Traceability Data in the form of Digital Food Product Passports for Fish Supply Chains

Traceability Data in the form of Digital Food Product Passports for Fish Supply Chains Shanshan Jiang Software engineering, safety and security SINTEF Digital Trondheim, Norway shanshan.[email protected] Paraskevas Bourgos Research & Innovation Development Department Netcompany-Intrasoft S.A. Luxembourg [email protected] Marit Kjøsnes Natvig Software engineering, safety and security SINTEF Digital Trondheim, Norway [email protected] Nikoleta Tsampanaki Research & Innovation Development Department Netcompany-Intrasoft S.A. Luxembourg [email protected] Truls Bakkejord Ræder SINTEF Nord Tromsø, Norway truls.[email protected] Abstract—The idea of a Digital Product Passport (DPP) is to provide access to product specific data for actors and consumers in supply chains to satisfy different needs. Solutions for DPP exist in sectors other than food. However, literature on Digital Food Product Passport (DFPP) is very limited, and there are no clear guidelines regarding what data should be contained in a DFPP. Various platforms for traceability exist and traceability data obtained along food supply chains can be used as a fundamental data basis for DFPPs. In this paper we define what data to be included in a DFPP at different steps of the supply chains based on a collection of end-to-end traceability data. The focus is on how to use such data systematically in a DFPP and how to support the different stakeholders involved. We demonstrate how this approach can be applied to implement a blockchain-based DFPP for a fish supply chain. Keywords—traceability, digital food product passport, fish supply chain, food fraud, blockchain I. INTRODUCTION The Eco-design for Sustainable Products Regulation (ESPR) addresses the environmental sustainability of products and aims to reduce the carbon and environmental footprint of products over their life cycle. Digital product passports (DPPs) are intended to support this goal by providing a set of data specific to a product. Article 1 of the regulation states that it applies to “any physical goods that are placed on the market or put into service, including components and intermediate products”. The regulation does not apply to food, but a dynamic and digital product passport for the food sector may according to [1] increase the transparency regarding the content of food products and the processes they go through and create trust, protect against fraud, and increase the market for suppliers and producers of sustainable food products that are produced in compliance with rules, regulations and ethical standards. However, literature on digital food product passport (DFPP) is limited and there are no clear guidelines regarding what data should be contained in a DFPP. To address this gap, this paper explores the idea of a DFPP, and defines the information of relevance to the actors in the food supply chain to be included in the DFPP and a possible implementation of the DFPP. Similar to a real-life passport, a DFPP can be considered as an official document proving the identity of a food product and the key properties it claims. A DFPP is a digital verified document that may provide easy access to key information about a food product from its source to the shelf, including all basic product information (such as origin, ingredients, nutritional and allergen data, expiration and storage information), relevant properties (such as quality and sustainability indicators), and how it is handled along the supply chain (such as all the transformations and transport routes). The key data is easily accessible via digital means (like QR codes, RFID tags, blockchain technologies). Combined with other documents, a DFPP can serve as a trusted source and evidence regarding product identity and properties, compliance to regulations, and enhancement to consumer trust. In this paper, DFPP is used as an interface to actors and consumers providing information regarding food products according to user needs, where reliable information on a food product and its life cycle is collected from a trustworthy end-to-end traceability system. A DFPP can be linked to other DFPPs, for example, a DFPP for a food product can be linked to the DFPPs of its constituent raw materials, and the DFPP for the food product can be referred to by another DFPP. The definition of the information content in the DFPP is based on studies of needs in real supply chains for fish and wine and work on a new traceability framework for food supply chains [2]. Actors in the supply chains are interviewed and literature on traceability needs are studied, a set of generic process steps and event types to be traced are identified, and the data to be registered for each event type are defined in an information model. In this paper, this information model is used as a starting point for a definition of the information content in a DFPP. Blockchain is a decentralized, distributed ledger technology that records transactions using blocks linked chronologically and securely in a