Recent advances in molecular recognition and ultrasensitive detection of growth-promoting drug residues in meat and meat products

Abstract

Background

Meat and meat products are rich in nutrient supply but accompanied with potential safety issues. Especially, illegal veterinary drugs have to be banned due to serious effects on human body. Among these, growth-promoting drugs (GPDs) should be emphasized because of high detection rate in meat and meat products in many countries. Hence, it is urgent to exploit rapid, sensitive and smart detection methods for monitoring GPD residues and ensuring meat safety.

Scope and approach

Both molecular recognition and ultrasensitive detection are necessary for monitoring GPDs. Herein, we introduced five predominant recognition manners, including molecular polarity-based extraction, reaction-based molecular recognition, antibody-based immune recognition, aptamer-based affinity recognition and molecular imprinting-based biomimetic recognition. Then, we compared five effective detection techniques, involving novel liquid-phase chromatography tandem mass spectrometry, electrochemical sensing, enzyme linked immunosorbent assay, lateral flow immunoassay and emerging dual-mode detection.

Key findings and conclusions

Comparison of GPD recognition and detection methods revealed that conventional polarity-based extraction was still necessary for molecular identification but not suitable for rapid on-site detection. Highly specific immune, affinity and biomimetic recognition were more widely employed by virtue of multiple binding ability and coupled with various nanomaterials as effective signal amplification medium for fast and ultrasensitive detection. However, the robustness and accuracy of rapid GPD detection should be noticed, which could be solved by emerging dual-mode detection strategies. Rapid, accurate, sensitive, multiplex and portable detection of GPDs in meat and meat products remain to be explored in the future for intelligent and proactive monitoring of potential safety issues.