title: Self-Organizing Maps and Support Vector Regression as aids to coupled chromatography: illustrated by predicting spoilage in apples using volatile organic compounds.
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.
authors.affiliation: Centre for Chemometrics, School of Chemistry, University of Bristol, Cantocks Close, Bristol BS8 1TS, United Kingdom.
authors.forename: Sim S
authors.initials: SS
authors.lastname: Fong
authors.forename: Virág
authors.initials: V
authors.lastname: Sági-Kiss
authors.forename: Richard G
authors.initials: RG
authors.lastname: Brereton
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005504:Food Analysis / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D027845:Malus / Q000737:chemistry*; D015999:Multivariate Analysis; D009930:Organic Chemicals / Q000032:analysis* / Q000737:chemistry*; D011786:Quality Control; D012044:Regression Analysis; D013997:Time Factors; D014835:Volatilization
chemical_list: D009930:Organic Chemicals