Traceability Model Research Articles

Source Apportionment of Heavy Metals in Wet Deposition in a Typical Industry City Based on Multiple Models

Handan city as a transportation hub in North China, air quality ranks at the bottom all year round, causing environmental pollution that has aroused widespread concern. In order to explore the pollution characteristics and main sources of heavy metals in atmospheric wet deposition in the city, and comparison of the applicability of multiple traceability models, a total of 76 wet deposition samples were collected in the three functional areas from December 2017 to November 2019 by a dry and wet deposition automatic sampler. Concentrations of Cu, Zn, Cr, Ni, Pb, and As were determined and enrichment factors were calculated. Sources of these heavy metals were apportioned by PMF, Unmix, and APCS-MLR models, and analyzed using a backward trajectory analysis model. The results showed that the concentrations of these heavy metals in the atmospheric wet deposition were in order of Zn, Cr, Pb, Cu, Ni, and As, and their mean concentrations were 29.53, 14.11, 9.18, 7.03, 6.41, and 1.21 μg·L−1, respectively. According to the results of EF, the studied heavy metals were mainly affected by anthropogenic activities. The source apportionment results showed that heavy metal pollution in the wet deposition was mainly affected by traffic sources, industrial sources, and coal combustion sources, and PMF identified an additional source factor: metal smelting source. By comparing the relevant parameters of the source apportionment results of the three models, the APCS-MLR model has better accuracy results than PMF and Unmix models. The analysis of the backward trajectory of the air mass showed that the wet deposition of Handan in the study time was mainly from the southwest direction, accounting for 54.35%. In the future, more evaluation methods and models will be used to compare and analyze the different application scenarios and parameter selection requirements in order to contribute to urban atmospheric environmental pollution prevention and control work.

Exploring blockchain-supported authentication based on online and offline business in organic agricultural supply chain

The rise of blockchain technology has disrupted the traditional traceability method of organic agricultural products supply chain (OASC) by emerging authentication or certification. For an offline business scenario, a blockchain certification traceability model (BTM) is popular, in which consumers check the product information before purchasing by convenience scanning bar-code. Similarly, for an online business scenario, a blockchain-based e-commerce model (BEM) is popular, in which consumers verify and book the product while surfing the web. However, few studies have an insight into the potential OASCs business model and employ quantitative methods to confirm the benefits of the adoption of blockchain. According to a current real-world online and offline business, we first conceptualize two models in this paper. We employ the Stackelberg game to compare the supply chain profit and consumer surplus. Furthermore, we extend the analysis by using the case of Red Beauty orange, which proves that the blockchain e-commerce model is a good supply chain model under the condition of high shopping convenience and low operating cost of the blockchain platform. Our findings are probably generalizable to apply to other industrial products that require such services.

A Business Process Reference Model for the Development of a Wine Traceability System

Traceability is among the most significant challenges in supply chains, where multiple stakeholders and activities are involved in the production and distribution of products. No supply chain can become sustainable without effectively addressing the problem of traceability by recognizing, monitoring, and implementing all necessary activities of the processes. This research provides a reference model for effective wine supply chain traceability and is part of a research project for the development of a blockchain-enabled traceability system. The reference model not only depicts processes but also covers all views that are necessary for achieving the whole picture of an effective traceability system. These views include the value chain, organizational resources, functions, processes, systems, data, and risks that are related to wine production and distribution. The reference model has a strong contribution to practice and research as it pertains to bridging the barrier between developers and users while also offering significant research outcomes. The research output is the reference model that includes standard wine traceability processes and all necessary data for effective wine supply chain traceability. The results of this research will be used for creating the traceability system’s specifications and ensuring that it will be effectively designed and implemented. The reference model can also be used for the implementation and adaptation of the traceability system to the stakeholders of the wine supply chain.

