Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

Abstract

The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies

“…On the one hand, the TIC chromatogram for every sample allowed to build up a matrix including all TIC information (size of this matrix was the number of samples by the number of points in the time axis, i.e., retention times). On the other hand, the MSROI procedure generated a features matrix containing the peak areas of the detected features (defined by a m / z value) for each sample [ 32 ].…”

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

“…After preprocessing, the focus shifts to translating complex data into useful information on a sample. Many methods for information extraction have been developed during the last decades [93][94][95][96][97][98]. In this context, the data analysis process can be divided into several levels.…”

Classification of quinoa varieties based on protein fingerprinting by capillary electrophoresis with ultraviolet absorption diode array detection and advanced chemometrics

“…However, the use of ultraviolet absorption diode array detection (UV-DAD) in combination with LC and CE allows the acquisition of three-way datasets (samples, elution/migration times and UVspectra), which have proven to be an enhanced tool in profiling of other type of bioactive components in food and beverages, such as polyphenols in strawberry, olive oil and beer by LC-UV-DAD or CE-UV-DAD (Godoy-Caballero, Culzoni, Galeano-Díaz, & Acedo-Valenzuela, 2013;Mas, Fonrodona, Tauler, & Barbosa, 2007;Pérez-Ràfols & Saurina, 2015). There are different data analysis procedures that allow processing of two-, threeand multi-way data sets (Escandar & Olivieri, 2019;Navarro-Reig, Bedia, Tauler, & Jaumot, 2018). Among them, multivariate curve resolution alternating least squares (MCR-ALS) offers several advantages (Jaumot, de Juan, & Tauler, 2015;Jaumot, Gargallo, de Juan, & Tauler, 2005), as it can resolve overlapped chromatographic or electrophoretic peaks from the collected data and provide the separation profiles and pure spectra of the constituents in the analyzed samples.…”