Abstract

Systematic investigations of the impact of nitric acid, inorganic salts and organic compounds on the decrease in the analytical signals in the analysis of whole blood, serum and urine by mass spectrometry with inductively coupled plasma are performed. Two strategies for the sample pretreatment – dilution by different organic mixtures and acidic mineralization – are investigated. The maximum effect of the nitric acid and salts on the results of the determination of the diagnostically important elements P, Mn, Co, Cu, Zn, As, Se, Cd and Pb were found using synthetic model solutions. Analytes with ionization potentials (IP) above 9.0 eV are found to be affected most by these non-spectral interferences. The possibility of avoiding such interferences through simply specific tuning of the instrumental parameters is investigated. It is found that the greatest reduction of the interferences is seen for an increase in the RF power and a decrease in the velocity of the nebulized argon flow. Various strategies for selection of an internal standard (IS) for different tunings of the instrument are found. Two approaches for elimination of the non-spectral interferences are established: i) combination of a standard set of instrumental parameters and the use of the IS with IPs matched to the IPs of the analytes; ii) combination of “robust” instrumental parameters and the use of a single IS with any mass and IP. The first approach provides higher sensitivity but requires the use of different IS for different analytes. The second approach provides a much more flexible analytical performance for the routine determination of the various sets of analytes but at the expense of a modest decrease in the analytical sensitivity.The efficiency of the developed strategy is demonstrated by the analysis of standard reference materials of whole blood and urine and applied to the analysis of bio-liquids of patients suffering from different forms of a systematic inflammatory response syndrome (SIRS). The concentrations of Cu and Zn were found to be diagnostically important features.

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