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

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.