peer-to-peer distributed network. It offers secure data storage and uniform data access and other benefits due to their resilience, robustness and trustworthiness [3][4]. Blockchain technology has been widely used in DPP design and implementation, and according to [5], 14 out of 32 DPP-related initiatives have adopted or plan to use blockchain technology. In this paper, we provide a suggested blockchain-based DFPP solution. In the following, Section II describes briefly the related work. Section III presents a conceptual framework describing how the contents of DFPPs can be established systematically based on an end-to-end tracing of food supply chains. Section Author accepted manuscript version of the publication by S. Jiang, M. K. Natvig, T. B. Ræder, P. Bourgos & N. Tsampanaki. In 2025 6th International Conference in Electronic Engineering & Information Technology (EEITE). Published version: https://doi.org/10.1109/EEITE65381.2025.11166326 Distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) IV illustrates how DFPPs can be established and updated in association with the business logics along an example fish supply chain. Section V suggests a blockchain based implementation and visualization of the suggested DFPP solution. Discussion is provided in Section VI before the final conclusions. II. RELATED WORK As mentioned in the introduction, DPPs are in general not related to food products, and some of the issues targeted in current publications, like recycling, are not relevant to DFPPs. Other aspects are however of interest to DFPPs. [6] provides a systematic literature review and summarizes, among other information, requirements to DPPs. Several of these are of relevance to DFPPs, for example, product information, environmental information (e.g. carbon footprint, environmental impact and societal impact), processing information (e.g. use of resources), life-cycle data (e.g. share of waste), supply chain information (e.g. actors involved) and compliance to regulations. The literature on DFPPs is very limited. [1] addresses what they call dynamic digital product passports (dDPP) for food and emphasize the need for such dDPPs to verify organic and geographical indications (GI) food assets. The need for certificates, product information, actor information and sustainability data is addressed as well as the need for a verification of compliance with handling requirements such as transport temperatures. Dynamic updates as the product flows through the supply chain are considered to be important, as emphasized in [7]. [8] provides a technical approach for DPPs for dairy products motivated by food safety. The DPP information is collected from assets in the supply chain from the cow to the cheese products. III. CONCEPT FRAMEWORK The DFPP is, as mentioned in the introduction, based on an end-to-end traceability framework for food supply chains that facilitates a fine granular traceability [2]. The framework defines generic process steps for the whole supply chain, the event types that may occur within the process steps, and the data to be registered for each event type. The traceability framework defines detailed static and real-time data needed in traceability systems that may cover all supply chain process steps. The data are intended to support the operations of actors in the chain regarding food safety management (recalls of products, etc.), detection of fraud, verification of compliance with rules, regulations and ethics, and efficiency improvement. It is likely that not all supply chain stakeholders, end consumers included, have access to such systems. DFPPs may however provide easy access to aggregations of the detailed information available through a traceability system or a dashboard, and DFPPs may be established and updated during the process steps of the supply chain to document properties of the product of interest to the stakeholders. In general, there may be Business-to-Consumer (B2C) and Business-to-Business (B2B) types of DFPPs. B2C DFPPs are aiming for end consumers and is mainly used as a certificate that provides static information about a consumer product. B2B DFPPs provide more or less dynamic data for other stakeholders in the food supply chain that is critical for the planning of operations, follow-ups and decisions. A DFPP will target one or more Traceable Resource Units (TRU), which have the same properties. The TRUs may be raw materials, a batch of raw materials, a batch of products, or a specific product. Fig. 1 shows an example of a fish supply chain with a selection of process steps that illustrate generic aspects of DFPPs. The first DFPP may typically be established during the sourcing step, and it will be of type B2B and address a TRU that is the harvested raw materials in general. The sourcing step may also establish additional B2B DFPPs for smaller batches of raw materials that are fed into the supply chain. These DFPPs will be updated with information as the TRUs move along the supply chain. At a certain point, e.g. from the trading step, different batches may take different paths