A chemometric approach based on Autocovariance Function ( EACVF) computation was found reliable to characterize the complex signals obtained fro GC-MS analysis on aerosol samples. as alternative to the conventional method based on peak integration, In particular, the n-alkane homologous series is characterized using two parameters: Carbon Preference Intex, CPI, and series%, estimating the % abundance relative to total alkane concentration. These two parameters have proven useful chemical markers for tracking the biogenic and anthropogenic origins of n-alkanes.

A chemometric approach based on the AutoCoVariance Function for handling complex signals from environmental monitoring

PIETROGRANDE, Maria Chiara;MERCURIALI, Mattia;MARCHETTI, Nicola;PASTI, Luisa;BACCO, Dimitri;ZANGHIRATI, Gaetano;DONDI, Francesco
2009

Abstract

A chemometric approach based on Autocovariance Function ( EACVF) computation was found reliable to characterize the complex signals obtained fro GC-MS analysis on aerosol samples. as alternative to the conventional method based on peak integration, In particular, the n-alkane homologous series is characterized using two parameters: Carbon Preference Intex, CPI, and series%, estimating the % abundance relative to total alkane concentration. These two parameters have proven useful chemical markers for tracking the biogenic and anthropogenic origins of n-alkanes.
chemometric approach; AutoCoVariance Function; decoding complex signals; environmental monitoring
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1400567
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