Reducing Strawberry Waste via Intelligent Distribution

Reducing Strawberry Waste and Losses
in the Postharvest Supply Chain via
Intelligent Distribution Management
Jeff Brecht and Francisco Loayza*
University of Florida, Center for
Food Distribution & Retailing
Ismail Uysal, Cecilia Nunes, and
Ricardo Badia*
University of South Florida
National Strawberry Sustainability
Initiative Grants Project Meeting,
Fayetteville, AR, May 21-22, 2014
J. P. Emond, CEO, Illuminate, LLC
Jeff Wells, CEO, Franwell, Inc.
Jorge Saenz, Dir. Cold Chain,
Hussman, Inc.
Gary Campisi, Sr. Dir. Quality
Control, Walmart
*Graduate students

BACKGROUND:
Remote Environmental Monitoring
and Diagnostics in the Perishables
Supply Chain*
Goal: Identify sensor-equipped RFID
technology and develop automated knowledge
system capability to determine the remaining
shelf life of operational rations in the DoD supply
chain based on remotely monitored temperature
history.
*Contracts W911QY-08-C-0136 and W911QY-11-C-0011; U.S. Army Natick
Research, Development, and Engineering Center

Meals Ready to Eat (MRE), First Strike
Rations (FSR), and Fresh Fruits &
Vegetables (FFV)
We used wireless temperature sensors, remote monitoring
(RFID), algorithms, and diagnostics to demonstrate that
shelf life can be automatically calculated in real time using
web-based computer models.
Temperature data collection using commercially available
RFID tags and commercial handheld readers.

RFID-Enabled Temperature Tag
Accuracy & Reliability Testing
Accuracy
– Range Span Test
– Extended Requirement Limit Test
– Freezing Temperature & Recovery Test
– Two-Point Swing Test
Reliability
– Truck, Rail & Air Mode Vibration Test
(different temperature profiles)
– Sine Mode Vibration Tests
– Read Range Test
– Context-Based Temp Accuracy Metric

Best (Fewest) Tag Locations to Accurately
Estimate Product Temperatures
Temperature variation within pallets, trailers, containers
and warehouses results in shelf life variation
Effective shelf life estimation requires temperature
mapping Representations of the temperature
distribution within a FSR pallet
A snapshot after
12 hours of
cooling

Placement of Temperature
Recorders in FFV Loads
2 1
4 3
6 5
8 7
10 9
12 11
14 13
16 15
18 17
20 19BACK
FRONT Three temperature monitors:
1. Inside the first pallet near the front
bulkhead of the reefer unit
2. Inside a pallet near the center of the load
(position 9, 10, 11, or 12)
3. On the outside rear face of the last pallet
at eye level. If only one temperature
recorder is being used, place it here.
Do not place temperature recorders
directly on trailer walls.

Shelf Life Estimation versus Tag
Accuracy
Tag accuracy varies with temperature
– For the most accurate shelf life estimation, tags
need to be most accurate in the temperature
range in which shelf life changes most rapidly
Thus, “context-based accuracy” (CBA) was
developed for shelf life modeling
– Improves shelf life estimation accuracy by
amplifying the effect of sensor error at
temperatures around which shelf life changes
rapidly

Shelf Life Estimation Model
A flexible model in complexity and accuracy
– Can work with mobile computers with low CPU power
– Increase in complexity and accuracy for
computers/servers with more CPU power
Complex learning model - yet simple operation
Can include multiple environmental factors as
needed such as temperature, humidity, etc. in
calculating product quality
Validated for different time-temperature profiles

Supply Chain Decision Support System
All sensory information available on the cloud
accessed through a web application
Each time an RFID temperature tag is scanned
by a reader:
– its location in the supply chain,
– its temperature records and,
– estimated product quality and shelf life
are recorded on a remote server
The web application also has decision making
and simulation capabilities with FIFO and FEFO