A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques

Food authenticity and traceability and climate change are key scientific issues that must be addressed in response to the food crisis in 2050. Lanmaoa asiatica mushroom is an expensive and nutritious fungi-based diets resource, it is necessary to identify its geographical origin and explore the impact of the climate on it. Thus, the purpose of this study is to establish a fast and accurate geographical traceability model based on L. asiatica mushrooms chemical information collected by near-infrared spectroscopy (NIRS) technology, and screen out key climate variables by competitive adaptive reweighted sampling (CARS) algorithm. Based on the NIRS information of L. asiatica mushrooms, two-dimensional correlation spectroscopy (2D-COS) images were generated and a residual neural network (ResNet) image recognition model was established to identify the geographical origin of L. asiatica mushrooms. The accuracy of training set and test set of ResNet model is 100%, and the loss value is 0.052, which indicates that the model is effective. In addition, the CARS algorithm was used to select the feature variables from 105 climate variables. Four important variables (February, March, and April precipitation and January minimum temperature) related to NIRS difference of L. asiatica mushroom were obtained by CARS algorithm. The results can provide a fast and accurate method for food authenticity and traceability research, and provide an innovative idea for screening key climate factors.

One-year Simulation of Air Pollution in Central China, Characteristics, Distribution, Inner Region Cross-transmission, and Pathway Research in 18 Cities

Air pollution in Henan province is serious and is significantly impacted by pollution transmission and interactions with surrounding areas. The emission sources in 18 cities in Henan province were labeled and applied to the WRF-CMAQ traceability model for simulation in January, April, July, and October of 2017. The pollutant distribution results showed that due to the combined influence of emissions and meteorology, the concentrations of PM2.5, NO2, and SO2 in Henan province were the highest in winter and the lowest in summer. The seasonal variation in O3-8h concentration was the highest in summer, followed by spring, and the lowest in winter. There was a large difference in pollutant concentrations between different seasons. The average concentrations of PM2.5, NO2, and SO2 in winter in Henan province were 4.17, 4.12, and 6.24 times those in summer, respectively, whereas the concentration of O3-8h in summer was 2.24 times that in winter. Since PM2.5, NO2, and SO2 are closely related to primary emissions and have a certain homology, the distributions of high values of these three pollutants were higher in the north and lower in the south, and the seasonal trends were more consistent. The seasonal distribution of O3-8h varied widely, with high O3-8h values mainly distributed in the northeastern region of Henan province in summer, when meteorological conditions contributed to O3 production; in winter, spring, and autumn, high O3-8h values were mainly distributed in the southern part of Henan province due to the suppression of meteorological conditions and NOx consumption. The results of the study on the transport of pollutants showed that extra-provincial transport and natural sources contributed the most to the concentrations of PM2.5, O3-8h, NO2, and SO2 in winter, with 36.20%-72.32%, 77.96%-96.08%, 49.45%-78.80%, and 59.05%-88.85%, respectively. When considering only local emissions and intra-provincial transmission, the contributions of emissions to local concentrations of the four pollutants in summer were the highest in all cities of Henan province. The contributions of intra-provincial transmission to PM2.5 and O3-8h in spring were the largest, with 25.63%-74.69% and 30.21%-80.01%, respectively, and the contributions of intra-provincial transmission to NO2 and SO2 in winter were larger, with 26.02%-76.96% and 20.30%-82.34%. The transmission paths of PM2.5, NO2, and SO2 were more similar in Henan province, with more transmission from north to south in winter, from west to east and southwest to northeast in spring, from southwest to northeast in summer, and from north to south in autumn; however, the transmission of PM2.5 was more complicated. The O3-8h transport path was more different from the others, especially in autumn when pollutants were mostly transported from north to south, but the O3-8h transport path from southwest to northeast was obvious.

Traceability Information Model for Sustainability of Black Soybean Supply Chain: A Systematic Literature Review

Traceability information as a solution option becomes an important task for the industry in providing products, preparing sustainable raw materials, and ensuring adequate safety quality. The emergence of these demands makes the industry perform tracking in order to prepare product inventories ranging from raw materials to products that have been produced. Based on these reasons, the scope of this paper is to provide a systematic review of the literature on various aspects of implementing information traceability models and sustainability of supply chain on economic, social, environmental, technological, institutional, and infrastructural dimensions. For this purpose, we use the Scopus, Science Direct, EBSCO Host, and ProQuest databases. We used the PRISMA model to identify, filter, and test for the eligibility of articles to be included. We selected 52 articles contributed by this search engine. We found was that between 2018 to 2021 there was increasing interest in this research. The dominant traceability information model in the article uses blockchain, the rest use operations research (OR), Google Earth Engine (GEE), website-based, Unified Modeling Language (UML), Extensible Markup Language (XML), physical markup language (PML), logit, enterprise resource planning (ERP), soft independent modelling of class analogies (SIMCA), and Spatially Explicit Information on Production to Consumption Systems (SEI-PCS).

