Pattern-Based Risk Mapping of Pesticide Residues in Turkish Horticultural Exports Using RASFF Alerts (2020-2025) - Kirtil, Emrah (Mdpi, 2025)
Pesticide residue violations continue to challenge the compliance of Turkish horticultural exports with European Union food safety regulations. This study examined 1138 RASFF alerts (1660 detections) issued between 2020 and 2025 using statistical enrichment, time-series modeling, and unsupervised machine learning. Pepper was the most frequently rejected commodity, with strong enrichments of formetanate (71-fold), pyridaben (35-fold), and acetamiprid (5-fold). Notably, chlorpyrifos and chlorpyrifos-methyl remained among the most commonly detected residues despite EU bans, suggesting continued use of stockpiled or illicit products and prolonged environmental persistence. Rejections peaked during winter and spring, particularly for citrus and greenhouse-grown crops. Clustering and association rule mining revealed modular commodity-pesticide structures and recurrent co-detection patterns. Anomaly detection further identified discrete periods of irregular contamination. Overall, the results indicate that violations are seasonally patterned and structurally embedded. Targeted monitoring aligned with crop calendars and stricter enforcement of legacy pesticide phase-outs could significantly improve compliance and reduce export rejections.
Keywords: pesticide residues; maximum residue limits (MRLs); EU border controls; horticultural trade; anomaly detection; network analysis; unsupervised learning; seasonal risk mapping