Supply Chain Decision Support System
Making logistics decisions using information from quality
parameters and shelf life models allows those decisions
to be based on a “First Expired, First Out” (FEFO)
model instead of “First in, First Out (FIFO)

Shelf life depends on a multiplicity of
variables and their changes…
– type of fruit or vegetable
– environmental conditions
– packaging

Temperature low, high, fluctuating
Humidity low, high, fluctuating
Atmosphere oxygen, carbon dioxide
Packaging packed, bulk
Postharvest history
Postharvest treatments (pre-cooling, quarantine
treatments, fumigation, heat, ozone…)
+ All factors combined
Maturity/ripeness at harvest

Shelf life can be limited by different
things…
– Appearance color, texture…
– Flavor aroma, taste
– Nutritional value sugar content, vitamins, antioxidants…

Storage for 8 days
Shelf Life Prediction of
Fresh Fruits and Vegetables
0 °C
5 °C
10 °C
15 °C
20 °C

Shelf Life Modeling
Many possible algorithms for shelf life estimation
For example, Arrhenius:
A typical shelf life plot for an imaginary product

Methods to Predict Shelf Life
Predictive microbiology based on microbial growth.
Sensory quality limits the shelf life and not microbial growth (Labuza & Fu 1993;
Riva et al. 2001; Jacxsens et al. 2002; Sinigaglia et al. 2003; Corbo et al. 2006).
Time-temperature indicators use chromatic variation
that depends on temperature-time exposure and assumes a relationship
with the loss of quality. Monitors the temperature history in response to the
cumulative effect of time and temperature (Wells & Singh 1988; Riva et al. 2001; Giannakourou
& Taoukis 2003).
Bio-indicators direct use of a microbial culture that displays the
same temperature characteristics as the food spoilage organism (McKeen &
Ross 1996).

Methods to Predict Shelf Life
Respiration rate by measuring the oxygen consumed and the
carbon dioxide released, but not appearance, texture or composition
(Rieblinger et al. 1977).
Changes based on single quality factors are
assumed to be a measure of average biological aging or development
patterns: firmness (Rieblinger et al. 1977; Aggarwal et al. 2003), color (Ishikawa & Hirata
2001; Hertog 2002; Schouten et al. ; Hertog et al. 2004), shriveling (Hertog 2002)
Changes based on multiple quality
factors as a function of individual commodity characteristics,
handling temperature, humidity, temperature & humidity and time
(Nunes and colleagues, 2001-2012)

Time (days)
0 2 4 6 8 10 12 14 16
Ascorbicacid(mg/100gdryweight)
300.0
400.0
500.0
600.0
700.0
800.0
Time (days)
0 2 4 6 8 10 12 14 16
Qualityrating(1-5)
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0C
5C
10C
15C
20C
Time (days)
0 2 4 6 8 10 12 14 16
Weightloss(%)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Time (days)
0 2 4 6 8 10 12 14 16
SSC(%dryweight)
50.0
60.0
70.0
80.0
90.0
100.0
13 days
shelf life
@ 10C
4%
weight
loss
42%
reduction
in SSC
48%
reduction
in AA
Papaya
More than visual/tactile indicators need to be considered in
determining the shelf-life and limiting quality factors

Modeling to predict shelf life
The ultimate goal of Modeling is to
provide reliable predictions of occurrences
that have not yet taken place, for any
product, from any source and in any
situation.” (Tijskens and Luyten, 2003)

Modeling to predict shelf life
Challenge Predict shelf life of produce
throughout the distribution system non-constant
environmental conditions
Use data available and collect more data on quality changes based
on constant environmental conditions
Use time-temperature tracking technologies that allow a
constant monitoring of the environmental conditions during distribution (i.e., RFID)

Shelf Life Estimation Based on
Quality Curves
A dynamic versus a static system
– Accommodates real-world fluctuating temperature
conditions
– Polynomial trendlines chosen that result in the
strongest correlations
– Different quality curve equations are used for each
time step based on the limiting quality factor for that
temperature (interpolated for intermediate temps)
– The model predicts the final quality index and the
residual shelf life

