This review (purchase required,$USD 31.50) critically evaluates the comparative analytical performance, operational failure modes, and regulatory integration requirements of each technology for food fraud control. The authors assert that rapid DNA-based methods — including polymerase chain reaction (PCR), DNA barcoding, next-generation sequencing (NGS), and CRISPR-Cas diagnostics — have emerged as transformative analytical tools for food fraud control, offering high specificity, applicability to processed matrices, and increasing compatibility with decentralized deployment. Artificial intelligence (AI) further enhances these platforms by enabling automated pattern recognition, fraud risk inference, and intelligence-driven surveillance.
Rather than describing individual technologies in isolation, the authors have set out to give a structured comparative analysis of detection performance across real-world food control conditions, including matrix effects, DNA degradation, and database limitations. They also propose an evidence-based technology selection framework aligned with regulatory enforcement objectives. Finally, they analyse specific standardization and legislative gaps that currently limit operational deployment.
Key findings indicate that CRISPR-Cas assays show the greatest promise for rapid field screening but lacks ISO/AOAC validation; NGS is essential for unknown adulterant discovery but requires bioinformatic standardization; and AI provides the critical decision-support layer for proactive, risk-based surveillance. The authors conclude that these findings are directly relevant to food safety management system design, official method selection, and international regulatory harmonization efforts
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