to different processors, and the DFPPs will be updated with information from the steps that take place. Processing steps will process and transform raw materials and other food products to new products, and new DFPPs will be established for these products or for batches of such products. Depending on the supply chain, there may be several processing steps that establish new products with new DFPPs, and there may be other steps between the processing steps (e.g. transport steps). The final processing step will establish a B2C DFPPs for TRUs that are bound for the same retailer or wholesaler. Fig. 2 provides an overview of data we consider as relevant for DFPPs in general. The light blue classes are stereotyped to indicate that they originate from existing GS1 industry standards, which are commonly used in transport and logistics, especially regarding TRU identifiers, locations and actors. Classes stereotyped with “eCom” originates from [9]. Classes stereotyped with “GS1 Identification” originates from [10]. TRUs are identified through the TransactionalTradeItem class. It inherits the TradeItemIdentification that may include a Global Trade Item Number (GTIN), which may identify a unique item type as well as more proprietary identifiers. The TransactionalItemData class may in addition include a batch or serial number for a finer granularity. The number of items in a batch is provided in the TransactionalTradeItem class. Fig. 1. A fish supply chain example with generic process steps and Digital Food Product Passports (DFPP) Retailing TransportSourcing Transport Storage Processing (factory) Transport Trading Transfer (Landing) Transfer Import/ export F_DFPPs of batch of bags with fish •F_TRU: haul + fish species + size + quality + batch number P_DFPPs of batch of consumer products •P_TRU: GTIN + batch number C_DFPP of raw material •C_TRU: catch ID DFPP update DFPP(s) established Scope of DFPP Legend: Consumer Processing (On-board) Fishing vessel Fishing vessel Retalier Transport service provider Cold storage provider Processor Transport service providers Trader/ Sales association Sales association Transport service provider Trader Process step Fig. 2. Information model for the generic Digital Food Product Passport The TransactionalParty class has a Global Location Number (GLN) attribute that identifies an actor or a location and may for example identify the issuer of DFPP, the TRU producer, the actor that has done quality inspections, and the source of the information provided. The DFPP has three main parts as indicated by the lightyellow colour in Fig. 2. The documentation part may provide certificates (e.g. regarding the origin of the TRU according to the European geographical indications (GI) scheme) and verify the compliance with rules and regulations, and with ethics (ecological farming, halal procedures, etc.). The Ecom_DocumentReference class will provide a link to relevant reports and certificates. The TRUData and TransactionalItemData classes include and link to static and overall information that identify and characterise the TRU. The DFPPDataCarrier class links to the DFPP data, and such links may for example be implemented using QR codes and RFID tags on the physical trade items. The Measurement class may represent the total weight or size of the TRU. If a TRU is part of a larger TRU that is split into smaller batches or products, the TRUData links to the parent TRU, which also may have a DFPP. If a TRU is re-labelled, a new TRUData instance will be created. It will refer to the previous TRUData (the “sameAs” association as indicated in Fig. 2) and to the same DFPP. Thus, a DFPP may link to several TRUData instances, and the TRUData attribute will indicate the sequence of the instances. The difference between the instances will in general just be the identifiers used in the labelling of the TRU product. Static data that is unchanged should not be repeated in new instances. The static data may include allergen and nutrition information, and the BillOfMaterialEntry may provide information on input materials and products. For each entry, there will be an input TRU and a quantity (through the Measurement class). Input TRUs may also have DFPPs that can provide details on the input. There may also be handling instructions regarding the storage and transport of TRUs with minimum and/or maximum values for temperature, humidity, etc. If the TRU is a raw material, the ProductSpecificDetails class provides further data on the TRU. A content example for the composite ProductSpecificDetails (dark yellow class in Fig. 2) is provided in Fig. 3. The DFPP may include dynamic properties regarding the TRU, i.e. information that is dynamically established during process steps. This may be information on quality inspections, and results can be provided through the Measurement class or as deviation details in case of deviations. The transport route is represented by data on the transport legs carried out, and state transitions (e.g. if and when the product is frozen, defrozen, re-frozen, etc.), condition represented as time