Recent Advances in Pretreatment Methods and Detection Techniques for Veterinary Drug Residues in Animal-Derived Foods
Abstract
1. Introduction
2. Pretreatment Methods
2.1. Liquid–Liquid Extraction (LLE) Technology
2.2. SPE
2.3. Immunoaffinity Chromatography (IAC)
2.4. QuEChERS
2.5. Molecular Imprinting Technology (MIT)
3. Chromatographic Detection Techniques
3.1. GC-MS
3.2. Liquid Chromatography Quadrupole-Time-of-Flight Mass Spectrometry (LC-QTOF-MS)
3.3. Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)
3.4. Liquid Chromatography Coupled to Ion Trap Mass Spectrometry (LC-IT-MS)
3.5. CE-MS
4. Rapid Detection Techniques
4.1. Immunoassay Analysis Techniques
4.1.1. GICA
4.1.2. ELISA
4.2. Fluorescence Polarization Immunoassay (FPIA)
4.3. Surface-Enhanced Raman Scattering (SERS)
5. Comparative Analysis of Merits and Limitations in Contemporary Veterinary Drug Residue Analytical Techniques for Animal-Derived Food Products
6. Conclusions and Further Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | LOD (μg/kg) | Accuracy (Recovery Rate) | Applicability | Advantages | Disadvantages | Feasibility | Cost-Effectiveness |
---|---|---|---|---|---|---|---|
LLE | 0.5–1.0 | 71.4–120% | Suitable for lipophilic drugs in aquatic products, milk, and tissues. | Excellent selectivity for lipophilic drugs. Simple operation with minimal equipment. Low cost. | High solvent consumption. Prone to emulsification. Limited extraction efficiency for polar analytes. | Simple but requires solvent management. | Low |
SPE | 0.2–3.0 | 60–120% | Broad applicability for diverse matrices (honey, muscle, milk, and eggs). | Effective enrichment of trace residues. High selectivity with tailored adsorbents. Reduced solvent use. | High adsorbent costs. Complex optimization for matrix effects. Susceptible to column clogging. | Requires skilled optimization. | Moderate to high |
IAC | 0.04–0.10 | 74.5–105% | Ideal for specific targets (e.g., chloramphenicol and β-agonists) in muscle/liver. | Exceptional specificity via antibody–antigen binding. High sensitivity for trace residues. High-throughput potential. | Antibody development is costly/time-consuming. Limited column lifespan. Cross-reactivity risks. | Antibody-dependent and storage-sensitive. | High |
QuEChERS | 0.15–3.03 | 52.1–138.2% | Effective for high-fat matrices (beef and chicken) and multi-residue analysis. | Rapid and simple. Cost-effective with minimal solvents. Effective impurity removal. | Sorbent selectivity limitations. Residual matrix interference. Optimization challenges for diverse analytes. | Easy to implement with standard lab tools. | Low to moderate |
MIT | 0.05–0.5 | 68.6–95.5% | Customizable for antibiotics (e.g., tetracyclines and β-agonists) in complex matrices. | Tailored specificity via imprinting. Reusable and stable. Adaptable to diverse targets. | Labor-intensive synthesis. Cross-reactivity with structural analogs. Requires confirmatory methods. | Specialized expertise needed for polymer design. | Moderate |
Method | LOD (μg/kg) | Accuracy (Recovery Rate) | Applicability | Advantages | Disadvantages | Feasibility | Costing |
---|---|---|---|---|---|---|---|
GC-MS | 2.3–4.3 | 77.38–95.7% | Volatile/semi-volatile compounds. | High specificity for volatile analytes. Robust qualitative capabilities. Wide applicability for small molecules. | Requires derivatization for non-volatile compounds. Limited to thermally stable analytes. | Requires derivatization expertise. | Moderate to high |
LC-QTOF-MS | 0.5 | More than 70% | High-resolution multi-residue screening. | Ultra-high resolution for accurate mass identification. Broad-spectrum detection. Rich structural data. | High equipment/maintenance costs. Demands advanced data analysis skills. | Requires high-end infrastructure. | Very high |
LC-MS/MS | 0.02–82 | 70–120% | Gold standard for trace-level quantification. | High sensitivity and selectivity. Reliable for multi-residue analysis. Robust quantitative accuracy. | Expensive instrumentation. Complex sample preparation. Matrix effects require mitigation. | Skilled operation and maintenance needed. | High |
LC-IT-MS | 0.01–18.75 | 63–122% | Multi-stage fragmentation for structural elucidation. | Mul-ti-stage mass for structural insights. Compact and cost-effective. | Slower scanning speeds. Moderate resolution limits complex mixture analysis. | Suitable for targeted analysis. | Moderate |
CE-MS | 1–9 | More than 78% | Ionizable metabolites. | High separation efficiency. Minimal sample/reagent consumption. Fast analysis. | Poor reproducibility due to buffer/temperature sensitivity. | Technically demanding for calibration. | Moderate |
GICA | 0.01–0.5 | 84.2–112.9% | Rapid on-site screening. | Equipment-free, rapid results. Low cost and user-friendly. | Qualitative/semi-quantitative only. Limited sensitivity for trace residues. Matrix interference risks. | Ideal for field testing. | Low |
ELISA | 1.56–2.72 | 70.1–103.1% | High-throughput screening. | High throughput and specificity. Cost-effective for batch analysis. Minimal instrumentation. | Cross-reactivity with analogs. Enzyme activity affected by environmental factors. | Requires antibody development. | Low to moderate |
FPIA | 0.01 | 78.6–107.77% | Homogeneous assays. | Rapid and homogeneous. Minimal sample pretreatment. Moderate sensitivity. | Limited by antibody/tracer availability. Matrix interference in complex samples. | Suitable for simple matrices. | Moderate |
SERS | 0.01–0.015 | 88.8–111.3% | Ultra-trace detection. | Ultra-high sensitivity. Rapid and minimal pretreatment. Multiplexing potential. | Poor reproducibility due to nanoparticle variability. | Requires nanoparticle optimization. | High |
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Dai, Q.; Tang, S.; Dai, C. Recent Advances in Pretreatment Methods and Detection Techniques for Veterinary Drug Residues in Animal-Derived Foods. Metabolites 2025, 15, 233. https://doi.org/10.3390/metabo15040233
Dai Q, Tang S, Dai C. Recent Advances in Pretreatment Methods and Detection Techniques for Veterinary Drug Residues in Animal-Derived Foods. Metabolites. 2025; 15(4):233. https://doi.org/10.3390/metabo15040233
Chicago/Turabian StyleDai, Qing, Shusheng Tang, and Chongshan Dai. 2025. "Recent Advances in Pretreatment Methods and Detection Techniques for Veterinary Drug Residues in Animal-Derived Foods" Metabolites 15, no. 4: 233. https://doi.org/10.3390/metabo15040233
APA StyleDai, Q., Tang, S., & Dai, C. (2025). Recent Advances in Pretreatment Methods and Detection Techniques for Veterinary Drug Residues in Animal-Derived Foods. Metabolites, 15(4), 233. https://doi.org/10.3390/metabo15040233