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Standardising chromatographic denoising procedure

Abstract

The denoising of the chromatograms is an important step before subjecting it to any data analysis workflow. The present work carries a comparison between the wavelet analysis, Savitzky-Golay (SG) algorithm and chemometric approaches towards finding a simple, fast and user-friendly denoising approach. It is found that wavelet analysis and SG algorithm requires optimisation of the several parameters making entire denoising procedure user involved, laborious and time consuming. Moreover, denoising of the two chromatographic data sets achieved with wavelet and SG algorithm are of not desired level. Compared to wavelet and SG algorithm, chemometric techniques are found to provide a better denoising of the chromatograms. In the present work, two chemometric techniques principal component analysis (PCA) and multivariate curve resolution alternating least square (MCR-ALS) analysis are tested. Between these two chemometric techniques, PCA is found to provide a simple and better denoising approach. Application of PCA does not require optimisation of any user specified parameters. PCA also provides better denoising and it is mainly because it involves projection of the data sets in the space spanned by principal components that are essentially orthogonal to each other and most of the important information is mainly captured by few initial principal components. The noise contents of the data sets are usually explained by higher principal components. Thus, application of PCA with a suitable number of components denoises the chromatograms while preserving all the essential information. The present work, clearly suggests that PCA can provide an easy, fast and simple denoising procedure and hence must be integrated in the data analysis workflow.

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Publication details

The article was accepted on 31 Jul 2018 and first published on 01 Aug 2018


Article type: Paper
DOI: 10.1039/C8AY01606B
Citation: Anal. Methods, 2018, Accepted Manuscript
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    Standardising chromatographic denoising procedure

    K. Kumar, Anal. Methods, 2018, Accepted Manuscript , DOI: 10.1039/C8AY01606B

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