Аннотация
Introduction Digital twins in food production increasingly support quality monitoring, traceability, and operational decision-making. However, a digital twin can only be as reliable as the quality-control evidence used to update its state. In many food-production settings, laboratory quality information is stored in laboratory information management systems (LIMS), supplier certificates, or external laboratory reports, while blockchain-based food studies mainly focus on supply-chain traceability rather than verified laboratory event integration. This paper proposes a minimal Solana-based hash-anchoring framework that allows food-production digital twins to verify whether LIMS-compatible quality-control evidence is original, modified, or missing before accepting it as batch quality-state evidence. Methods Laboratory data remain off-chain, while Solana stores only event identifiers, cryptographic hashes, and minimal metadata. The framework is evaluated using an anonymized bakery flour quality-control case based on two flour certificates and generated LIMS-like events derived from the same field structure. Experiments evaluate tamper detection, anchoring and verification latency, benchmark repeatability, and digital twin state classification. Results In the primary 1,000-event local-validator benchmark, Solana per-event anchoring detected 100% of post-validation modifications under a local-store compromise threat model, with mean anchoring latency of 474.7862 ms per event and mean verification latency of 1.5945 ms. A repeat 1,000-event run produced the same order of overhead, with mean anchoring latency of 472.2501 ms and mean verification latency of 1.2591 ms. Local hash and append-only log baselines were faster, but failed to provide external tamper-evident evidence under local reference compromise. Discussion Under the evaluated conditions, Solana hash anchoring provides an external integrity layer for verified quality-state updates in food-production digital twins, rather than a performance optimization.