2026-10-10T07:50:38Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33986953 2026-09-24T16:32:59Z category_575 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Knowledge graph-based expert system for tea pesticide residue compliance verification and detection method selection optimization.csv Yanyan Zhang (1462111) Jianrong Wen (25104793) Xuexiang Su (25104796) Tingxin Chen (25104799) Min Xie (273315) Jingdong Chen (4242991) Food Packaging, Preservation and Safety compliance verification detection method selection optimization expert system food safety informatics GB 2763 knowledge graph laboratory information systems tea pesticide residues Introduction<p>Strict Maximum Residue Limits (MRLs) and complex residue definitions in pesticide standards (e.g., GB 2763) pose significant challenges for compliance verification and detection method selection in tea safety management. Manual inspection is prone to systematic errors, particularly false negatives caused by neglecting metabolites or isomers.</p>Methods<p>This study proposes a Knowledge Graph-based Expert System to address these issues. We constructed a domain-specific knowledge graph integrating 110 tea-registered pesticide active substances (comprising 847 entities and 2,156 relationships) and 37 detection standards using a human-in-the-loop verification process. A semantic classification model categorizing residue definitions into six patterns (Type A–F) was developed to enable automated, logic-driven compliance verification. A weighted set-cover algorithm was implemented to optimize the selection of detection methods among existing standardized methods. Validation included a small exploratory pilot user study (n = 5) and a retrospective analysis of 500 historical laboratory samples.</p>Results<p>In the retrospective analysis, the system’s automated verdicts agreed with the laboratory’s human Final Verdict in all 500 cases (95% CI: 99.3%–100.0%), demonstrating faithful reproduction of institutional interpretation of GB 2763. Notably, the system independently flagged three metabolite-summation (Type B) cases that first-level manual review had passed, illustrating its value as a redundant safeguard for multi-component residue definitions. The pilot user study illustrated qualitative trends consistent with cognitive load theory, whereas the system produced deterministic verdicts at a mean of 0.63 s per sample.</p>Discussion<p>This system provides an effective decision-support tool for laboratory quality control and regulatory compliance.</p> 2026-09-24T16:32:59Z Dataset Dataset 10.3389/fsufs.2026.1865601.s002 https://figshare.com/articles/dataset/Data_Sheet_1_Knowledge_graph-based_expert_system_for_tea_pesticide_residue_compliance_verification_and_detection_method_selection_optimization_csv/33986953 CC BY 4.0