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A Wavelet, Fourier and PCA Data Analysis Pipeline:  Application to Distinguishing Mixtures of Liquids

Kokuer, Munevver, Murtagh, Fionn, McMillan, Norman D., Riedel, Sven, O'Rourke, Brian, Beverley, Katharine J., Augousti, Andy and Mason, Julian (2003) A Wavelet, Fourier and PCA Data Analysis Pipeline:  Application to Distinguishing Mixtures of Liquids. Journal of Chemical Information and Computer Science, 43 (2). pp. 587-594. ISSN 0095-2338

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Abstract

Using a new optical engineering technique for the “fingerprinting” of beverages and other liquids, we study and evaluate a range of features. The features are based on resolution scale, invariant frequency information, entropy, and energy. They allow mixtures of beverages to be very precisely placed in principal component plots used for the data analysis. To show this we make use of data sets resulting from optical/near-infrared and ultrasound sensors. Our liquid “fingerprinting” is a relatively open analysis framework in order to cater for different practical applications, in particular, on one hand, discrimination and best fit between fingerprints, and, on the other hand, more exploratory and open-ended data mining.

Item Type: Article
Subjects: Q Science > QD Chemistry
Schools: School of Art, Design and Architecture
Related URLs:
Depositing User: Katharine Beverley
Date Deposited: 13 Dec 2012 12:12
Last Modified: 16 Nov 2015 01:22
URI: http://eprints.hud.ac.uk/id/eprint/16327

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