Background Correction and Multivariate Curve Resolution of Online Liquid Chromatography with Infrared Spectrometric Detection

Abstract

The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.

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

“…MCR requires the data to satisfy the condition of bilinearity. Examples of its application include LC-DAD and LC-MS data [36,37]. MCR decomposes a matrix into pure chromatographic and spectral profiles, plus noise or error, as in equation (5) = + (5) in which represents the recorded data, and and the pure chromatographic and spectral profiles of the components in the sample, respectively.…”

“…A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data Wavelet transform 2006 [30] Baseline correction of spectra in Fourier transform infrared: Interactive drawing with Bézier curves Bezier smoothing 1998 [42] A general baseline-recognition and baseline-flattening algorithm Curve fitting 1977 [21] The elimination of errors due to baseline drift in the measurement of peak areas in gas chromatography (Blank) Subtraction 1965 [20] On a New Method of Graduation Smoothing 1922 [27] Background correction and multivariate curve resolution of online LC with IR detection. MCR-ALS 2011 [37] Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution MCR-ALS 1995 [34] Mixture models for baseline estimation Mixture model 2012 [49] Morphology-based automated baseline removal for Raman spectra of artistic pigments Morphological correction 2010 [33] Automatic correction of continuum background in Laser-induced Breakdown Spectroscopy using a model-free algorithm Automatic time-shift alignment method for chromatographic data analysis ATSA 2017 [69] GC × GC retention time shift correction and modelling using bilinear peak alignment, correlation optimized shifting and MCR.…”

Current challenges in second‐order calibration of hyphenated chromatographic data for analysis of highly complex samples

“…The background correction methods fall into 2 main categories: either the background contribution is estimated beforehand (for example, using spectral baseline correction or by fitting to B‐splines in two‐way of measurements) and removed from each data matrix before modeling by second‐order calibration methods, or the drift is modeled as one or more factors throughout performing the multiway or multiset methods on the raw chromatographic data . During the last few years, some protocols have been suitably proposed for correcting or modeling background drift in two‐way chromatographic data, such as using MCR‐ALS for modeling background signal of GC × GC‐TOFMS data of complex mixture of polycyclic aromatic hydrocarbons (PAHs), using ATLD for modeling background contribution of LC × LC‐DAD signals, developing 2 straightforward methods based on simple‐to‐use interactive self‐modeling mixture analysis and principal component analysis to remove background signal and facilitate the performance of MCR‐ALS modeling, using orthogonal spectral signal projection to simultaneously treat various types of background drift . Also, the other methods, which were suitably proposed for correcting background drift in two‐way chromatographic data, can be found in the literature …”

Pixel-Level Data Analysis Methods for Comprehensive Two-Dimensional Chromatography

“…It can also be applied to unfolded pixel-level LC Â LC-DAD or GC Â GC-TOFMS chromatograms wherein 1 t R and 2 t R are properly augmented in the same dimension or multiple sample chromatograms have been properly augmented to yield a bilinear 2D data matrix [66][67][68][69][70].…”

“…Similarly, for LC Â LC-DAD, under some circumstances the data may not be sufficiently trilinear, for example, when the data are collected over a relatively long period of time [70]. However, Tauler, Rutan, and others have shown that the data can be readily analyzed if the data array is properly unfolded so that the 1 t R and 2 t R indices are augmented, producing a bilinear 2D (A*B Â J) matrix [66][67][68][69][70]. For the remaining discussion of the matrix dimensions and MCR-ALS, A*B will be replaced with I, the number of pixels in the unfolded dimension.…”