Self-Organizing Maps and Support Vector Regression as aids to coupled chromatography: illustrated by predicting spoilage in apples using volatile organic compounds |
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Authors: | Fong Sim S Sági-Kiss Virág Brereton Richard G |
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Institution: | a Centre for Chemometrics, School of Chemistry, University of Bristol, Cantocks Close, Bristol BS8 1TS, United Kingdom b Corvinus University of Budapest, Department of Applied Chemistry, H-1118 Budapest, Villányi út 29-33, Hungary |
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Abstract: | The paper describes the application of SOMs (Self-Organizing Maps) and SVR (Support Vector Regression) to pattern recognition in GC-MS (gas chromatography-mass spectrometry). The data are applied to two groups of apples, one which is a control and one which has been inoculated with Penicillium expansum and which becomes spoiled over the 10-day period of the experiment. GC-MS of SPME (solid phase microextraction) samples of volatiles from these apples were recorded, on replicate samples, over time, to give 58 samples used for pattern recognition and a peak table obtained. A new approach for finding the optimum SVR parameters called differential evolution is described. SOMs are presented in the form of two-dimensional maps. This paper shows the potential of using machine learning methods for pattern recognition in analytical chemistry, particularly as applied to food chemistry and biology where trends are likely to be non-linear. |
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Keywords: | Gas chromatography-mass spectrometry Food spoilage Volatile organic compounds Support Vector Regression Self-Organizing Maps Pattern recognition |
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