Design of traceability system models for potato chips agro-industry based on fuzzy system approach

Identification of recorded information is the most important requirement for developing an effective traceability system. This paper aimed to develop an information technology (IT)-based traceability system combined with an intelligent system in potato chips agro-industry. In this paper, we present a business process in potato chips agro-industry which consists of three activities, i.e. raw materials receiving, processing, and warehousing of final products. First of all, a traceability system architecture was developed. To develop computational models, quality, food safety and environment criteria using fuzzy inference system (FIS) and adaptive neuro fuzzy inference system (ANFIS), and intelligent decision support system (IDSS) traceability model using android front end. An internal information capture point was identified for each step and corresponding traceability information to be recorded was determined. Furthermore, a prototype IDSS was developed to represent method of information modeling on products, processes, quality, and transformation at each node in potato chips agro-industry. In this study, intelligent systems have been developed for decision making in quality control good agricultural practices.

Mineral element fingerprints verified the geographical origin of years and quantities of rice

Mineral element fingerprint analysis technology is one of the effective methods for grain origin identification. The accuracy of the identification model is closely related to sample geographical origin, including sample year and sample quantity. In order to study the influence of sample year and quantity on the origin identification of mineral elements. The content of mineral elements in rice was determined by inductively coupled plasma mass spectrometry (ICP-MS). Stoichiometry methods were combined to establish provenance models for different years and sample quantities. The results showed that the accuracy rate of the origin traceability model based on multi-year rice (2018–2020) was 100.0 %. It improved the accuracy of the model by 12.0 % compared with the model established in a single year (2019). The accuracy of origin tracing model for rice with 236 varieties was 98.8 %. The accuracy of rice model with smaller sample size (83 samples) increased by 3.8 %. It verifies and compares the model accuracy of origin discrimination of 15 randomly collected rice samples from three producing areas. The number of correct discriminations (15) for the model of multi-year rice was more than that of single year (13). The number of correct discriminations (15) was more than the number of small samples (12). This indicates that the year and the quantity of samples are two important factors to be considered in the establishment of discriminant model. Multiple sample years and multiple sample quantities were used to improve the accuracy of the model. It also provides a theoretical basis for the establishment and standardization of agricultural product traceability method.

Optimization of traditional puffing of a nutritious local grain (Amaranthus spp.) and development of a scalable business model supporting community health

Promoting and encouraging use of diverse nutritious grains is not only healthy but can also, provide livelihood opportunities to rural groups.The study attempted a farm to table approach by standardizing traditional puffing of amaranth grains and training women for a batch scale process to develop a scalable business model. Manual puffing process for amaranth grains was developed and standardized. Puffed samples were compared with market samples for sensory parameters, expansion ratio, nutritive composition and sodium content using SPSS and MS-excel statistical analysis.It was observed that direct heat puffing of cleaned and sieved grains on inclined karahis, provided good puffing efficiency (approximate 94%). Manually puffed grains had similar nutritional values and were crunchier than the market samples. Though, the machine puffed samples had better expansion ratio but, a higher sodium content of 1349.01 ± 0.073 and 1346.15 ± 0.014 mg/100g was reported when compared with manually puffed grains having only 135.3 ±0.037mg/100g of sodium. Individual capacities of women were observed for six months to develop a learning curve to understand production efficiencies. The production capacity of women ranged from 2250 kg to 3250 kg per month. A traceable model was developed, by procuring grains from small and marginal farmers, and processing undertaken by trained women groups. A production of 34, 936 kg of puffed grain in the FY 2020-21 indicated a potential for a sustainable business supporting livelihoods and health.

Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production

Facilities based on the Internet of Things and embedded systems along with the application of ambient intelligence paradigms offer new scenarios for optimization services in agronomic processes, specifically in the hemp industry. The traceability of products and activities demonstrates the scope of these technologies. However, the technologies themselves introduce integration-related problems that can affect the planned benefits. This article proposes a model that balances agricultural expert knowledge (user-centered design), value chain planning (through blockchain implementation), and digital technology (Internet of Things protocols) for providing tamper proof, transparent, and secure traceability in this agricultural sector. The proposed approach is backed by a proof-of-concept implementation in a realist scenario, using embedded devices and a permissioned blockchain. The model and its deployment fully integrate a set of services that other proposals only partially integrate. On one hand, the design creates a permissioned blockchain that contemplates the different actors in the value chain, and on the other hand, it develops services that use applications with human–machine interfaces. Finally, it deploys a network of embedded devices with Internet of Things protocols and control algorithms with automated access to the blockchain for traceability services. Combining digital systems with interoperable human tasks it has been possible to deploy a model that provides a new approach for the development of value-added services.

