Critical comparison of background correction algorithms used in chromatography

The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool i...

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Veröffentlicht in:	Analytica chimica acta Jg. 1201; S. 339605
Hauptverfasser:	Niezen, Leon E., Schoenmakers, Peter J., Pirok, Bob W.J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 08.04.2022
Schlagworte:	Algorithms automation Background correction Chemometrics Chromatography data collection Data processing hybrids least squares Least-Squares Analysis Noise filtering Pre-processing Smoothing ANN 1D-LC Pre-processing SASS arPLS MPLS airPLS 2D RPLC asLS PLS MairPLS EMG MM SSE Background correction DAD Data processing Noise filtering RMSE RID FIR BEADS 2D-LC 1D LMV Smoothing LC Chemometrics
ISSN:	0003-2670, 1873-4324, 1873-4324
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Abstract	The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows. [Display omitted] •A software application was developed that allows for the comparison of smoothing and drift correction algorithms.•It can generate hybrid (part experimental, part simulated) data for other comparison studies and allows anyone to do so.•The need for a large and varied common data set against which all correction algorithms will be tested is highlighted.
AbstractList	The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows. The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows.The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows. The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows. [Display omitted] •A software application was developed that allows for the comparison of smoothing and drift correction algorithms.•It can generate hybrid (part experimental, part simulated) data for other comparison studies and allows anyone to do so.•The need for a large and varied common data set against which all correction algorithms will be tested is highlighted.
ArticleNumber	339605
Author	Niezen, Leon E. Schoenmakers, Peter J. Pirok, Bob W.J.
Author_xml	– sequence: 1 givenname: Leon E. orcidid: 0000-0002-3849-9228 surname: Niezen fullname: Niezen, Leon E. email: L.E.Niezen@uva.nl organization: Analytical Chemistry Group, van ‘t Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098, XH Amsterdam, the Netherlands – sequence: 2 givenname: Peter J. orcidid: 0000-0002-9167-7716 surname: Schoenmakers fullname: Schoenmakers, Peter J. organization: Analytical Chemistry Group, van ‘t Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098, XH Amsterdam, the Netherlands – sequence: 3 givenname: Bob W.J. orcidid: 0000-0002-4558-3778 surname: Pirok fullname: Pirok, Bob W.J. organization: Analytical Chemistry Group, van ‘t Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098, XH Amsterdam, the Netherlands
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Keywords	ANN 1D-LC Pre-processing SASS arPLS MPLS airPLS 2D RPLC asLS PLS MairPLS EMG MM SSE Background correction DAD Data processing Noise filtering RMSE RID FIR BEADS 2D-LC 1D LMV Smoothing LC Chemometrics
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Snippet	The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven...
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SubjectTerms	Algorithms automation Background correction Chemometrics Chromatography data collection Data processing hybrids least squares Least-Squares Analysis Noise filtering Pre-processing Smoothing
Title	Critical comparison of background correction algorithms used in chromatography
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