Challenges in Obtaining Relevant Information from One- and Two-Dimensional LC Experiments

To address the quest for greater separation power, the chromatographic community developed comprehensive two-dimensional liquid chromatography (LCxLC). But even with LCxLC, it can still be challenging to analyze highly complex samples and obtain accurate and correct information. In this article, opportunities for optimizing methods for extracting maximum information from one-dimensional (1D)-LC and two-dimensional (2D)-LC chromatographic data are explained.

Liquid chromatography (LC) methods are continuously improving to maintain our ability to meet the growing need of society to obtain more reliable information about a number of sample characteristics. With the samples subjected to LC analysis becoming increasingly complex, analysis of the resulting convoluted data has been increasingly challenging. To aid identification and quantification, LC systems were hyphenated with multichannel detectors (such as mass spectrometry [MS] and ultraviolet-visible spectroscopy [UV-vis]), which yielded relief to some extent, but also required new advanced data analysis methods. Not waiting for an answer, the chromatographic community resorted to the tool it understood best to address the quest for more separation power, and developed comprehensive two-dimensional (2D) chromatography. However, even with 2D chromatography, it can still be difficult to extract accurate and correct information from the results obtained for highly complex samples. Use of sophisticated detectors, such as high-resolution mass spectrometers, certainly helps, but also generates mountains of data. Arguably, extracting all relevant information is the biggest challenge currently faced in high-resolution chromatography. In this article, the challenge of and opportunities for extracting information from one-dimensional (1D)-LC and 2D-LC data is explained.