Traceability model based on improved witness mechanism

Traceability system is one of the popular applications of graphic blockchains. However, there are centralisation problems and a long time for final consistency confirmation in the graphic blockchain. In addition, the blockchain system in the traceability application scenario has the problem of insufficient supervision. Therefore, a witness-based graphic blockchain consensus mechanism is proposed. In the consensus mechanism, a verifiable random function is used to screen the publishers of the unit; an SM2 threshold signature is used to sign the unit information to improve the non-repudiation of the traceability information uploaders to the unit information under the supervision of the witness. The improved consistency algorithm cancels the process of finding a stable main chain and makes relatively many nodes to participate in the consensus process. The experimental results show that the graphic blockchain using the improved witness mechanism can reduce the degree of centralisation, shorten the time for new units to reach consensus, and greatly ensure the security and scalability of the blockchain system.

Receptor-Targeted Dual pH-Triggered Intracellular Protein Transfer.

Protein therapeutics are of widespread interest due to their successful performance in the current pharmaceutical and medical fields, even though their broad applications have been hindered by the lack of an efficient intracellular delivery approach. Herein, we fabricated an active-targeted dual pH-responsive delivery system with favorable tumor cell entry augmented by extracellular pH-triggered charge reversal and tumor receptor targeting and pH-controlled endosomal release in a traceless fashion. As a traceable model protein, the enhanced green fluorescent protein (eGFP) bearing a nuclear localization signal was covalently coupled with a pH-labile traceless azidomethyl-methylmaleic anhydride (AzMMMan) linker followed by functionalization with different molar equivalents of two dibenzocyclooctyne-octa-arginine-cysteine (DBCO-R8C)-modified moieties: polyethylene glycol (PEG)-GE11 peptide for epidermal growth factor receptor-mediated targeting and melittin for endosomal escape. The cationic melittin domain was masked with tetrahydrophthalic anhydride revertible at mild acidic pH 6.5. At the optimally balanced ratio of functional units, the on-demand charge conversion at tumoral extracellular pH 6.5 in combination with GE11-mediated targeting triggered enhanced electrostatic cellular attraction by the R8C cell-penetrating peptides and melittin, as demonstrated by strongly enhanced cellular uptake. Successful endosomal release followed by nuclear localization of the eGFP cargo was obtained by taking advantage of melittin-mediated endosomal escape and rapid traceless release from the AzMMMan linker. The effectiveness of this multifunctional bioresponsive system suggests a promising strategy for delivery of protein drugs toward intracellular targets. A possible therapeutic relevance was indicated by an example of cytosolic delivery of cytochrome c initiating the apoptosis pathway to kill cancer cells.

A traceability model for imported dairy products using multiplatform ID product

Milk is a food product with a long chain circulation pattern. It must be handled correctly to maintain its quality. The variety of products and suppliers creates the need for various product identities, types and patterns. Identity or ID is made in the form of a code of important information. This diversity poses a challenge for industrial-scale recipients to record and obtain information to ensure quality and traceability. This study aims to design a system that can read, store product identity data with complex patterns, different types of codes, and translate them into information. This study uses the SDLC method to build a conceptual framework, where the mapping of needs in this study is shown in UML diagrams, use case diagrams, and BPMN diagrams. The information system is made web-based. Mobile data and information are facilitated by android devices equipped with an ID code scanner (multiplatform). This system works in an online network and automatically provides decision-making information.

