Simple automatic strategy for background drift correction in chromatographic data analysis

Fu, Haiyan; Li, He-Dong; Ya, YU; Wang, Bing; Lu, Ping; Cui, Huapeng; Li, Pingping; She, Yuanbin

doi:10.1016/j.chroma.2016.04.054

Cited by 32 publications

(19 citation statements)

References 40 publications

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“…Another approach to baseline correction is by utilizing the concept of local minimum values (LMVs) [46]. The approach consists of three stages, namely: (i) initialization, (ii) iterative optimization, and (iii) an estimation of background drift.…”

Section: Local Minimum Value Approachmentioning

confidence: 99%

“…7a and 7b, (B) The resulting minimum vector, (C) removal of outliers by a moving-window strategy, with m the respective iteration, and (D) the original signal, the baseline, and the signal corrected for background. Reproduced with permission from [46] projection" (BD-OSP) method, which was utilized for the LC-QTOF-MS data [41]. In this case, the differences were only assessed qualitatively.…”

Section: Local Minimum Value Approachmentioning

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: Local Minimum Value Approachmentioning

confidence: 99%

Section: Local Minimum Value Approachmentioning

confidence: 99%

Section: Bayesian Statistics 2016 [26]mentioning

confidence: 99%

See 1 more Smart Citation

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

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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show abstract

“…In the present work, we introduced our recently developed method, namely, local minimum values-robust statistical analysis (LMV-RSA) 32 , to eliminate baseline drift prior to data analysis. First, LMVs in the chromatogram were extracted, and the corresponding positions were marked.…”

Section: Theorymentioning

confidence: 99%

Automatic time-shift alignment method for chromatographic data analysis

Zheng

et al. 2017

Sci Rep

Self Cite

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Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

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“…18 Algorithms that do not use a model of the baseline or signal shape and that have anti-noise capability are preferred. Many iterative algorithms [19][20][21] and Difference-of-Gaussian (DoG) functions can meet this requirement. Adaptive iteratively reweighted penalized least squares (airPLS) is a wellknown iterative algorithm, because it is flexible and valid; however, it needs further optimization.…”

Section: Introductionmentioning

confidence: 99%

Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

Jia

Sun

Yang

et al. 2018

Analyst

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An algorithm for signal extraction from a contaminated and distorted spectrum is proposed. First, this algorithm combines the salient space of the spectrum and the statistical characteristics of the noise to detect signal regions at different scales. Second, it extracts signals by subtracting the baseline from the spectrum in the signal regions. The baseline is fitted by segmented polynomial functions. This algorithm has been applied to simulated and experimental data, and the results show that this algorithm can accurately and automatically extract signals with varying widths from a contaminated spectrum. This method minimizes the influence of baseline distortion and exhibits good anti-noise capability and high real-time performance.

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Simple automatic strategy for background drift correction in chromatographic data analysis

Cited by 32 publications

References 40 publications

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

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

Automatic time-shift alignment method for chromatographic data analysis

Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

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