MCB. RFID Temperature Sensors Improve Food Quality. Background. Autonomous logistics. Microsystems Center Bremen Microsensor and system applications

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1 Reiner Jedermann, Microsystems Center Bremen, University of Bremen RFID Temperature Sensors Improve Food Quality Background Microsystems Center Bremen Microsensor and system applications LogDynamicsLab RFID test facilities Global RF Lab Alliance Project study with University Florida University of Bremen MCB Autonomous logistics Automated freight supervision and transport coordination Sell the fruits on the left side first! SFB 7

2 RFID for the cool chain Beyond tracing and tracing Fright: Bananas Temperature:.5 C Quality Index: 8%? Combination with sensors for temperature and quality monitoring Write back to tag Proactive selfsupervising goods Outline Existing RFID sensor hardware Sensor application (passive HF) Temperature profiles of cool chain transports Food chain demands on UHFRFID New approaches for cool chain supervision Intelligent RFID Combination with active sensor systems 5 Current RFID sensor applications Passive Tags HF + UHF EPC Gen No sensors SemiPassive HFData loggers Battery for sensors Active Tags Wireless Sensor networks / Class To expensive for item level Miniaturized data loggers HFRFID loggers with electrical interface Required Accuracy: <<.5 C Tests in climatic chamber Standard deviation: / of all values are inside ±δ 7

3 Comparison of different logger types Temperature deviations in typical transports Type Data points Battery Resolution KSW 7 ± ~. C TurboTag 7 + ~. C ibutton ++.5 C Rungis Express CCG Holding AG and CCG FRA Tested Accuracy Interface Price ±. C RFID 5$ ±.8 C RFID 5$ < ±. C OneWire > $ Sealed Air Corporation Gildemeister Handling Software + ± ± Carl Schröter (Insurance company) 8 9 Test at trucks for express delivery Rungis Express Trucks with temperature zones Temperature oscillations in delivery truck 8 Reefer Air Wall 8 Box Box Time : : 9: : Temperature C Reeefer Peaks ~ C Wall oscilations C to C

4 Meet compartment Deep freezer after 5 hours.8 5. C C. C.7 C.9 C.77 C.5.79 C. C.8 C.7 C.5.97 C. C 5. C.5.87 C. C.5 Average of reefer side ~ C colder than other side Single loggers behave 'chaotic' No simple averaging Measurement in sea containers Route Set point Bremen Nigeria 8 C Chile England C Hong Kong Bremen Non chilled.9. Deviation 5 C.8 C. C Temperature inside palette After hours in warm ambient 7 C C Time constant. days 5

5 How and where to measure Wall, packing and core temperature Quality depends on core temperature Wall temperature = Surface of palette + Isolation losses + Air stream Time constant. days Time constant 5 hours How to define and measure quality? Legal requirements: only temperature thresholds Multiple factors: Colour, firmness, taste, vitamin C content. Generalized Scale: Keeping quality / Shelf life Number of remaining days until a defined threshold will be passed (colour loss, bacteria limit, consumer acceptance ) 7 Mathematical modeling approaches Reference curves Recorded for constant temperatures Interpolation for dynamic temperatures 5 Taste Taste of raspberries (Nunes,UF) C 5 C C 5 C C Days 8 Shelf life modelling Calculation of loss per day as function of temperature Arrhenius equation for reaction kinetics Look up table Loss per Day 5 Tomato Green Q(5 C)=.95 Tomato Red Q(5 C)=8.77 Tomato Pink Q(5 C)=.8 Papaya Q(5 C)=.5 Beans Q(5 C)= Temperature in C 8 9

6 Effect of small temperature peaks Temperature in C Calculated shelf life Measured surface temperature Calculated core temperature (time constant hours) Time Shelf Life in Days 5 Application to recorded data 8 T (Box) T (Box) Shelf life Box Shelf life Box Difference in shelf in shelf life after life days after of transport days of transport Time Temperature in C Box Coldest Warmest 8 hours Pattern repeated for expess test of long term dilivery effects Average 8. C. C Zero Shelf life.5 days.5 days Temperature course of warmest and coldest box Shelf Life in Days 7 5 Strawberries Case Study Study by the University of Florida Temperature sensors inside and at surface of palettes Manual quality assessment Comparison with shelf life prediction First expires first out (FEFO) Split truck load by low / high shelf life palettes for delivery to nearby / distant stores Strawberries Case Study Temperature tracing and shelf life prediction would give the following recommendation: = full days = full days pallets reject immediately = full day pallets rejected at arrival = day 5 pallets sent immediately for stores 8 pallets sent to nearby stores 7 pallets no special instructions (remote stores) Center for Food Distribution and Retailing (J.P. Emond)

