Multi-Class Veterinary Drugs Analyses of QuEChERS Extracts Using an Automated Online μSPE Cleanup Coupled to LC-MS/MS

Význam tématu

The monitoring of veterinary drug residues in food-producing animals is crucial for ensuring food safety, regulatory compliance, and public health. Conventional sample cleanup methods can be labor intensive and prone to variability, while complex tissue matrices often challenge chromatographic performance. An automated online μSPE cleanup coupled with LC-MS/MS offers a streamlined alternative to enhance throughput, precision, and robustness in multi-class drug screening of animal tissues.

Cíle a přehled studie

This study aimed to develop and evaluate a fully automated workflow integrating QuEChERS extraction, online micro solid phase extraction cartridges, sandwich solvent injection, and high-sensitivity LC-MS/MS analysis. A total of 103 veterinary drug residues spanning diverse chemical classes were tested in bovine muscle, liver, and kidney. Key performance parameters included recovery, precision, linearity, and system robustness over extended injection sequences.

Použitá instrumentace

• Automated sample handling: Thermo Scientific TriPlus RSH μSPE autosampler with PAL3-RTC platform modules
• Liquid chromatography: Thermo Scientific Vanquish Flex binary UHPLC system with Accucore VDX column
• Mass spectrometry: Thermo Scientific TSQ Altis triple quadrupole with electrospray ionization
• μSPE cartridges: 15 mg CEC18 sorbent and 10 mg HyperSep Retain-PEP (HRP) material
• Software: TraceFinder for instrument control and data processing

Metodika

Homogenized bovine tissues underwent QuEChERS extraction with acetonitrile and salts, followed by automated μSPE cleanup. Uncleaned extracts were aspirated onto cartridges, conditioned with methanol and water, then eluted directly into the LC-MS/MS system. A sandwich injection technique placed aqueous mobile phase plugs around the eluent to sharpen early-eluting peaks. Matrix extracted standards (MES) and matrix-matched standards (MMS) at 1–100 ng/g were used for calibration and recovery studies.

Hlavní výsledky a diskuse

The automated μSPE workflow yielded absolute recoveries within 30–140%, with relative standard deviations below 20% for over 95% of the compounds across all tissue types. Calibration curves demonstrated excellent linearity (R2>0.99). The sandwich injection significantly improved peak shape in pure solvent matrices, eliminating the need for manual dilution or solvent exchange. A robustness test of 100 consecutive injections using the CEC18 μSPE cartridge showed consistent response and less than 20% variation, even after a 24-hour standby period.

Přínosy a praktické využití metody

• High-throughput screening with minimal manual intervention and reduced labor time
• Lower solvent consumption and waste generation compared to traditional SPE or dSPE
• Automated calibration standard preparation to minimize human errors
• Enhanced chromatographic performance and matrix cleanup for reliable quantitation
• Robust operation suitable for routine veterinary diagnostic laboratories

Budoucí trendy a možnosti využití

Future developments may include integration of additional sorbent chemistries to expand the scope of target analytes, further miniaturization of extraction cartridges, and coupling with high-resolution mass spectrometry for non-targeted screening. Implementing machine learning algorithms for real-time data quality assessment and predictive maintenance could further enhance system reliability and analytical throughput.

Závěr

The fully automated online μSPE cleanup synchronized with LC-MS/MS analysis presents a robust, efficient, and reproducible method for multi-class veterinary drug residue screening in animal tissues. The approach significantly reduces hands-on time, solvent usage, and manual calibration efforts, while delivering high precision and ruggedness essential for regulatory and research laboratories.

Reference

• Anastassiades M, Lehotay SJ, Stajnbaher D, Schenck FJ J AOAC Int 86 412–431 2003
• AOAC Standard Method Performance Requirements SMPR 2018.010 2018