In this study, researchers assess RPLC analyses for use in the screening for drug compounds.
This paper evaluating the identification power of RPLC analyses in the screening for drug compounds demonstrates that selecting the most dissimilar columns based on measures such as correlation or Snyder’s Fs value is not optimal, because these measures do not take into account the identification power of the individual systems. Three other measures are tested to evaluate the identification power obtained by parallel screening on two columns or by comprehensive two-dimensional LC (LC × LC). The simplest approach is counting the number of compounds separable with a difference in retention time greater than a predefined critical value. However, this measure does not reflect the coelution pattern of the unidentified drugs nor the separation degree of all compounds. The second tested measure, information, enables differentiation between systems identifying the same number of compounds but resulting in a different coelution pattern. Multivariate selectivity, the third tested parameter, takes into account the degree of separation of all compounds and has the advantage that it reflects the gain in identification power achieved by introducing DAD data. All three proposed measures also enable evaluation of whether the corresponding LC × LC method will result in a greater identification power. The identification of drugs of abuse is an important issue in forensic science. The main goal is to trace and identify as many drugs as possible in the shortest possible time preferably with a simple analysis method. One possibility is to screen samples using a Liquid Chromatography-Diode Array Detection (LC-DAD) system. However, when simultaneously performing another analysis on a chromatographic column exhibiting selectivity differences from the first one, that is, orthogonal or dissimilar columns, a greater number of drugs can be possibly identified without investing a lot of extra time or money. The primary difficulty is then selecting the most appropriate columns. (Published Abstract Provided)
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