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Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry, Analytical Chemistry.
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry, Analytical Chemistry.
2006 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 84, no 1-2, 33-39 p.Article in journal (Refereed) Published
Abstract [en]

One- or two-dimensional data obtained with LC/GC/CE and single or multiple channel detection (MS, UV/VIS) are often used as 'fingerprints' in order to characterize complex samples. The relation between samples is then explored by multivariate data analysis (PCA, hierarchical clustering), but inevitable more or less random variation in separation conditions obstructs the analysis. Several methods for peak alignment have been developed, with more or less increase of time and efforts for computations. In this work another approach is presented, based on a correlation measure less sensitive for variations in retention/migration time. The merits of the method as a fast initial data exploration tool are demonstrated for a case study of urine profiling with CE/MS.

Place, publisher, year, edition, pages
2006. Vol. 84, no 1-2, 33-39 p.
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-96250DOI: 10.1016/j.chemolab.2006.04.009ISI: 000242768200006OAI: oai:DiVA.org:uu-96250DiVA: diva2:170759
Available from: 2007-09-21 Created: 2007-09-21 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Managing and Exploring Large Data Sets Generated by Liquid Separation - Mass Spectrometry
Open this publication in new window or tab >>Managing and Exploring Large Data Sets Generated by Liquid Separation - Mass Spectrometry
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices).

The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets.

The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 61 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 346
Keyword
Analytical chemistry, liquid chromatography, capillary electrophoresis, capillary electrochromatography, mass spectrometry, electro spray ionization, chemometrics, multivariate data analysis, peptide, biomarkers, fingerprints, PCA, alignment, data compression, ANOVA, target correlation, data management, Analytisk kemi
Identifiers
urn:nbn:se:uu:diva-8223 (URN)978-91-554-6976-4 (ISBN)
Public defence
2007-10-12, B21, BMC, Box 599, 751 24 Uppsala, 10:15
Opponent
Supervisors
Available from: 2007-09-21 Created: 2007-09-21 Last updated: 2011-05-10Bibliographically approved

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Danielsson, RolfBäckström, Daniel

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