Analysis of Agricultural Product Information Traceability and Customer Preference Based on Blockchain

For traditional agricultural product traceability system, there are some problems, such as information asymmetry and serious centralized storage. To solve the problems, a set of an agricultural product traceability information analysis system based on blockchain is designed. The overall framework of the agricultural product information traceability system is built, and the traceability model and process are designed in detail from the data layer, consensus layer, and contract layer. Collaborative verification function modules are added in the data layer to ensure the authenticity of data on the blockchain. The integral penalty mechanism is introduced to improve the practical Byzantine fault tolerance algorithm (PBFT), which ensures the security of the block network and the validity of data. Smart contracts are adopted to ensure that changes in agricultural information are actually recorded on blockchain. The results show that the designed blockchain-based agricultural product information traceability system solves the problem of difficult sharing and traceability of agricultural product information, which realizes the traceability of agricultural product production information, processing information, transportation information, and transaction query information, and has certain theoretical and practical value for the information traceability of agricultural products and other products.

A new data processing strategy combined with a convolutional neural network for rapid and accurate prediction of geographical classifications of natural products

Geographic traceability has attracted attention in the field of food and natural products. It is related to security and quality and is inseparable from the vital interests of producers and consumers. A conventional method for geographic traceability is to combine chemical fingerprints with chemometrics. However, insufficient information is provided from a single detection system for high accuracy traceability. This study uses ‘Two-Elements-Multi-Information-Fingerprints’ (TEMIF) to obtain more fingerprint information, implemented on Cnidium monnieri (L.) Cuss from six different provinces in eastern China. Due to differing polarities, the chemical compositions of the sample can be divided into weak polar parts and strong polar parts. Different chromatographic systems were used to detect different polar compositions. All chromatograms obtained were deconstructed and reconstructed into TEMIF. The frequently used classification models have poor performance in such complex data. Therefore, a convolutional neural network (CNN) was used to process TEMIF. As a result, the more information carried on the fingerprint, the higher is the model's classification accuracy. The accuracy of the CNN model was much higher than that of the traditional machine learning model. Under the same data, support vector machine has the highest accuracy of 62.67% among the three traditional machine learning models, while CNN model can reach 99.80%. On the premise of enriching chemical information, our strategy greatly improved accuracy and made the geographic traceability model more rapid. The online automatic geographic traceability could be realized using a combination of the model and chromatographic workstation.

Information Traceability Model for the Grain and Oil Food Supply Chain Based on Trusted Identification and Trusted Blockchain.

The grain and oil food supply chain has a complex structure, long turnover cycles, and many stakeholders, so it is challenging to maintain the security of this supply chain. A reliable traceability system for the whole grain and oil food supply chain will help to improve the quality and safety of these products, thus enhancing people’s living standards. Driven by the trusted blockchain and trusted identity concepts, this paper constructs an information traceability model for the whole grain and oil food supply chain, and it describes how contract implementation and example verification are performed. First, an information traceability model framework of the whole grain and oil food supply chain is established based on the survey and analysis of the grain and oil food supply chain. Second, trusted identification, blockchain master–slave multi-chain storage, and trusted traceability mechanisms are designed. The trusted identification mechanism is used to track the data information of the whole grain and oil food supply chain. The blockchain master–slave multi-chain storage solves the problem of miscellaneous information caused by many links in the whole grain and oil supply chain, while the credible traceability mechanism ensures the credibility of information collection, storage, and transmission. Finally, based on the data flow, the model operation process is analyzed. Using the information traceability model, the grain and oil food trusted traceability system is designed and developed with the Hyperledger Fabric open-source framework, and a case study is conducted to verify the system. The results show that the model and system constructed in this study solve the problems of low data security and poor sharing, which exist widely in the traditional traceability mechanism, and enable the trusted uplink, storage, processing, and traceability of multi-source heterogeneous information in the lifecycle of the whole grain and oil food supply chain. The proposed system improves the granularity and accuracy of grain and oil food traceability, and provides support for the strategic security of grain stock.

Traceability Models and Traceability Systems to Accelerate the Transition to a Circular Economy: A Systematic Review

Research and implementation efforts and investment in the circular economy are rising sharply. With the high stakes associated with achievements in the field, an increasing emphasis on evaluation, transparency and accountability are to be expected. All require high-quality data, methodologies and tools that are able to improve results and to assess and document the implementation processes and outcomes. A challenging key issue in the implementation of a circular economy is ensuring coordination, control and transparency within a network of parties. Traceability models and systems are vital pillars of such an endeavor, but a preliminary search of the available literature revealed a rather unstable and fragmented research field and practice. The objective of this systematic review was to examine those studies discussing traceability models and traceability systems while connecting traceability capacities and outputs to implement the principles of the circular economy. The literature databases were searched on 6 January 2020, with an update for the entire year of 2020. Overall, 49 studies were included. By addressing eight specific research questions, we found that a link between traceability and the circular economy is yet to be established. Sound research and practice documentation are required to establish evidence regarding this connection, including methodologies that are able to support the design and implementation of business- and lifecycle-oriented, value-based traceability models and traceability systems, along with thorough evaluation methods and tools incorporating economic, social and environmental perspectives.

