Abstract
The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high‐resolution mass spectrometry, causes an urgent need for highly efficient data‐analysis and optimization strategies. This is especially true for comprehensive two‐dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre‐)processing and analyzing data arising from one‐ and two‐dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.
Highlights
Analytical instruments are indispensable for modern society
A different approach based on Bayesian regularized artificial neural networks (BRANN) [50] was developed by Mani-Varnosfaderani et al The iterative BRANN algorithm was compared to adaptive iteratively reweighted penalized least squares (airPLS), morphologically weighted penalized least squares (MPLS), iPF, and corner cutting (CC) methods using the projected-differenceresolution (PDR) criterion
Both airPLS and asymmetrically reweighted penalized least squares (arPLS) were investigated for baseline correction, while a moving-window strategy was employed for noise removal using the noise estimated from the base-peak profile as a threshold value
Summary
Analytical instruments are indispensable for modern society. To keep pace with the growing needs of society to obtain extended and reliable information on an increasing number of sample characteristics, analytical methods are continuously improved [1]. The employed detection techniques can detect one signal as a function of time, often referred to as single-channel data, or a spectrum at every point in time This multi-channel data may facilitate identification or quantification of the analyte represented by the chromatographic signal. When applied to highly complex samples even with 2D chromatography, it can still be difficult to extract accurate and correct information from the obtained results. Samples such as copolymer formulations [9,10], food [11,12], protein digests [13,14], metabolic mixtures [15], and oil mixtures [16,17,18] may contain thousands of different components. Many of the chemometric strategies used in 2D chromatography are based on the analysis of 1D chromatograms
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