Total selenium and selenomethionine in pharmaceutical yeast tablets: assessment of the state of the art of measurement capabilities through international intercomparison CCQM-P86.

Results of an international intercomparison study (CCQM-P86) to assess the analytical capabilities of national metrology institutes (NMIs) and selected expert laboratories worldwide to accurately quantitate the mass fraction of selenomethionine (SeMet) and total Se in pharmaceutical tablets of selenised-yeast supplements (produced by Pharma Nord, Denmark) are presented. The study, jointly coordinated by LGC Ltd., UK, and the Institute for National Measurement Standards, National Research Council of Canada (NRCC), was conducted under the auspices of the Comité Consultatif pour la Quantité de Matière (CCQM) Inorganic Analysis Working Group and involved 15 laboratories (from 12 countries), of which ten were NMIs. Apart from a protocol for determination of moisture content and the provision of the certified reference material (CRM) SELM-1 to be used as the quality control sample, no sample preparation/extraction method was prescribed. A variety of approaches was thus used, including single-step and multiple-step enzymatic hydrolysis, enzymatic probe sonication and hydrolysis with methanesulfonic acid for SeMet, as well as microwave-assisted acid digestion and enzymatic probe sonication for total Se. For total Se, detection techniques included inductively coupled plasma (ICP) mass spectrometry (MS) with external calibration, standard additions or isotope dilution MS (IDMS), inductively coupled plasma optical emission spectrometry , flame atomic absorption spectrometry and instrumental neutron activation analysis. For determination of SeMet in the tablets, five NMIs and three academic/institute laboratories (of a total of five) relied upon measurements using IDMS. For species-specific IDMS measurements, an isotopically enriched standard of SeMet (76Se-enriched SeMet) was made available. A novel aspect of this study relies on the approach used to distinguish any errors which arise during analysis of a SeMet calibration solution from those which occur during analysis of the matrix. To help those participants undertaking SeMet analysis to do this, a blind sample in the form of a standard solution of natural abundance SeMet in 0.1 M HCl (with an expected value of 956 mg kg(-1) SeMet) was provided. Both high-performance liquid chromatography (HPLC)-ICP-MS or gas chromatography (GC)-ICP-MS and GC-MS techniques were used for quantitation of SeMet. Several advances in analytical methods for determination of SeMet were identified, including the combined use of double IDMS with HPLC-ICP-MS following extraction with methanesulfonic acid and simplified two-step enzymatic hydrolysis with protease/lipase/driselase followed by HPLC-ICP-IDMS, both using a species-specific IDMS approach. Overall, satisfactory agreement amongst participants was achieved; results averaged 337.6 mg kg(-1) (n = 13, with a standard deviation of 9.7 mg kg(-1)) and 561.5 mg kg(-1) (n = 11, with a standard deviation of 44.3 mg kg(-1)) with median values of 337.6 and 575.0 mg kg(-1) for total Se and SeMet, respectively. Recovery of SeMet from SELM-1 averaged 95.0% (n = 9). The ability of NMIs and expert laboratories worldwide to deliver accurate results for total Se and SeMet in such materials (selensied-yeast tablets containing approximately 300 mg kg(-1) Se) with 10% expanded uncertainty was demonstrated. The problems addressed in achieving accurate quantitation of SeMet in this product are representative of those encountered with a wide range of organometallic species in a number of common matrices.

Investigation of heroin profiling using trace organic impurities.

The acidic and neutral impurities in heroin samples (46 Purified South-East Asian (PSEA) and 8 non-PSEA samples) were analysed using gas chromatography. Mass spectral detection allowed the construction of a comprehensive library of over 649 impurities. A variety of statistical procedures were used to select 70 viable profiling parameters. Cluster analyses were used to investigate the similarities between samples using organic parameters. A blended PSEA profile was constructed for comparison with samples of unknown origins. Resolution between samples of PSEA/non-PSEA origins was demonstrated with a selection of 25 (18 continuous and 7 dichotomised) discriminating factors. Several predictive logistic models were created using up to 18 parameters, explaining 85-100% of the variation in the experimental data.