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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.

GC–MS is a powerful hyphenated method commonly used in forensics and environmental laboratories for precise analysis of mixtures. Gas chromatography uses narrow capillary columns to separate components of a thermally stable volatile mixture and passes them to the mass spectrometer for analysis.

LC–MS is another hyphenated method that couples liquid chromatography with a mass spectrometer to analyze nonvolatile mixtures. To make liquid chromatography compatible with a mass spectrometer, suitable pressure-maintaining ionization interfaces or atmospheric-pressure ionization techniques like electrospray ionization, which is applicable for polar and ionic compounds, or atmospheric-pressure chemical ionization, which applies to less polar molecules, are used.

Another hybrid method, called tandem mass spectroscopy, uses multiple mass analyzers in sequence. Compared to other hyphenated methods, tandem spectroscopy is faster, more sensitive, and more selective due to smaller chemical noise. Tandem spectroscopy can be further combined with separation techniques to form GC–MS/MS or LC–MS/MS for more complex mixture analysis.

Capillary electrophoresis–mass spectrometry is a very sensitive technique commonly used to analyze large biomolecules like DNA, proteins, and polypeptides. This method feeds quadrupole mass analyzers with capillary effluents after passing through an electrospray ionization interface.

Теги
Mass SpectrometryComplex MixturesHyphenated MethodsGC MSLC MSTandem Mass SpectroscopyIonization TechniquesElectrospray IonizationAtmospheric pressure Chemical IonizationCapillary Electrophoresis mass SpectrometryChemical NoiseSensitive Analysis

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