Self-Organizing Maps and Support Vector Regression as aids to coupled chromatography: illustrated by predicting spoilage in apples using volatile organic compounds.

[1]  Dong-Sheng Cao,et al.  Model population analysis for variable selection , 2010 .

[2]  R. Brereton,et al.  One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process , 2010 .

[3]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[4]  R. Brereton,et al.  Supervised self organizing maps for classification and determination of potentially discriminatory variables: illustrated by application to nuclear magnetic resonance metabolomic profiling. , 2010, Analytical chemistry.

[5]  Martin Grootveld,et al.  Self Organising Maps for variable selection: Application to human saliva analysed by nuclear magnetic resonance spectroscopy to investigate the effect of an oral healthcare product , 2009 .

[6]  Richard G. Brereton,et al.  Chemometrics for Pattern Recognition , 2009 .

[7]  Nandini Das,et al.  A comparison study of three non-parametric control charts to detect shift in location parameters , 2009 .

[8]  Richard G Brereton,et al.  Self Organising Maps for distinguishing polymer groups using thermal response curves obtained by dynamic mechanical analysis. , 2008, The Analyst.

[9]  Sandip Kumar Lahiri,et al.  THE SUPPORT VECTOR REGRESSION WITH THE PARAMETER TUNING ASSISTED BY A DIFFERENTIAL EVOLUTION TECHNIQUE: STUDY OF THE CRITICAL VELOCITY OF A SLURRY FLOW IN A PIPELINE , 2008 .

[10]  A. Osman,et al.  Volatile flavour compounds and sensory properties of minimally processed durian (Durio zibethinus cv. D24) fruit during storage at 4°C , 2007 .

[11]  A. Goldstein,et al.  Quantifying sesquiterpene and oxygenated terpene emissions from live vegetation using solid-phase microextraction fibers. , 2007, Journal of chromatography. A.

[12]  A. Kushalappa,et al.  Detection and discrimination of two fungal diseases of mango (cv. Keitt) fruits based on volatile metabolite profiles using GC/MS , 2007 .

[13]  Richard G. Brereton,et al.  Pattern Recognition of Gas Chromatography Mass Spectrometry of Human Volatiles in Sweat to distinguish the sex of subjects and determine potential Discriminatory Marker Peaks , 2007 .

[14]  Kristian Karlshøj,et al.  Prediction of Penicillium expansum spoilage and patulin concentration in apples used for apple juice production by electronic nose analysis. , 2007, Journal of agricultural and food chemistry.

[15]  Sanyou Zeng,et al.  Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings , 2007, ISICA.

[16]  Yun Xu,et al.  Support Vector Machines: A Recent Method for Classification in Chemometrics , 2006 .

[17]  Lina Xu,et al.  Analysis of volatile compounds as spoilage indicators in fresh king salmon (Oncorhynchus tshawytscha) during storage using SPME-GC-MS. , 2006, Journal of agricultural and food chemistry.

[18]  J. Dewulf,et al.  Volatile metabolite production of spoilage micro-organisms on a mixed-lettuce agar during storage at 7 degrees C in air and low oxygen atmosphere. , 2006, International journal of food microbiology.

[19]  D. Penn,et al.  An automated method for peak detection and matching in large gas chromatography‐mass spectrometry data sets , 2006 .

[20]  G. Echeverría,et al.  Multivariate analysis of modifications in biosynthesis of volatile compounds after CA storage of 'Fuji' apples , 2006 .

[21]  H. Cartwright,et al.  Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets. , 2005, Analytical chemistry.

[22]  P. Eilers,et al.  New background correction method for liquid chromatography with diode array detection, infrared spectroscopic detection and Raman spectroscopic detection. , 2004, Journal of chromatography. A.

[23]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[24]  T. Simpson,et al.  Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

[25]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

[26]  Zora Singh,et al.  Aroma volatiles production during fruit ripening of ‘Kensington Pride’ mango , 2003 .

[27]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[28]  P. Dalgaard,et al.  Significance of volatile compounds produced by spoilage bacteria in vacuum-packed cold-smoked salmon (Salmo salar) analyzed by GC-MS and multivariate regression. , 2001, Journal of agricultural and food chemistry.

[29]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[30]  M. Timón,et al.  Microbial populations and volatile compounds in the‘bone taint’ spoilage of dry cured ham , 2000, Letters in applied microbiology.

[31]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[32]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .

[33]  F. Biasioli,et al.  PTR-MS real time monitoring of the emission of volatile organic compounds during postharvest aging of berryfruit , 1999 .

[34]  W. Dott,et al.  Species-specific production of microbial volatile organic compounds (MVOC) by airborne fungi from a compost facility. , 1999, Chemosphere.

[35]  Giuseppe Musumarra,et al.  Chemometrics and cultural heritage , 1998 .

[36]  Rasmus Bro,et al.  Chemometrics in food science—a demonstration of the feasibility of a highly exploratory, inductive evaluation strategy of fundamental scientific significance , 1998 .

[37]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[38]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[39]  J. Frisvad,et al.  Characterization of volatile metabolites from 47 Penicillium taxa , 1995 .

[40]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[41]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[42]  George H. Dunteman,et al.  Principal Components Analysis , 1990 .

[43]  D. Coomans,et al.  Potential pattern recognition in chemical and medical decision making , 1986 .

[44]  I. Jolliffe.  Principal Components in Regression Analysis , 1986 .

[45]  Paul Geladi,et al.  An example of 2-block predictive partial least-squares regression with simulated data , 1986 .

[46]  Kurt Varmuza,et al.  Pattern recognition in chemistry , 1980 .

[47]  Svante Wold,et al.  Pattern recognition by means of disjoint principal components models , 1976, Pattern Recognit..