Assisted baseline subtraction in complex chromatograms using the BEADS algorithm

Navarro-Huerta, J.A.; Torres-Lapası́o, J.R.; López-Ureña, Sergio; Garcı́a-Alvarez-Coque, M.C.

doi:10.1016/j.chroma.2017.05.057

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…One algorithm that utilizes this concept of sparsity, and has been developed recently, is called baseline estimation and denoising using sparsity (BEADS) [51]. It was later further improved to create the "assisted BEADS" algorithm [52]. BEADS specifically aims to model the signal, background, and noise, without employing the use of overly restrictive parametric models.…”

Section: Baseline Estimation and Denoising Using Sparsitymentioning

confidence: 99%

“…To summarize, the following difficulties arise when using BEADS for baseline correction: (i) parameter adjustment and selection (ii) the signal intensity for the first and last points in the chromatogram should be equal, and (iii) difficulties with assessing data that may contain negative peaks. Most of these limitations have been addressed by Navarro-Huerta et al [52] who have developed the assisted-BEADS algorithm, and by Selesnick, who has proposed a solution for the endpoint artifacts [54]. Parameter selection may be facilitated by auxiliary autocorrelation plots.…”

Section: = L + Ĥ(13)mentioning

confidence: 99%

“…By determining the autocorrelation of the noise after background correction and by plotting this as a function of one (or, ideally, all) of the adjustable parameters, the optimal value of the parameter(s) can be established from the location in the plot where autocorrelation is minimized. To address the sensitivity of BEADS to negative peaks, an additional algorithm has been applied, which discards sporadic negative signals [52].…”

Section: = L + Ĥ(13)mentioning

confidence: 99%

“…A concise iterative method using the Bezier technique for baseline construction Corner cutting 2015 [25] Assisted baseline subtraction in complex chromatograms using the BEADS algorithm Smoothing/Filtering 2017 [52] Background correction in separation techniques hyphenated to high-resolution mass spectrometry -Thorough correction with mass spectrometry scans recorded as profile spectra MS Profile spectra-based correction 2017 [55] Simple automatic strategy for background drift correction in chromatographic data analysis Local minimum values 2016 [46] Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data…”

Section: Bayesian Statistics 2016 [26]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: Baseline Estimation and Denoising Using Sparsitymentioning

confidence: 99%

Section: = L + Ĥ(13)mentioning

confidence: 99%

Section: = L + Ĥ(13)mentioning

confidence: 99%

Section: Bayesian Statistics 2016 [26]mentioning

confidence: 99%

See 2 more Smart Citations

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

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

View full text Add to dashboard Cite

show abstract

“…The BEADS algorithm is generally used to process other signals with baseline interference. Its main functions are baseline correction, noise reduction and instrument zero drift correction [32]. As the BEADS algorithm has the characteristics of self-adapting, and it can self-adaptively deduct the continuous background and noise, avoid the influence of parameter selection on the results.…”

Section: 31mentioning

confidence: 99%

Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM

Peng

Chen

et al. 2018

Plasma Sci. Technol.

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Laser-induced breakdown spectroscopy (LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine (SVM) algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.

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