RFID-Enabled Temperature Tag Accuracy & Reliability Testing. Meals Ready to Eat (MRE) and First Strike Rations (FSR)
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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 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 Shelf Life Estimation Many possible algorithms for shelf life estimation For example, Arrhenius: 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 A typical shelf life plot for an imaginary product 1
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 pallet A snapshot after 12 hours of cooling Placement of Temperature Recorders in Produce Loads* FRONT BACK 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. *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 timetemperature 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 2
3 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 Prediction of Fruits and Vegetables Storage 8 days Shelf life depends on a multiplicity of variables and their changes type of fruit or vegetable environmental conditions packaging Storage days Postharvest history Maturity/ripeness at harvest Postharvest treatments (pre-cooling, quarantine treatments, fumigation, heat, ozone ) Temperature low, high, fluctuating Humidity low, high, fluctuating Shelf life can be limited by different things Appearance color, texture Flavor aroma, taste Nutritional value sugar content, vitamins, antioxidants Atmosphere oxygen, carbon dioxide Packaging packed, bulk + All factors combined 3
4 Methods to Predict Shelf Life 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) Bio-indicators direct use of a microbial culture that displays same temperature characteristics of the food spoilage organism (McKeen & Ross 1996) 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) 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, ) Each temperature-related quality curve is designed based on the shelf life limiting quality factor at that specific temperature Mango Quality rating (1-5) Tommy Atkins Palmer 2C 5C 12C 15C 20C 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 Green snap beans SSC (% DW) Ascorbic acid (mg/100g DW) Chlorophylls (mg/g DW) 'Opus' 'Leon' 60.0 LSD 0.05 = C LSD 0.05 = C 10C C 20C LSD 0.05 = 6.11 LSD 0.05 = LSD = 1.51 LSD 0.05 = 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 More than visual/tactile indicators need to be considered in determining the shelf-life and limiting quality factors Papaya SSC (% dry weight) Quality rating (1-5) days shelf 10C 42% reduction in SSC Ascorbic acid (mg/100g dry weight) Weight loss (%) % weight loss 48% reduction in AA C 5C 10C 15C 20C 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) 4
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 Pilot Process Map Information Flow Product Flow Contract Program tag, add Lot # and Start tag Strawberry Contract Shelf Life Farms Strawberry Contract Farms Automatic Reading of Tag into Facility Estimate Strawberry Farms Automatic Reading of Tag out of Facility Up to 48 hrs Real-time Data Stop tag and end consignment Retail Store 1 to 10 hrs CRITICAL: Batch Data Association of Retail Store RFID Tag ID to Up to 2 hrs Warehouse Pallet Batch Data FEFO License Plate Retail Store Recommendation from backroom to store shelf Grocer s Transportation Strawberry 3 rd Party Grocer s Supplier Transportation Perishable DC Regional DC Up to 24 hrs Up to 24 hrs Real-time Data hrs Real-time Data Batch Data Leg 1 Leg 2 Leg 3 Leg 4 Leg 5 Leg 6 Baseline Quality Shelf Life Shelf Life Score/Shelf FEFO Estimate Estimate Life Estimate Recommendation shipping to stores Strawberry Validation Tests Fruit were inspected to validate the quality prediction versus physical inspection At the DC: Test RFID Tag # Predicted Shelf-life = 0 Observed Test Shelflife = 0 Difference (hours) Timing of Model vs. Observed 1 DC 1 lb /28 13:30 10/28 23: before 1 DC 2 lb /28 13:30 10/28 16:30 3 before 2 DC 1 lb /29 17:00 10/29 18: before 2 DC 2 lb /30 13:00 10/30 7: after 3 DC 1 lb /31 14:00 10/31 6:00 8 after 3 DC 2 lb /31 13:00 10/31 16: before The worst case out of 6 tests was a 9.5-hour difference between predicted and observed (over a 7-day shelf life) Strawberry Validation Tests Each flat had a RFID temperature tag At the Retail Store: Test RFID Tag # Predicted Shelf-life = 0 Observed Test Shelflife = 0 Difference (Hrs) Timing of Model vs. Observed 2 Store 1 lb /29 17:00 10/28 23: after 3 Store 1 lb /30 11:00 10/30 14:00 3 before 3 Store 2 lb /31 5:00 10/31 11:00 6 before 4 Store 1 lb /26 16:30 10/27 0:30 8 before 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 5
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! Jeff Brecht: jkbrecht@ufl.edu Cecilia Nunes: mariacecilia@usf.edu J.P. Emond: jpemond@gmail.com Ismail Uysal: iuysal@usf.edu Questions? ~ 6
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