Baseline correction using adaptive iteratively reweighted penalized least squares

Zhang, Zhimin; Chen, Shan; Liang, Yi‐Zeng

doi:10.1039/b922045c

Cited by 821 publications

(502 citation statements)

References 29 publications

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“…For removing background coming from the measured material (fluorescence) or signal from the substrate different methods have been developed that are capable of handling irregularly shaped baselines [125][126][127][128]. Baseline correction of Raman spectra is especially important prior to multivariate methods and different solutions to improve baseline correction methods have been developed [125,129,130].…”

Section: Spectra Pre-processingmentioning

confidence: 99%

Raman Imaging of Lignocellulosic Feedstock

Gierlinger¹,

Keplinger²,

Schwanninger³

2013

Cellulose - Biomass Conversion

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Section: Spectra Pre-processingmentioning

confidence: 99%

Raman Imaging of Lignocellulosic Feedstock

Gierlinger¹,

Keplinger²,

Schwanninger³

2013

Cellulose - Biomass Conversion

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“…[29][30][31][32][33][34] A chemometric approach (of which there are many) was preferred because this could be more easily implemented on conventional Raman systems. [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] Morphological weighted penalized least squares (MPLS) 49 was used for baseline correction of Raman spectra because of its inherent simplicity, combined with its flexibility, suitability for automation, and effectiveness at mitigating baseline artefacts. MPLS required neither a priori knowledge nor subjective user intervention, and was reasonably efficient computationally (~5 minutes for 8410 spectra).…”

Section: Resultsmentioning

confidence: 99%

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

et al. 2015

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Publication InformationLi, B.; Calvet, A.; Casamayou-Boucau, Y.; Morris, C.; Ryder, A.G. (2015) 'Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection'. Analytical Chemistry, 87 (6):3419-3428. PublisherAmerican Chemical Society Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection.Boyan Li, Amandine Calvet, Yannick Casamayou-Boucau, Cheryl Morris, and Alan G. Ryder* Nanoscale Biophotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.* To whom correspondence should be addressed.Tel: +3539149 2943 Email: alan.ryder@nuigalway.ie ABSTRACT A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, sub-sampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artefacts, use of a 0-100% concentration range partial least squares (PLS) regression model to estimate concentration at each pixel, use of a more-accurate, reduced concentration range PLS model to accurately calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ~2.4% for a 0.05~1.0% concentration range and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems, which cannot be easily analyzed chromatographically such as hydrate, polymorph, or solvate contamination.

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“…由于实际测量信号的复杂性, 计算过程中将数据矩阵按保留时间顺序划分为 14 个保留时间区间依次进行了解析. 同时, 为了减小背景对计算的干扰, 本文采用了自适应迭代加权惩罚最小二乘(airPLS)法 [23] 对实验信号中的背景进行了扣除.…”

Section: 计算unclassified

Rapid Analysis of Pesticide Mixture by Gas Chromatography-Mass Spectrometry with a New Alternative Iterative Algorithm

Mei¹,

Cai²,

Shao³

2013

Acta Chim. Sinica

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Chemometric methods have been proved to be a powerful tool for resolution of overlapping signals. Immune algorithm (IA) is one of the chemometric approaches for analyzing multi-component GC-MS signals. The method extracts the information of the components by iteratively eliminating standard information (chromatogram or mass spectrum) from overlapping GC-MS signals. In the primary IA, however, the standard signal of each component possibly contained in the mixture must be provided by measuring the standard or by theoretical simulation. When there is difference between the measured signals of the standards and the mixture, distortion and negative values will appear in the resolved chromatograms. In order to conquer the problem, an algorithm based on an alternative iteration of least squares fitting and IA was proposed in this work. In the method, the measurement of GC-MS was achieved with a very fast temperature program to make the analytes to elute within a short retention time period, and then the chromatographic and mass spectral information of the components in the overlapping signal is calculated with the proposed algorithm. In the calculation, the algorithm takes random mass spectra of the components as the starting input, and then the information of each component is obtained with an alternating iterative process. In the iteration, the mass spectra are calculated by using the least squares fitting and the chromatographic profiles are resolved by IA. Furthermore, the non-negative and unimodality constraints are adopted in the calculation for improving the resolved results. The iteration stops when the remaining signal does not change. The feasibility of the method was validated by using a simulated GC-MS data matrix of a three-component mixture, and the practicability of the method was proved by resolving the GC-MS data of the 40-pesticide mixture. The results show that both the mass spectra and the chromatographic information of the components were extracted from the overlapping signals, and the pesticide mixture was analyzed within 10 min elution with the help of the proposed method.

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Baseline correction using adaptive iteratively reweighted penalized least squares

Cited by 821 publications

References 29 publications

Raman Imaging of Lignocellulosic Feedstock

Raman Imaging of Lignocellulosic Feedstock

Low-Content Quantification in Powders Using Raman Spectroscopy: A Facile Chemometric Approach to Sub 0.1% Limits of Detection

Rapid Analysis of Pesticide Mixture by Gas Chromatography-Mass Spectrometry with a New Alternative Iterative Algorithm

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