Electrical, Comp & Energy Engr
Non Technical Summary
The food supply chain is now plagued by the dual scourges of contamination and counterfeiting; both proliferate in this complex environment and have already risen to the level where their effects threaten everyone on the planet. Advanced information management, such as blockchain, will help us to reduce the impact of tainted and fake food but what is missing is an incorruptible physical identifier or "trust mark" that securely links individual products or small batches to the database. We need an artificial fingerprint that can be scanned throughout the supply chain using existing equipment and infrastructure to ease adoption and reduce cost. This ability must go all the way to the consumer in order to maintain a strong chain of trust from farm to fork.Our solution to this issue derives from a technology based on "dendrites" - exquisite tree-like patterns that are very easy to form using techniques which lend themselves to inexpensive mass manufacturing. No two of these patterns are the same, much like snowflakes, and they are extremely difficult to copy so each item in the supply chain can have its own unique trusted identifier. Using advanced software, dendritic patterns can be read and verified securely and reliably using a cell phone. Adoption in the supply chain from producers to consumers will be frictionless due to the use of such familiar technology.The societal impact of this technology could be very great indeed, with an overall improvement in the human condition occurring due to increased trust in the food supply. Individual item identification will lead to hyper-targeted rapid recalls that save lives, reduce food waste, and eliminate economic and brand damage. These benefits materialize with low investment via use of existing infrastructure for label manufacture and use within the supply chain. Verification and reading by cell phone brings information to consumers and their social network, allowing and encouraging the socialization of food information and safety.
Animal Health Component
Research Effort Categories
Goals / Objectives
The long-term goal of the proposed project is to protect the US food supply from the dual scourges of contamination and counterfeiting while ensuring high levels of consumer trust through a low cost, highly usable and secure tracing methodology that is compatible with evolving approaches to information handling such as blockchain. This will be achieved via the utilization of existing manufacturing processes and cell phone infrastructure, in full partnership with stakeholders within the supply chain. The supporting objectives are:1. The creation of a low-cost robust and unclonable food-safe tag that utilizes a novel high security dendritic pattern formation technique coupled with standard methods and materials for food and agricultural item labeling to uniquely mark individual items or packages.2. The development of a high reliability pattern reading and recognition system based on a cell phone front end that is image noise tolerant and compatible with existing and emerging supply chain database approaches that can be used by anyone from field to kitchen with little or no training.3. The design and piloting of stakeholder-driven use models that fit with the economics and physical realities of the food and agricultural supply chain, and the socialization of food knowledge and safety via cell phone-based applications.
The project work will be conducted in three locations - ASU's Nanofab, NAU's computer science labs, and in the field, mainly at the stakeholder sites.1. ASU Nanofab. The project work at ASU will be split into two sub-tasks - (a) tag fabrication and (b) tag testing.(a) Tag fabrication. This work is completely unique to our team and will concentrate on finding the optimal material and growth parameters for the production of well-defined, highly ramified, stable, cm-scale dendrites at high speed on label materials in current use in the industry, including polyethylene (PE) and polypropylene (PP) substrates as used in PLU tags. We will aim for a branching morphology that is akin to a diffusion limited aggregate (DLA) structure with a fractal dimension around 1.7, as this will facilitate our graph-based reading scheme which detects branching points using cell phone optics and back-end processing. Our current process takes a few seconds to grow cm-scale patterns (large enough to be read by cell phone) but we would like to optimize the growth process to push this below one second while maintaining proper morphology and optical characteristics. For this work, we will produce an experimental design that involves different label substrates, coatings, electrode shapes, electrolyte molarity, growth voltage, and growth waveform. The output will be an optimal "recipe" for dendrite fabrication on the chosen substrates that would be usable by a manufacturer. Key metrics are dendrite fractal dimension (measured by ImageJ software), clarity (which relates to low error rate readability by the NAU cell phone-based scheme), and speed of formation (which should be <1 sec and is trivial to measure).(b) Tag testing. The reading algorithm is fault/noise-tolerant, but the tag clearly must survive the ravages of the use environment in such a way as to allow unambiguous reading of the information contained within the dendritic pattern. The environmental testing of the finished dendritic tags will mostly be performed in our laboratory, but we also intend to have field trials on a limited basis with local growers. The laboratory tests will involve (i) repeated stretching, bending, and abrasion using a robotic arm to provide thousands of mechanical cycles to simulate use conditions, (ii) heat and humidity in an environmental oven, (iii) ultraviolet light exposure under sun simulation lamps, (iv) citric acid, salt, and detergent exposure in chemical baths and sprayers, and (v) adhesion and general tag integrity by inspection. After all tests, the tags will be examined for damage using optical, polarizing, and scanning electron microscopy. Certain samples, particularly those which exhibit corrosion or other reactions, will be further examined using materials analysis techniques such as energy dispersive x-ray analysis (EDXA), x-ray photoelectron spectroscopy (XPS), and secondary ion mass spectrometry (SIMS), all available in our facility or sister center at ASU, the Eyring Materials Center. Failure mechanisms will be identified, and steps taken to correct the issues to produce the desired stability in the pattern and tag. Finally, using the reading system developed by NAU, the error rates for the various materials and dendrite growth recipes will be assessed following environmental testing. The key metric in this effort is the readability of the tags with low error rate, which will be achieved by producing relatively large quantities of tags and running them through the reading system for verification.2. NAU computer science. The NAU work will involve the development of algorithms implemented in cell phone and mainframe/cloud-based software platforms. The cell phone interface will mainly comprise a graphical user interface (GUI) and secure data transmission. In pushing the bulk of the information processing activity to network-based units, the NAU team will build on their expertise in parallel computing based on open Message Passing Interface (open MPI) and SLRUM workload manager using NAU's high-performance computing (HPC) server. This work will also involve the emerging technology of mobile edge computing (MEC) to further improve performance by computation offloading and pushing the bulk of the processing to the edge of access network (e.g., LTE servers and 5G HetNets). They will also continue to refine their image-based identification and authentication algorithm that translates patterns into representative graphs by extracting the geometrical features. This graph matching approach is unique to our project and is ideal for the analysis of the structured dendritic patterns, even in noisy environments, leading to data economy on an unprecedented scale. The end result of the graph matching algorithm is a similarity score that is used to authenticate the tag if exceeds an application-specific threshold. The key success metric relates to the ASU tag fabrication and testing work, as it will involve the determination of error rates for significant quantities of both new and "worn" tags.3. Field research and economic analysis.Our understanding of the unique supply chain for the product(s) used in use model development and the pilot program is essential. This will be achieved by consulting with multiple stakeholders who have agreed to support the work of this proposal in problem identification, planning, implementation, and evaluation. Much of this information gathering will be performed by researchers visiting the stakeholder sites but web-based communication will also be employed to minimize travel cost and time. In analyzing the results of this study, the team will perform detailed economic analyses of the dendritic solution focusing on financial feasibility, including a firm-level net present value (NPV) analysis that will be conducted at the farm, distributor, and retail levels, including the influence of initial cost of implementation, variable maintenance costs associated with the system, and any incremental cost savings relative to existing traceability systems that may be in place. This analysis will also consider any cost-savings and/or revenue preservation resulting from the use of the dendritic system in the presence of a recall event. The NPV analysis can be examined under alternative scenarios for both the probability of a food safety scare as well as its financial severity under assumptions of implementing the dendritic system versus no implementation. This type of simulation is easily facilitated using Monte Carlo simulation software such as @RISK, a tool that the ASU agribusiness research team has considerable expertise in using. The key metrics from this activity are industry acceptance levels and NPV for the system/use models employed.