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.