Multivariate background correction for hyphenated chromatography detectors

Abstract

This paper reports a new multivariate method of background correction for hyphenated chromatography data such as diode array HPLC and diode array capillary electrophoresis (CE) measured under conditions of low signal‐to‐noise. The new method is able to correct linear and curved dynamically shifting baselines, a significant problem that limits the precision of CE assays. Its use is illustrated with nine simulated and 63 measured data sets. Serial dilutions were used to give measured data sets with a signal‐to‐noise ratio down to 10:1. The method's usefulness for routine operation was demonstrated by applying it to the above 72 data sets in an ‘automatic’ mode without any user interaction. In many cases these experiments showed improved precision and quantitative accuracy of peak areas after multivariate background correction. In most other cases the background correction method was neutral, i.e. it did not harm the peak area accuracy and precision where background offsets were absent or were too small to be reliably corrected. Compared with second‐order calibration methods such as the generalized rank annihilation method (GRAM) which can also correct for unknown background signals, the new method is insensitive to variation in peak shape and retention time from run to run, and prior knowledge in the form of standards is not needed. Copyright © 1999 John Wiley & Sons, Ltd.

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

“…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 …”

Speciation of adsorbates on surface of solids by infrared spectroscopy and chemometrics

“…In order to remove the baseline drift contribution, a multivariate baseline correction algorithm [28] adapted to spectroscopic adsorption data was implemented for its ability to correct for complex background contributions without affecting the underlying linear mixture model (See…”

Baseline correction using asymmetrically reweighted penalized least squares smoothing

“…Hence, background elimination or baseline correction for spectral data has been paid much attention and several methods have been proposed. [1][2][3][4] The diverse sources of background and additive noise make it hard to correct baseline for experimental spectral data. Furthermore as a baseline is usually varying from sample to sample, the situation is much worse.…”