The elimination of errors due to baseline drift in the measurement of peak areas in gas chromatography

Wilson, Jarod; McInnes, C.A.J.

doi:10.1016/s0021-9673(01)99489-0

Cited by 12 publications

(8 citation statements)

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“…Steps (i) and (ii) together are generally termed "background correction" and are required for the accurate identification and, especially, quantification of analytes. This has been a long-standing issue, with the first reports having been published in the 1960s [20,21]. During the denoising and smoothing procedures, low-amplitude signals are first removed, irrespective of their frequency spectrum, after which high-frequency signals are removed, irrespective of their amplitude.…”

Section: Aimmentioning

confidence: 99%

“…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.…”

Section: Curve Fitting 2007 [300]mentioning

confidence: 99%

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

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional 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 oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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Section: Aimmentioning

confidence: 99%

Section: Curve Fitting 2007 [300]mentioning

confidence: 99%

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

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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“…First, although seemingly simple, the problem of baseline subtraction remains a long-standing issue, which can be traced back to [58,38]. Recent overviews are presented in [42,20,27].…”

Section: Introductionmentioning

confidence: 99%

Chromatogram baseline estimation and denoising using sparsity (BEADS)

Ning

Selesnick

Duval

2014

Chemometrics and Intelligent Laboratory Systems

169

107

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This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of chromatogram peaks as sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is utilized. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data.

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“…Early reports of baseline correction that appear in the literature were predominantly based on hardware implementations or modifications. [2][3][4][5] In 1969, some of the first computer-based methods designed for the automated removal of baselines were published. 6,7 Activity in the development and use of BCMs accelerated through the 1970s and 1980s, partly facilitated by the increasing availability of distributed computing equipment.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

et al. 2005

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Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as 'baseline', 'background', and/or 'background noise'. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.

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The elimination of errors due to baseline drift in the measurement of peak areas in gas chromatography

Cited by 12 publications

References 1 publication

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

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

Chromatogram baseline estimation and denoising using sparsity (BEADS)

Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

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