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Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial

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Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial. / Bieroza, Magdalena; Baker, Andy; Bridgeman, John.
In: Education for Chemical Engineers, Vol. 7, No. 1, 01.2012, p. e22-e31.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Bieroza, M, Baker, A & Bridgeman, J 2012, 'Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial', Education for Chemical Engineers, vol. 7, no. 1, pp. e22-e31. https://doi.org/10.1016/j.ece.2011.10.002

APA

Bieroza, M., Baker, A., & Bridgeman, J. (2012). Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial. Education for Chemical Engineers, 7(1), e22-e31. https://doi.org/10.1016/j.ece.2011.10.002

Vancouver

Bieroza M, Baker A, Bridgeman J. Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial. Education for Chemical Engineers. 2012 Jan;7(1):e22-e31. doi: 10.1016/j.ece.2011.10.002

Author

Bieroza, Magdalena ; Baker, Andy ; Bridgeman, John. / Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial. In: Education for Chemical Engineers. 2012 ; Vol. 7, No. 1. pp. e22-e31.

Bibtex

@article{0b794d26217e4df8a15738abfc87bddf,
title = "Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial",
abstract = "Large datasets are common in chemical and environmental engineering applications and tools for their analysis are in great demand. Here, the outputs of a series of fluorescence spectroscopy analyses are utilised to demonstrate the application of the self-organising map (SOM) technique for data analysis. Fluorescence spectroscopy is a well-established technique of organic matter fingerprinting in water. The technique can provide detailed information on the physico-chemical properties of water. However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools.This paper presents a tutorial for training engineering postgraduate researchers in the use of SOM techniques using MATLAB{\textregistered}. Via a tutorial, the application of SOM to fluorescence spectra and, in particular, the characterisation of organic matter removal in water treatment, is presented. The tutorial presents a step-by-step example of the application of SOM to fluorescence data analysis and includes the source code for MATLAB{\textregistered}, together with presentation and discussion of the results. With this tutorial we hope to popularise this robust pattern recognition technique for fluorescence data analysis and large data sets in general, and also to provide educational practitioners with a novel tool with which to train engineering students in SOM.",
keywords = "Tutorial, Self-organising maps, Pattern recognition, Fluorescence, Organic matter",
author = "Magdalena Bieroza and Andy Baker and John Bridgeman",
year = "2012",
month = jan,
doi = "10.1016/j.ece.2011.10.002",
language = "English",
volume = "7",
pages = "e22--e31",
journal = "Education for Chemical Engineers",
issn = "1749-7728",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Exploratory analysis of excitation–emission matrix fluorescence spectra with self-organizing maps—A tutorial

AU - Bieroza, Magdalena

AU - Baker, Andy

AU - Bridgeman, John

PY - 2012/1

Y1 - 2012/1

N2 - Large datasets are common in chemical and environmental engineering applications and tools for their analysis are in great demand. Here, the outputs of a series of fluorescence spectroscopy analyses are utilised to demonstrate the application of the self-organising map (SOM) technique for data analysis. Fluorescence spectroscopy is a well-established technique of organic matter fingerprinting in water. The technique can provide detailed information on the physico-chemical properties of water. However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools.This paper presents a tutorial for training engineering postgraduate researchers in the use of SOM techniques using MATLAB®. Via a tutorial, the application of SOM to fluorescence spectra and, in particular, the characterisation of organic matter removal in water treatment, is presented. The tutorial presents a step-by-step example of the application of SOM to fluorescence data analysis and includes the source code for MATLAB®, together with presentation and discussion of the results. With this tutorial we hope to popularise this robust pattern recognition technique for fluorescence data analysis and large data sets in general, and also to provide educational practitioners with a novel tool with which to train engineering students in SOM.

AB - Large datasets are common in chemical and environmental engineering applications and tools for their analysis are in great demand. Here, the outputs of a series of fluorescence spectroscopy analyses are utilised to demonstrate the application of the self-organising map (SOM) technique for data analysis. Fluorescence spectroscopy is a well-established technique of organic matter fingerprinting in water. The technique can provide detailed information on the physico-chemical properties of water. However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools.This paper presents a tutorial for training engineering postgraduate researchers in the use of SOM techniques using MATLAB®. Via a tutorial, the application of SOM to fluorescence spectra and, in particular, the characterisation of organic matter removal in water treatment, is presented. The tutorial presents a step-by-step example of the application of SOM to fluorescence data analysis and includes the source code for MATLAB®, together with presentation and discussion of the results. With this tutorial we hope to popularise this robust pattern recognition technique for fluorescence data analysis and large data sets in general, and also to provide educational practitioners with a novel tool with which to train engineering students in SOM.

KW - Tutorial

KW - Self-organising maps

KW - Pattern recognition

KW - Fluorescence

KW - Organic matter

U2 - 10.1016/j.ece.2011.10.002

DO - 10.1016/j.ece.2011.10.002

M3 - Journal article

VL - 7

SP - e22-e31

JO - Education for Chemical Engineers

JF - Education for Chemical Engineers

SN - 1749-7728

IS - 1

ER -