series (e.g. temperature logs), and sustainability data per type of process step (e.g. transport, sourcing or processing) may also be provided. For the latter, the sustainabilityIndicatorType and value attributes must be standardized, including the way to calculate the sustainability value inherited from the Measurement class. Input factors (e.g. energy and water), environmental footprint indicators (e.g. CO2 emissions and other emissions) should be addressed as well as the share of the TRU that is discarded and becomes waste. TABLE I. provides an overview of the data that may be registered for each type of process step. TABLE I. DATA REGISTERED PER PROCESS STEP Process step Data that may be registered Sourcing New DFPP TRUData: -TransactionalItemData -TransactionalTradeItem with identifier -Measurement - ProductSpecificDetails (for raw materials) Transport Dynamic properties - TransportLegData Processing New DFPP TRUData: -TransactionalItemData -TransactionalTradeItem with identifier - Measurement - ProductSpecificDetails for raw materials (batches) -Allergen information -Nutrition information -BillOfMatrialEntry refering to input TRUs with DFPPs All steps Documentation Dynamic properties -QualityInspection -StateTransition -DeviationDetails - Sustainability D ata IV. DFPPS FOR AN EXAMPLE FISH SUPPLY CHAIN In this section, we describe how DFPPs are created and updated using a real-life fish supply chain as an example. The supply chain addressed is depicted in Fig. 1. In the sourcing step, fishes are caught in the Northern Sea. A catch represents fish of the same species from one haul. The fishing vessel will create a Catch DFPP (C_DFPP) for each catch, and the catch is a TRU (C_TRU) for which the relevant information in Fig. 2 will be registered, including the product specific details as defined in Fig. 3. These details include the data on the catch (catch area, etc.), species (name and code), vessel, fishing license and quota, and gear/fishing method. As shown in Fig. 2, a C_DFPP may refer to relevant documentation, e.g. documentation of quality inspections (QI) and documentation confirming ethical sourcing, origin and compliance with regulations, such as the fishing quota as a proof against overfishing and illegal, unreported, and unregulated (IUU) fishing. Reporting messages to the authorities (DEP, DCA, POR) may be included as evidence for the catch. The C_DFPP can also link to other documentations such as videos and photos from the fishing trip. Additionally, the C_DFPP may include sustainability data like the carbon footprint for the catch operation. The catch is processed in an onboard processing step. The fish is headed and gutted, cleaned, inspected, sorted according to size and quality (based on bruises, bleedings, poor cuts, discolouration, etc.) and packed into bags. Batches of bags that contain the same type of fish with the same size and quality are placed on pallets that are frozen and stored in the freezer onboard. The batch of bags with fish on one pallet is a TRU representing a fish product (F_TRU), and a new DFPP (F_DFPP) is created for each F_TRU. Bycatch will be separate fish products (F_TRUs) with separate F_DFPPs. The TRUData of a F_TRU will link to the TRUData of its parent TRU, which is the C_TRU mentioned above, and the C_DFPP will provide information on the raw material for the F_TRU. The TRUData in Fig. 2 will hold static product information such as the product form (fillet), state (frozen), quality grade and processing method (heading and gutting). The tradeItemQuantity attribute in the TransactionalTradeItem class will be the number of bags in the F_TRU batch. The TradeItemIdentification will provide an overall identifier for the F_TRU (may for example refer to species, size, quality, haul and production date), and the TransactionalItemData will provide the batch/lot number for the batch of bags on the pallet and serial numbers that are bag identifiers. Handling instructions regarding storage and transport of the frozen fish are also included. The product specific details for the F_TRU are shown in Fig. 3 and include bag size, first frozen date, and species name and size. The dynamic part of the DFPP may, as illustrated in Fig. 2, include information on state transition (from fresh to frozen) and quality inspections (and associated links to documentation Fig. 3. Information model for product specific details for the C_DFPP and F_DFPP. such as QI reports). Information on head or bones thrown away is registered as SustainabilityData with a sustainability indicator indicating that this is fish waste from a normal gutting of the fish. In the transfer step, the pallets with fish bags are landed, and all F_DFPPs are updated with the link to the landing note documentation. At the trading step, F_DFPPs will be linked to sales notes and catch certificates (in case of export). In the transport steps, the pallets with batches of bags with fish are used as logistic units, and the bags stay on the same pallet until they arrive at the processor’s site. The pallets may be transported to different