Smart FDA-Grade Food Safety by Blockchain-Traceable IoT Sensor Network

Thesis Abstract

The globalization of food supply chains has heightened the need for end-to-end traceability, real-time safety verification, and rapid incident response in order to protect public health and maintain consumer trust. Despite advances in food safety regulations, current systems often rely on fragmented records, manual auditing, and opaque exchange of information, which impede timely detection of contamination, mislabeling, and adulteration. This study investigates a blockchain-enabled, Internet of Things (IoT) sensor network designed to deliver FDA-grade food safety through immutable provenance, real-time monitoring, and secure data sharing among stakeholders across the supply chain. The aim is to design, implement, and evaluate a technology-augmented framework that integrates IoT sensors, smart contract-enabled data governance, and scalable analytics to ensure compliance with FDA standards from farm to fork. Specific objectives include (1) to engineer an interoperable IoT sensor suite (temperature, humidity, pH, and geolocation) with tamper-evident data packaging and encryption, (2) to develop a permissioned blockchain architecture that supports traceability, auditability, and automated compliance checks aligned with FDA requirements, (3) to create analytical models that detect anomalies and forecast safety risk using time-series and causal inference techniques, (4) to assess user acceptance and operational impact among growers, processors, distributors, and retailers, and (5) to evaluate system performance in a simulated but realistic supply-chain pilot involving 10 farms, 6 processing facilities, and 8 distribution centers over a 12-month period. The research adopts a pragmatic mixed-methods design, combining quantitative sensor data analytics with qualitative stakeholder insights. The population comprises actors across five supply-chain tiers within a metropolitan food system. A stratified random sample will recruit 200 stakeholders for survey-based measures of usability, trust, and perceived risk, while 20 expert interviews will probe governance challenges and implementation strategies. The IoT subsystem will deploy 500 battery-powered sensors across the pilot network, transmitting data to edge gateways and a Hyperledger Fabric-based blockchain, with data pushed to a centralized analytics platform. Instruments include calibrated environmental sensors, a validated survey instrument for technology acceptance (TAM-inspired), and semi-structured interview guides. Validity and reliability will be ensured through triangulation, pilot testing of instruments (n=20), and test-retest reliability checks on survey scales. Analytical techniques encompass time-series regression and anomaly detection (LSTM networks) for sensor streams, multivariate regression to link environmental conditions with safety incidents, and survival analysis to model time-to-incident risk. The blockchain layer will be evaluated for throughput, latency, and fault tolerance under peak load, using stress testing and queuing theory modeling. Thematic analysis will interpret qualitative data, guided by institutional theory and the Technology Acceptance Model (TAM) to contextualize adoption dynamics. A conceptual model will map the interrelations among sensor fidelity, blockchain governance, and safety outcomes. Expected findings include (i) improved detection and containment of contamination through near-real-time alerts triggered by integrated sensor-data anomalies, (ii) enhanced data integrity and traceability evidenced by reduced data tampering risk and transparent provenance records, (iii) statistically significant associations between environmental conditions and incident risk that enable proactive mitigation, and (iv) positive shifts in stakeholders’ perceived usefulness and ease of use, moderated by trust in data integrity and organizational readiness. The study anticipates performance metrics such as sub-second latency for critical events, 99.9% data availability, and a 30% reduction in time to recall initiation compared with conventional systems. The contribution to knowledge lies in advancing an end-to-end, blockchain-empowered, IoT-driven framework that operationalizes FDA-grade safety criteria within modern supply chains, provides a transferable architectural blueprint for scalable adoption, and offers empirically grounded insights into governance, trust, and practical deployment barriers. The main conclusion is that integrating a permissioned blockchain with an IoT sensor network can deliver verifiable FDA-compliant provenance, real-time safety monitoring, and enhanced stakeholder collaboration, without compromising performance or data privacy. Recommendations include establishing standardized data schemas and interoperability protocols across partners, scalable governance models for smart contracts aligned with FDA regulatory pathways, and phased rollouts accompanied by capability-building programs to foster organizational readiness. Further research should explore cross-sector generalizability to perishables with higher risk profiles, the integration of genomic traceability methods, and the economic evaluation of total cost of ownership under varied regulatory regimes.