Selectivity in the sample preparation for the analysis of drug residues in products of animal origin using LC-MS

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Sample preparation is critical in relation to analysis time, sample throughput and therefore analysis costs. Due to recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation, the detection of many compounds within one run became possible, and methods for the simultaneous analysis of different compound groups were developed. To be able to analyze compounds with different physical and chemical properties simultaneously, generic, non-selective sample-preparation procedures are applied. The most frequently reported generic sample-preparation methods are a solvent extraction only, solid-phase extraction and a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) approach. These multi-analyte methods – sometimes including more than 150 different compounds – are of much interest for analytical laboratories due to their reduction in costs. A clear drawback of generic sample-preparation procedures is the occurrence of abundant matrix effects, which compromise detection limits, quantitative aspects, method selectivity and maintenance frequency. In contrast to the trend towards non-selective sample preparation, an opposite trend towards more selective sample-preparation methods is expected to be able to confirm the identity of compounds unambiguously (e.g., stereoisomers). This review gives an overview of generic sample-preparation procedures in the analysis of veterinary drug residues in products of animal origin using LC-MS as the detection technique and an outlook towards expected future trends.
Original languageEnglish
Pages (from-to)229-239
Number of pages11
JournalTrAC : Trends in Analytical Chemistry
Publication statusPublished - 2013


  • tandem mass-spectrometry
  • quantitative trace analysis
  • solid-phase extraction
  • veterinary drugs
  • antibiotic-residues
  • multiclass method
  • environmental-samples
  • multiresidue method
  • screening method
  • food analysis

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