Transportation to supermarket
Transportation by the consumer
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Storage time (days)
Sto
rage
tem
epra
ture
(o
C)
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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
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
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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0 5 10 15 20 25 30 35 40 45
Storage time (days)
Sto
rag
e t
em
ep
ratu
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oC
)
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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
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
profile
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
profile
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
profile
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
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
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
profile
Use the Representative t-T profile and predictive kinetic models
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
profile
Use the Representative t-T profile and predictive kinetic models Remaining shelf life prediction
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database
Retrieve time-temperature
profiles
Cold Chain Predictor Software
Build representativeTime-temperature
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
FRISBEE Cold Chain Management TOOLS
Remaining shelf lifeprediction
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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)
Distribution warehouse
(2 days)
Transportation(0.2 days)
Retail Display(10 days)
Transportation by consumer(0.05 days)
Consumer refrigerator
(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
Transportation(2.9 days)
Distribution warehouse
(2 days)
Transportation(0.2 days)
Distribution warehouse
(2 days)
Transportation(0.2 days)
Retail Display(10 days)
Transportation by consumer(0.05 days)
Consumer refrigerator
(25 days)
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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
Transportation(2.9 days)
Distribution warehouse
(2 days)
Transportation(0.2 days)
Distribution warehouse
(2 days)
Transportation(0.2 days)
Retail Display(10 days)
Transportation by consumer(0.05 days)
Consumer refrigerator
(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
en
cy
[%
]
Taking into account the actual distributionof effective average temperature per stage throughout the cold chain…
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
Cold Chain optimization: Reducing food waste
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Narrowing the distribution
Shifting the distribution to the right
Cold Chain Database
&
Cold Chain Predictor Software
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
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
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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Lo
g(c
fu/g
)
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10000
Qu
ino
ne
Te
xtu
re/C
olo
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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
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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tre
ng
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olo
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Texture: Burst strength
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
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Pseudomonas
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Storage time (days)
Lo
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fu/g
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xtu
re/C
olo
r/V
ita
min
C
Color (DE)
Texture: Burst strength
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
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Fresh cut salad-case study: Rocket
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
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Cold Chain Database: Retrieving temperature profile
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6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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No
rmal
ize
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ual
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par
ame
ter
valu
e
Fresh cut salad-case study: Rocket
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Production
Transportation to distribution warehouse
Transportation to supermarket
Distribution warehouse
Supermarket display
Transportation by the consumer
Domestic refrigerator
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0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
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)
Cold Chain Database: Retrieving temperature profile
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
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No
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ize
d q
ual
ity
par
ame
ter
valu
e
Fresh cut salad-case study: Rocket
Texture
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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0,2
0,3
0,4
0,5
0,6
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Color
Fresh cut salad-case study: Rocket
Texture
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Pseudomonas
Fresh cut salad-case study: Rocket
Texture
Color
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Fresh cut salad-case study: Romaine lettuce
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Fresh cut salad-case study: Romaine lettuce
Pseudomonas
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Fresh cut salad-case study: Romaine lettuce
Pseudomonas
Color
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Fresh cut salad-case study: Romaine lettuce
Color
Pseudomonas
Quinone
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
No
rmal
ize
d q
ual
ity
par
ame
ter
valu
e
Fresh cut salad-case study: Romaine lettuce
Texture
Pseudomonas
Color
Quinone
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
0
2
4
6
8
10
12
14
16
18
0 1 2 3 4 5 6 7 8
Time (days)
Tem
per
atu
re (
oC
)
-1
0
1
2
3
4
5
6
7
8
9
10
Rem
ain
g sh
elf
life
(day
s)
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Remaining shelf life estimation
0
2
4
6
8
10
12
14
16
18
0 1 2 3 4 5 6 7 8Time (days)
Te
mp
era
ture
(oC
)
-1
0
1
2
3
4
5
6
7
8
9
10
Re
ma
ing
sh
elf
lif
e (
da
ys)
0
2
4
6
8
10
12
14
16
18
0 1 2 3 4 5 6 7 8Time (days)
Te
mp
era
ture
(oC
)
-1
0
1
2
3
4
5
6
7
8
9
10
Re
ma
ing
sh
elf
lif
e (
da
ys)
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
Predictive models Spoilage & Quality
indicators
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
Related publications
6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
Taoukis P.S., Gogou E., Tsironi T., Giannoglou M., Dermesonlouoglou E., Katsaros G. 2016. Chapter 16: Food Cold Chain Management and Optimization.
Laboratory of Food Chemistry & Technology School of Chemical Engineering - National Technical University of Athens
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6th International Conference Cold Chain ManagementTemperature Controlled LogisticsJune 6-7, 2016, University of Bonn, Germany
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
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