TY - JOUR
T1 - Chromatographic fingerprinting by comprehensive two-dimensional chromatography
T2 - Fundamentals and tools
AU - Stilo, Federico
AU - Bicchi, Carlo
AU - Jimenez-Carvelo, Ana M.
AU - Cuadros-Rodriguez, Luis
AU - Reichenbach, Stephen E.
AU - Cordero, Chiara
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Chemometrics
KW - Chromatographic fingerprinting
KW - Comprehensive two-dimensional gas chromatography
KW - Fingerprinting workflows
KW - GC×GC data processing
KW - Machine learning
KW - Multidimensional analytical platforms
KW - Peak features
KW - Peak-region features
KW - Profiling vs. fingerprinting
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U2 - 10.1016/j.trac.2020.116133
DO - 10.1016/j.trac.2020.116133
M3 - Review article
AN - SCOPUS:85097347316
SN - 0165-9936
VL - 134
JO - TrAC - Trends in Analytical Chemistry
JF - TrAC - Trends in Analytical Chemistry
M1 - 116133
ER -