Geographic origin discrimination of red swamp crayfish Procambarus clarkii from different Chinese regions using mineral element analysis assisted by machine learning techniques

To explore the application prospects of mineral element analysis assisted by machine learning in the geographical origin traceability of red swamp crayfish (Procambarus clarkii) and establish an origin traceability model for it, we used inductively coupled plasma mass spectrometry (ICP-MS) to determine the content of 24 mineral elements, including Cu, Zn, Ga, As, Se, Rb, Sr, and Mo, in red swamp crayfish muscle tissues from Anhui, Hunan, and Hubei provinces of China. The ten characteristic elements, screened by the principal component analysis (PCA), were incorporated into the linear discriminant analysis (LDA) to establish an origin discriminant model of red swamp crayfish. We have verified the feasibility of the mineral element fingerprinting to trace the geographical origin of red swamp crayfish in three regions of China. To improve the discrimination performance, three machine learning classification algorithms, the support vector machine (SVM), the feed-forward neural networks (FNN), and the back-propagation neural network (BPNN), were combined to classify the red swamp crayfish of different origins based on mineral element content. The discriminative accuracy of the training models of both FNN and BPNN reached 100%, and their cross-validation accuracy were 91.88% and 93.13%, respectively, with the area under the receiver operating characteristic (ROC) curve (AUC) values close to 1. Thus, the overall performance of the FNN and BPNN was better than that of SVM. Taken together, our results showed that mineral elements combined with neural networks showed great advantages and potential in geographic origin traceability of red swamp crayfish.

The MLOps Approach to Model Deployment: A Road Map to Seamless Scalability

The operational problems of deploying Machine Learning (ML) models at scale are the focal point of the research, which explores the complex world of MLOps. To determine which operational platforms are most effective in managing deployment pipelines, the research examines MLflow, Kubeflow, and Airflow, among others. Focusing on version management and reproducibility, the article explores methods and resources used to guarantee the longterm viability and traceability of models that have been put into use. This study delves into the incorporation of Continuous Integration and Continuous Deployment (CI/CD) pipelines into MLOps processes, which are crucial for attaining operational effectiveness. Case studies demonstrate how Continuous Integration/Continuous Deployment approaches have helped with deployment constraints and joint development in real-world settings. Common operational issues in MLOps are also covered in the investigation, including dealing with dropping performance, increasing dependencies, and data drift management. Presenting practical insights via a scientific perspective, this work attempts to guide MLOps practitioners as they navigate the ever-changing operational environment of large-scale model deployment.

Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach

Abstract Traceability of food products to their sources is critical for quick responses to food emergencies. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that food traceability is a challenging task, including diversity of stakeholders and their lexicons, standards, tools, and methods; unwillingness to expose information about internal operations; lack of a common understanding of the steps in a supply chain; and incompleteness of data. The objective of this work is to address the traceability challenge by developing a formal ontology that can provide a shared and common understanding of the traceability model across all stakeholders in bulk food supply chains. A formal ontology can support semantic mediation, data integration, and data exploration, thus improving the intelligence and reliability of trace and track process. The Industrial Ontologies Foundry (IOF) procedures and principles are employed in the development of the supply chain traceability ontology. Basic Formal Ontology (BFO) is selected as the top-level ontology. A bottom-up approach is also adopted in a sense that a real use case related to the bulk grain domain is selected to be used for requirements definition and ontology validation. A software tool for visualization of the traceability graph is developed to validate the developed ontology based on simulated data. The test and validation results indicate that the developed ontology has the expressivity needed to represent the semantics of traceability data models within the scope of the selected use case. Also, it was observed that the developed supply chain traceability tool can effectively facilitate the track and trace process through visualizing the Resource Description Framework (RDF) triples, thus eliminating the need to formulate complex SPARQL queries for information retrievals.