7 Strawberries Case Study Revenue and profit Waste at the store level ( pallets sent) Days left Number of pallets Waste random retail 9.7% Waste (RFID + Model) (rejected) (Recommendation) (don t transport) Actual RFID + Model REVENUE $7,57 $58,55 COST $9,87 $5, %.7% (5%) (.%) (sell immediately) (nearby stores) PROFIT ($,) $,7 7 % (%) (remote stores) 5 Steps towards UHF EPC Standard Better pulk reading and higher data rate Range ~ m Gen with password protection Currently up to 8 bytes user memory (NXP Tags) Warnings: UHF needs careful planning, but still is the best solutions #: Be aware of the speed of forklifts Reading speed is critical Large tagpopulations High (temperature) data amount What is the effective Gen data rate? European UHF Bandwidth MHz MHz USA Maximum of kbit / sec hardly reached 7

8 Analysis of Gen protocol Reading 8 Bytes user memory Deselect all Tags Select one EPC Send EPC Query Tag Send Handle Request Handle Total Protocol length: ms Send 8 Byte ms Read Command The communication bottleneck Data rate Interrogator > Tag Tag > Interrogator Tags Inventory + 7 Temperature data each Pessimistic kbaud kbaud 88 ms Even with Gen Protocols it is hardly possible to transfer full temperature history of multiple items Medium 8 kbaud kbaud 88 ms 8 9 #: The influence of water Identification rate Best of reads from different positions Distance 5 cm Reader: Feig Obid ISCLRU Tags: NXP / TagNology Strict recommendation: Tags only at surface Inside very limited penetration Distance surface to water bottles.5 cm HF: less sensitive, but also less range Identification Rate % front st row nd row rd row between bottles Effectice radiated Power ERP (Watt)

9 Writing to the tag Write back Maximum transport temperature Calculated quality index Surface No problems st row % more power but rate < % Write Rate % front st row nd row rd row between bottles Effectice radiated Power ERP (Watt) #: The influence of air humidity Identification / Write (doted) Rate % C 5% Air Humidity 95% Air Humidity Reader Power Reader Power Low max. absolute humidity at low temperatures.5 g/m (5 C) 9.5 g/m ( C) Minor influence chain applications < 5 C Identification / Write (doted) Rate % C 5% Air Humidity 95% Air Humidity Solutions for the communication bottleneck Chain supervision by intelligent RFID OnChip processing of sensor data by Intelligent RFID / sensor label Split between identification and measurement task Step : Configuration Step : Transport Step : Arrival Step : Post control Shelf life model to assess effects of temperature Only state flag transmitted at read out Standard identification tag at item level Active sensor nodes for permanent access Manufacturer Reader gate Handheld Reader 8 Shelf Life (days) T ( C) Measures/stores temperature Calculates shelf life Low quality flag List Remaining shelf life per item Full protocol 5

10 Required hardware resources Is it feasible to squeeze a shelf life model into a microchip? Type of Resource Processing time Program memory RAM memory Calculation of Arrhenius equations. ms 88 bytes 58 bytes Available energy Very small additional recourses compared to circuit of data logger Shelf life model can run by paper thin batteries Power consumption per month Update every 5 minutes Stand by current of MSP (µa at.v) Turbo Tag (Zink oxide battery. J / month 5.7 J / month 8 J Energy µjoule 7 Hardware platforms Hardware of the intelligent container Wireless sensor nodes Tmode Sky / Sentilla Own development Goal: Integration into passive UHFRFIDTag Conversion of existing HF Interface Freight Object (RFID) RFID Reader Sensor Nodes Local Pre Processing External Communication Unit 8 9

11 RFID and information flow Creation of transport order Logistical object Passive RFIDLabel Dynamic link RFIDTag is read at transshipment R F I D Container requests agent at loading Warehouse or Means of Transport CPU platform Sensors RFIDReader Oscilloscope view Message screen Temperature Humidity Intelligent Agent Software representation of the physical object Transport and handlinginstruction Quality Index 8 T ( C) Shelf Life (days)

12 Sensor tracing technologies Technology Telemetric systems RFID data loggers Wireless sensors Intelligent RFID Intelligent Sensors Intelligent Container Online accessibility Local processing Granularity Current state Available Short range available Prototypes, pilot studies Concept Under development Demonstration system Summary and outlook Case study (strawberries) showed the potential to reduce waste and increase profits Quality evaluation of the level of RFID tags is feasible Development of new UHF hardware required The intelligent container offers online access to temperature and quality data 5 For more information and publications please visit Report: Technische Grenzen des Einsatzes von UHF Identifikationssystemen (RFID) im Lebensmittelbereich Industrial Meeting: December th, 7 in Bremen Dipl.Ing. Reiner Jedermann Universität Bremen, FB, OttoHahnAllee NW, D859 Bremen, GERMANY Phone , Fax rjedermann@imsas.unibremen.de Thank You! 7