This contribution reviews state-of-the approaches for chromatographic fingerprinting of 2D peak patterns. Concepts of sample’s fingerprint and profile, as established in metabolomics, are conceptually translated to comprehensive two-dimensional chromatography (C2DC) separations embracing the principles of biometric fingerprinting. Approaches founded on this principle - referred to as chromatographic fingerprinting - are described and discussed for their information potential and limitations for providing a higher level of information about sample composition. The different type of features (i.e., datapoint, region, peak, and peak-region) are discussed and insights on processing tools and advances in the development of new algorithms are provided. Selected examples cover the most relevant application fields of GC×GC. Challenging scenarios with severe chromatographic misalignment, parallel detection, and translation of methods from thermal to differential-flow modulated GC×GC are also considered for their relevance in specific applications. Machine learning/chemometrics tools are briefly introduced, highlighting their fundamental role in supporting fingerprinting workflows.

Chromatographic fingerprinting by comprehensive two-dimensional chromatography: fundamentals and tools

Stilo, Federico
First
;
Bicchi, Carlo;Cordero, Chiara
2021

Abstract

This contribution reviews state-of-the approaches for chromatographic fingerprinting of 2D peak patterns. Concepts of sample’s fingerprint and profile, as established in metabolomics, are conceptually translated to comprehensive two-dimensional chromatography (C2DC) separations embracing the principles of biometric fingerprinting. Approaches founded on this principle - referred to as chromatographic fingerprinting - are described and discussed for their information potential and limitations for providing a higher level of information about sample composition. The different type of features (i.e., datapoint, region, peak, and peak-region) are discussed and insights on processing tools and advances in the development of new algorithms are provided. Selected examples cover the most relevant application fields of GC×GC. Challenging scenarios with severe chromatographic misalignment, parallel detection, and translation of methods from thermal to differential-flow modulated GC×GC are also considered for their relevance in specific applications. Machine learning/chemometrics tools are briefly introduced, highlighting their fundamental role in supporting fingerprinting workflows.
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Art n° 116133
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chromatographic fingerprinting, comprehensive two-dimensional gas chromatography, multidimensional analytical platforms, peak features, peak-region features, machine learning, chemometrics, profiling vs. fingerprinting, fingerprinting workflows, GC×GC data processing
Stilo, Federico; Bicchi, Carlo; Jimenez-Carvelo, Ana M.; Cuadros-Rodriguez, Luis; Reichenbach, Stephen E.; Cordero, Chiara
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1763280
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