processing destinations through different routes, and each F_DFPP is updated with information on the transport legs. Links to transport documents and QI documentation may also be added to the F_DFPPs along the supply chain as well as delivery tickets from storage terminals. The storage step will re-label the batches (i.e. the pallets) before putting them in the storage. Thus, a new TRUData with a new identifier will be established. It will link to the previous TRUData through the parentTRU association. Both TRUData instances will be included in the same DFPP. Deviations with respect to the handling instructions may occur in any process step and information on such deviations will also be added in F_DFPPs. F_TRUs that do not meet absolute requirements (e.g. a handling instruction regarding temperature) will be discarded and registered as waste (an indicator in the sustainability data). The sustainability data may also include indicators for fuel consumption and emissions, calculated using the data collected along the supply chain. When the fish pallets arrive at the processor site, they are stored and scheduled for production. The frozen raw materials stay in a chill room for defrosting before they are sent to the production line. The change in state from frozen to de-frozen should be registered in the F_DFPP (StateTransition in Fig. 2). The raw materials are graded after defrosting, and the quality information in the F_DFPP may be updated. In the factory processing step, consumer products are produced from the F_TRUs (i.e. the fish in the bags) and packed. A production batch may be transported and stored in different factory locations for secondary processing (not shown in Fig. 1). Batches of consumer products are ordered by retailers, and each batch (P_TRU) will get a DFPP (P_DFPP). Labels with QR codes that link to the respective P_DFPPs will be generated and put on the consumer packages so that consumers can access the P_DFPP information. The P_DFPPs will also include allergen information and nutrition information as described in Section III, and they will link to the previous TRUs and DFPPs (C/F_TRUs and C/F_DFPPs) through BillOfMaterialEntries. Important processing details are also registered in P_DFPP as well as the quality grade decided through internal quality inspections. Information regarding the transport legs (those to the retailers included) are added as well as sustainability indicators for use of resources (water, energy, etc.) and emissions. V. BLOCKCHAIN-BASED DFPP FOR FISH SUPPLY CHAIN The proposed Digital Food Product Passport (DFPP) solution is built upon a modular architecture. A DFPP architecture instantiation is illustrated in Fig. 4. The design leverages blockchain, aiming to enhance traceability, transparency, and ensure secure data exchange in the Fish Supply Chain based on the concept framework and business logic described in this paper. The system has multiple layers logically grouped in three elements: Supply Chain Participant(s), Centralized Services, and User & Services Dashboards. At the participant level, the Supply Chain Participant module operates as a localized system handling data collection and processing through IoT sensors, human inputs, and various internal and external data sources, with localized services managing data adaptation, and blockchain synchronization. This connects to the Centralized Services group, which forms the backbone of the system's security and accessibility framework, featuring a Service Gateway for unified access control, Identity & Access Management for user authentication, and Digital Data Models & Vocabularies for standardized blockchain interaction. The user-facing component is delivered through the DFPP Dashboard module, which provides stakeholders with intuitive interface and serves as the central point for real-time product traceability visualization, offering search capabilities and detailed product information access. In the Fish Supply Chain, the B2B DFPP is instantiated to a Fish Supply chain use case involving four key participants: a fishing vessel, the onboard processing, the transfer (transport included), and a processing facility, all integrated using Hyperledger Besu blockchain technology. The system utilizes four Besu nodes deployed on separate virtual machines, employing IBFT 2.0 consensus protocol for data integrity. Smart contracts automate crucial processes including catch registration, handover events, and quality assurance. The DFPP Dashboard provides a two-level traceability visualization system, allowing users to track fish from catch to final packaging through an intuitive geospatial interface, illustrated in Fig. 5, and complete with information from each stage of the supply chain, as depicted in Fig. 6. Fig. 4. Digital Food Product Passport architecture instantiation designed to leverage blockchain in the Fish Supply Chain using four participants Fig. 5. Digital Food Product Passport Dashboard - Traceability visualization Fig. 6. Digital Food Product Passport Dashboard - Supply chain sourcing details VI. DISCUSSION The