Gas Chromatography-Mass Spectrometry (GC-MS) is the best analytical technique for the determination of these organic compounds but it produces large amount of data when it is applied on such complex mixtures like environmental samples. Moreover there could be even interfering substances, artifacts, noise and data redundancy. Homologous series of n-alkanes and n-alcanoic acids are usually used as molecular tracers: they are common to multiple sources and they help to differentiate aerosols of anthropogenic origin (i.e. associated with industrial and urban activities) from those of natural, biogenic origin (i.e. epicuticular waxes and pollens) This work describes the application of a signal processing method to GC–MS chromatograms of PM10 and PM2.5 samples collected in rural and urban areas. The method is focused on the computation of two relevant parameters -- number of series term, nmax, and Carbon Preference Index, CPI, -- that can be directly estimated from the AutoCoVariance Function (ACVF) computed on the acquired chromatogram. The procedure makes it possible to extract usable information hidden in the chromatogram thus reducing the labour and time required and increasing the quality and objectivity of the results.
Data handling of complex GC-MS signals: characterization of homologous series as organic tracers in environmental samples.
PIETROGRANDE, Maria Chiara;MERCURIALI, Mattia;BACCO, Dimitri;DONDI, Francesco
2010
Abstract
Gas Chromatography-Mass Spectrometry (GC-MS) is the best analytical technique for the determination of these organic compounds but it produces large amount of data when it is applied on such complex mixtures like environmental samples. Moreover there could be even interfering substances, artifacts, noise and data redundancy. Homologous series of n-alkanes and n-alcanoic acids are usually used as molecular tracers: they are common to multiple sources and they help to differentiate aerosols of anthropogenic origin (i.e. associated with industrial and urban activities) from those of natural, biogenic origin (i.e. epicuticular waxes and pollens) This work describes the application of a signal processing method to GC–MS chromatograms of PM10 and PM2.5 samples collected in rural and urban areas. The method is focused on the computation of two relevant parameters -- number of series term, nmax, and Carbon Preference Index, CPI, -- that can be directly estimated from the AutoCoVariance Function (ACVF) computed on the acquired chromatogram. The procedure makes it possible to extract usable information hidden in the chromatogram thus reducing the labour and time required and increasing the quality and objectivity of the results.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