Shelf Life Estimation Based on
Quality Curves
Different quality factors limit shelf life at different
temperatures
– Initial quality indices are measured to set the starting
point
– The shelf life limiting quality factors at different
temperatures are known for each product and
– Residual shelf life is based on calculated time to
reach a pre-defined lower threshold quality index
(The residual shelf life calculation can be based on the
current temperature or a future temperature regime)

Strawberry Validation Tests
Fruit were inspected to validate the quality prediction
versus physical inspection
At the DC:
The worst case out of 6 tests was a 9.5-hour difference
between predicted and observed (over a 7-day shelf life)
Test RFID
Tag #
Date/Time
Predicted Shelf-life =
0
Date/Time
Observed Test Shelf-
life = 0
Difference
(hours)
Timing of Model
vs. Observed
1 DC 1 lb. 500304 10/28 13:30 10/28 23:00 9.5 before
1 DC 2 lb. 500243 10/28 13:30 10/28 16:30 3 before
2 DC 1 lb. 500372 10/29 17:00 10/29 18:30 1.5 before
2 DC 2 lb. 500315 10/30 13:00 10/30 7:30 5.5 after
3 DC 1 lb. 500435 10/31 14:00 10/31 6:00 8 after
3 DC 2 lb. 500430 10/31 13:00 10/31 16:30 3.5 before

Strawberry Validation Tests
Each flat had a RFID temperature tag
At the Retail Store:
The worst case out of 4 tests was a 8-hour difference
between predicted and observed (over a 7-day shelf life)
FEFO decision making was estimated to result in
30% less shrink than FIFO
Test RFID
Tag #
Date/Time
Predicted Shelf-life =
0
Date/Time
Observed Test Shelf-
life = 0
Difference
(Hrs)
Timing of Model
vs. Observed
2 Store 1 lb. 500317 10/29 17:00 10/28 23:30 7.5 after
3 Store 1 lb. 500411 10/30 11:00 10/30 14:00 3 before
3 Store 2 lb. 500416 10/31 5:00 10/31 11:00 6 before
4 Store 1 lb. 500233 10/26 16:30 10/27 0:30 8 before

Strawberry Validation Test
FEFO
Recommendation
from backroom
to store shelf
Retail Store
FEFO
Recommendation
shipping to stores
Contract
Strawberry
Farms
Contract
Strawberry
Farms
Pilot Process Map
Contract
Strawberry
Farms
Strawberry
Supplier
Regional DC
Retail Store
Grocer’s
Perishable DC
3rd
Party
Transportation
1 to 10 hrs
Batch Data
Up to 48 hrs
Real-time Data
Up to 24 hrs
Real-time Data48 – 72 hrs
Batch Data
Up to 2 hrs
Batch Data
Up to 24 hrs
Real-time Data
Retail Store
CRITICAL:
Association of
RFID Tag ID to
Warehouse Pallet
License Plate
Program tag, add Lot # and Start tag
Automatic Reading of Tag into Facility
Automatic Reading of Tag out of Facility
Stop tag and end consignment
Information Flow
Product Flow
Leg 1 Leg 2 Leg 3 Leg 4 Leg 5 Leg 6
Grocer’s
Transportation
Baseline
Quality
Score/Shelf
Life Estimate
Shelf Life
Estimate
Shelf Life
Estimate
Shelf Life
Estimate

Final Points
To model FFV shelf life, consider all possible
shelf life limiting quality factors over a wide
temperature range
Dynamic shelf life modeling accommodates real-
world fluctuating temperature conditions
Accurate determination of initial product quality
is crucial
To complete our project, we need to confirm that
our shelf life model accurately predicts
strawberry quality at the store level

Thanks for your attention!
Questions?
This project was funded by a grant from the Walmart Foundation and
administered by the University of Arkansas System, Division of Agriculture,
Center for Agricultural and Rural Sustainability.

Reducing Strawberry Waste via Intelligent Distribution

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