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Data Analysis

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Two-Dimensional Liquid Chromatography

Part of the book series: SpringerBriefs in Molecular Science ((BRIEFSMOLECULAR))

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Abstract

2DLC can resolve far more peaks than standard HPLC but it also produces far more complex data. We are at a point in analytical science where running samples and generating terabytes of data is often the simplest part of the workflow and analysing the data properly takes up the majority of the analyst’s time. The question then becomes how to draw meaningful insights from these datasets, and once the data’s salient features are extracted, how can we best identify them, and infer meaning from that list of identified compounds. The data must be processed properly to create useful chromatograms, identify all the peaks, and generate new knowledge from the data obtained. There are a number of different ways to do this ranging from pre-process smoothing algorithms to geometric approach factor analysis. Understanding these methods as well as the format of the raw data is an important part of the 2DLC workflow.

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Authors and Affiliations

  1. Australian Center for Research on Separation Science, School of Science, RMIT University, Bundoora West Campus, PO Box 71, Bundoora, VIC, 3083, Australia

    Oliver Jones

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Correspondence to Oliver Jones .

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Jones, O. (2020). Data Analysis. In: Two-Dimensional Liquid Chromatography. SpringerBriefs in Molecular Science. Springer, Singapore. https://doi.org/10.1007/978-981-15-6190-0_4

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