3/12/2013

1

Utilizing Dynamic Shelf Life Estimation for Smart Distribution of Food Products: FEFO versus FIFO

Jeff Brecht

University of Florida

Center for Food Distribution & Retailing

Cecilia Nunes, J.P. Emond and Ismail Uysal

University of South Florida

PROJECT: 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) and First Strike Rations (FSR)

We used wireless temperature sensors, remote monitoring (RFID), algorithms, and diagnostics to demonstrate that MRE and FSR 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 TestingAccuracy– 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

Shelf Life Estimation

Many possible algorithms for shelf life estimation

For example, Arrhenius:

A typical shelf life plot for an imaginary product

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

3/12/2013

2

Estimating the Inside Temperature of a Pallet from the Recorded Outside (Air)

Temperature

Air temperature changes more rapidly than product temperature inside a pallet of tightly packed food products

Product temperature lags behind air temperature changes during fluctuating temperature regimes imposed on pallets of FSR.

Comparison of air temp to actual and estimated (i.e., modeled) product temps inside a pallet of FSR.

Temperature profiles based on the estimated product temperature were used in shelf life calculations.

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 palletA snapshot after

12 hours of cooling

Placement of Temperature Recorders in Produce 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 unit2. 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.

*Based on Ph.D. research at UF by Cecilia Amador (now at Sensitech)

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

Completely validated for FSR 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

3/12/2013

3

Supply Chain Decision Support SystemMaking 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)

Storage 8 days

Shelf Life Prediction ofFruits and Vegetables

Storage 12-16 days

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…

3/12/2013

4

Methods to Predict Shelf Life

Predictive microbiology based on microbial growthsensory 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 assume 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 same temperature characteristics of 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 assumed to be a measure of average biological aging or development pattern: 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 18 20

Time (days)

0 2 4 6 8 10 12 14 16 18 20

Qu

ality

rat

ing

(1-5

)

1.0

2.0

3.0

4.0

5.0

2C5C12C15C20C

Tommy Atkins Palmer

2°C CI or CI & softening

5°C Softening

12°C Softening, color changes & decay

15°C Softening & color changes or softening & decay

20°C Softening & color changes and decay or softening & color changes

Each temperature-related quality curve is designed based on the shelf life

limiting quality factor at that specific temperatureMango

SS

C (

% D

W)

18.0

24.0

30.0

36.0

42.0

48.0

54.0

60.0

1C5C10C15C20C

Asc

orb

ic a

cid

(m

g/1

00

g D

W)

30.0

45.0

60.0

75.0

90.0

105.0

120.0

135.0

150.0

Time (days)

0 1 2 3 4 5 6 7

Ch

loro

phyl

ls (

mg

/g D

W)

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

Time (days)

0 1 2 3 4 5 6 7

'Opus' 'Leon'

LSD0.05 = 1.21 LSD0.05 = 1.19

LSD0.05 = 6.11 LSD0.05 = 7.19

LSD0.05 = 1.51 LSD0.05 = 1.02

Soluble solids (SSC), total ascorbic acid (AA) and total chlorophylls contents of snap beans stored at chilling (1 and 5°C) or non-chilling (10, 15 and 20°C) temperatures

When snap beans reach their minimum acceptable sensory quality, reductions in SSC and AA content are already considerable

Green snap

beans

Time (days)

0 2 4 6 8 10 12 14 16

Asc

orb

ic a

cid (

mg/1

00g d

ry w

eig

ht)

300.0

400.0

500.0

600.0

700.0

800.0

Time (days)

0 2 4 6 8 10 12 14 16

Qualit

y ra

ting (

1-5)

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0C5C10C15C20C

Time (days)

0 2 4 6 8 10 12 14 16

Weig

ht lo

ss (

%)

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

SS

C (

% d

ry w

eight)

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 Modelling 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)

3/12/2013

5

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 Test

FEFORecommendation from backroom

to store shelf

Retail Store

FEFORecommendation shipping to stores

Contract Strawberry

FarmsContract

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 hrsBatch 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

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

3/12/2013

6

Conclusions

Tag accuracy varies for different temperatures (incorporate context-based accuracy in model)

Product temperature changes lag behind air temperature changes and are not uniform within a load

Use temperature mapping to relate air temperature measurements to product temperatures in different locations and to choose the (fewest) best tag locations

Consider all possible shelf life limiting quality factors over a wide temperature range for each specific product

A dynamic shelf life modeling system accommodates real-world fluctuating temperature conditions

Accurate determination of initial product quality is crucial

Thanks for your attention!

Questions?

~

Jeff Brecht: jkbrecht@ufl.edu

Cecilia Nunes: mariacecilia@usf.edu

J.P. Emond: jpemond@gmail.com

Ismail Uysal: iuysal@usf.edu