conceptual framework presented herein provides an important step towards a systematic implementation of a DFPP in food supply chains. The suggested registration of events within the process steps covers the dynamics requested by [1] and [7], and the information content can, if the technical realization is secure, verify the origin of the food and certify its properties and handling, as requested by [1]. The topics requested in [6] are addressed: The supply chain information is represented by the events that are tracked. The product and processing information is provided for the sourcing and processing steps as described by the data structures starting with the TRUData class in Fig. 2 (the details in Fig. 3 included). Environmental information and life-cycle data like the share of waste, the use of resources and emissions are represented as classes following the DynamicProperties in Fig. 2, including the SustainabilityData class. The Documentation part in Fig. 2 can cover among others the compliance to regulations. Several practical challenges and barriers need to be solved in order to implement the framework. First, not all of the data specified by the models is necessarily available as inputs to the system. This could for instance be because it does not exist as required, because it is not digitalized, or because it does not adhere to any recognized standard. This will often be the case for transport documents. Second, the issue of granularity needs close consideration, that is, what are the TRUs to be considered in the supply chain in question. If one chooses a larger TRU (meaning that a TRU is a large batch of products), then it is necessary to have a precise handling of the transformations into new products in processing steps, so that the correct relations between input TRUs and output TRUs are maintained, the measurements of each input included. Section IV provides a suggestion on the TRU levels for the different DFPPs. Factors outside the scope of the conceptual framework may also affect the quality of the data and the implementation. Pallets in a truck may for instance inherit the temperature measurements from a sensor in the truck, or the TRUs may have individual sensors for tracking of the temperature. For the latter, the cost of the sensors might be prohibitively high, and one would still need to handle aggregation events, albeit potentially later on in the supply chain. This is a question that needs to be carefully considered and answered in each individual instance, depending on the value of the goods, cost of necessary sensors, the handling requirements, stakeholders involved, and more. The DFPPs of the TRUs that emerge in the supply chain can support different stakeholders and serve several purposes. The C_DFPPs and F_DFPPs covering the steps from sourcing to the factory processing are B2B DFPPs. They support the business actors in the tracing of operations and progress and address issues that may affect the quality of the TRUs and compliance to rules, regulations and requirements. This will support the actors when they plan and adapt their operations to be better coordinated with the rest of the supply chain. DFPPs may also refer to TRUs and DFPPs addressed in the previous process steps, and the actors may thereby get information on sources, qualities, sustainability, compliance to regulations, etc. The dashboard and blockchain solution described in Section V can support an access to such information in a trustworthy way. The P_DFPP is a B2C DFPP and is mainly intended as a documentation of issues of relevance to consumers and retailers, including information provided by DFPPs of TRUs that have provided input to the consumer product. The DFPP instantiation in Section V does not include the part of the retailers and the final consumer of the product, but it can be extended to cover the retailers and the final consumer providing the final product traceability information. VII. CONCLUSION The concept framework defined in this paper explains how the traceability data can be used for implementation of DFPPs for different supply chains. The concept framework and the DFPP instantiation is applied to a fish supply chain, and it is verified that the needs of the stakeholder are accounted for. The DFPP content can be managed and exchanged through a blockchain solution that can guarantee the authenticity of the DFPP data, and a dashboard can support the visualization of the data in a way that is adapted to stakeholder needs. The concept framework and blockchain implementation presented in this paper needs empirical validation. As future work, we will implement the approach outlined in this paper in a pilot on a real-life fish supply chain and gather lessons learned. In addition, as the presented concept framework is generic and applicable to other supply chains, similar work will be done for wine and honey supply chains. ACKNOWLEDGMENT This work was conducted as part of the Watson project under grant agreement No. 101084265. Funded by the European Union. 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