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1
IM SASIM SAS
Shelf life prediction by intelligent RFID - Technical limits of model accuracy
Jean-Pierre Emond, Ph.D.Associate Professor, Co-DirectorUF/IFAS Center for Food Distribution and RetailingUniversity of Florida
Reiner Jedermann Walter LangIMSAS Institute for Microsensors, -actuators and systemsMCB Microsystems Center BremenSFB 637 Autonomous Logistic ProcessesUniversity of Bremen
M CBM CB
Dynamics in Logistics
2
IM SASIM SASOutline
CFDR / University of Florida Evaluation of quality Case Study “Strawberries”
IMSAS / University Bremen Integration of quality models into embedded hardware Intelligent RFID Feasibility / required hardware resources
4
IM SASIM SASLaboratory evaluation of shelf life models
Several attributes have to be tested color firmness aroma / taste vitamin C
content
(Nunes, 2003)
5
IM SASIM SAS
Truck 1 - Front Pallet - Bottom
Wed 07/13 Thu 07/14 Fri 07/15 Sat 07/16 Sun 07/17 Mon 07/18 Tue 07/19
Tem
per
atu
re (
ºC)
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0AirProduct Temperature sensors were
placed inside and outside the load at all locations in the trailers
Quality was assessed from beginning to end
How retailers evaluate the quality of a shipment?
Joint project between Ingersoll-Rand Climate Control and UF
Economic impact of monitoring temperature and quality prediction
Strawberries – Case Study
6
IM SASIM SAS
RFID Temperature Tag + Prediction Models
= 3 full days
= 2 full days= 1 full day= 0 day
0
1
2
3
4
5
6
7
8
9
10
09/29/0517:45
09/30/0505:45
09/30/0517:45
10/01/0505:45
10/01/0517:45
10/02/0505:45
10/02/0517:45
10/03/0505:45
10/03/0517:45Time
Tem
pera
ture
(ºC
)
Air Temperature (ºC) - B Pulp Temperature (ºC) - B Air Temperature (ºC) - C
Pulp Temperature (ºC) - C Air Temperature (ºC) - T Pulp Temperature (ºC) - T
Strawberries – Case Study
7
IM SASIM SAS
= 3 full days
= 2 full days= 1 full day= 0 day
RFID + Models decision:
2 pallets never left origin2 pallets rejected at arrival5 pallets sent immediately for stores8 pallets sent to nearby stores7 pallets with no special instructions (remote stores)
Strawberries – Case Study
RFID Temperature Tag + Prediction Models
FEFO = First expires first out
8
IM SASIM SAS
Results at the store level (22 pallets sent)
Strawberries – Case Study
Days left
Number of pallets
Waste random
retail
Waste(RFID + Model)
(Recommendation)
0 2 91.7% (rejected) (don’t transport)
1 5 53 % (25%) (sell immediately)
2 8 36.7% (13.3%) (nearby stores)
3 7 10% (10%) (remote stores)
9
IM SASIM SAS
Actual RFID + Model
REVENUE $47,573 $58,556COST $49,876 $45,480
PROFIT ($2,303) $13,076
Strawberries – Case Study
Revenue and Profit
10
IM SASIM SASThe idea of intelligent RFID
Avoid communication bottleneck by pre-processing temperature data inside RFID
Temperature curve
Function to access effects of temperature
onto quality
Only state flag transmitted at read out
11
IM SASIM SASChain supervision by intelligent RFID
Step 1:
ConfigurationStep 2:
TransportStep 3: Arrival
Step 4:Post control
Full protocolList
• Temperature
• Shelf life
• Transport Info
Handheld Reader
Measures and stores temperature
Calculates shelf life
Sets flag on low quality
Reader gateManufacturer
12
IM SASIM SASModeling Approaches
Different model types
0
2
4
6
8
10
0 5 10 15 20
Temperature °C
Sh
elf
life
/ lo
ss in
day
s .
Shelf life(T)
Loss per Day
4.8 days shelf life at 6 °C
Reference temperature 6 °C
Tripple speed of quality decay at 14 °C
Activation energy for Lettuce
1
1
2
3
4
5
0 2 4 6 8Days
Tas
te
0 °C
5 °C
10 °C
15 °C
20 °C
Tables for different temperatures
Reaction kinetic model (Arrhenius)
Differential equation for bio-chemical processesd[P] / dt = −kPPO*[P]d[PPO] / dt = kPPO[P] − kbrown*[PPO]d[Ch] / dt = kbrown*[PPO]
13
IM SASIM SAS
0 5 10 15 20 250
2
4
6
8
10
12
14
16
18
20Color index for Mushrooms
Time in Days
Co
lor
ind
ex(
Sca
led
to 2
0 a
s in
itia
l va
lue
)
4 °C
8 °C12 °C
18 °C
Example Table Shift Approach
Only curves for constant temperature are known
How to calculate reaction towards dynamic temperature?
Interpolate over temperature and current quality to get speed of parameter change
Temperature Change from 12 °C to 4 °C
14
IM SASIM SASModel accuracy
Measurement tolerances Parameters like firmness or taste have high
measurement tolerances
Question: Is this table shift approach allowed? Yes, if all entailed chemical processes have the
similar activation energies (similar dependency to temperature)
Otherwise testing for the specific product required
15
IM SASIM SASSimulation
Comparison of reference model (Mushroom DGL) with table shift approach
Parameter tolerances 1 % and 5%
0 2 4 6 8 10 12 14 16 18 200
2
4
6
8
10
12
14
16
18
20
Time in Days
Te
mp
era
ture
°C
an
d c
olo
r in
de
x
Temperature °C
Diff. equation modelTable interpolation R=1%
Table interpolation R=5%
16
IM SASIM SASHardware Platforms
Wireless sensor nodes Tmode Sky from Moteiv Own development (ITEM)
Goal Integration into
RFID-Tag Comparable to RFID
data loggers
17
IM SASIM SASRequired Hardware Resources
Type of Resource
Calculation of Arrhenius equations
Look up table for Arrhenius
model
Table-Shift Approach
Processing time 1.02 ms 0.14 ms 1.2 ms
Program memory
868 bytes 408 bytes 1098 bytes
RAM memory 58 bytes 122 bytes 428 bytes
Energy 6 µJoule 0.8 µJoule 7 µJoule
18
IM SASIM SASAvailable Energy
Power consumption of model is not the issue
Multi parameter models are feasible on low power microcontroller
Reduce stand by current
Power consumption per month
Update every 15 minutes
(Table shift / 1 Parameter)
20 mJ / month
Stand by current of MSP430
(1µA at 2.2V)
5700 mJ / month
Typical battery capacities
Button cell 300 … 3000 J
Turbo Tag (Zink oxide battery) 80 J
19
IM SASIM SASSummary and Outlook
Case study (strawberries) showed the potential to reduce waste and increase profits
Quality evaluation of the level of RFID tags is feasible
Testing on existing hardware of sensor nodes Development of new UHF hardware required