The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artifici...
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The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. Machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics. Pattern recognition based on image processing, computer vision, and AI smelling are discussed by introducing the principles of operation, reviewing available tools and software solutions, and illustrating their potentials and limitations through selected applications.
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 t...
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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 x GC. Challenging scenarios with severe chromatographic misalignment, parallel detection, and translation of methods from thermal to differential-flow modulated GC x 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. (C) 2020 Elsevier B.V. All rights reserved.
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