LEADER 05331nam 2200673Ia 450 001 9910139922803321 005 20200520144314.0 010 $a1-282-18631-0 010 $a9786612186318 010 $a0-470-74646-7 010 $a0-470-74647-5 035 $a(CKB)1000000000789631 035 $a(EBL)454385 035 $a(OCoLC)609843892 035 $a(SSID)ssj0000353993 035 $a(PQKBManifestationID)11264848 035 $a(PQKBTitleCode)TC0000353993 035 $a(PQKBWorkID)10302177 035 $a(PQKB)10416568 035 $a(MiAaPQ)EBC454385 035 $a(Au-PeEL)EBL454385 035 $a(CaPaEBR)ebr10315615 035 $a(CaONFJC)MIL218631 035 $a(OCoLC)441886989 035 $a(PPN)137140398 035 $a(EXLCZ)991000000000789631 100 $a20090310d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aChemometrics for pattern recognition /$fRichard Brereton 205 $a1st ed. 210 $aChichester, West Sussex, U.K. ;$aHoboken, NJ $cWiley$d2009 215 $a1 online resource (524 p.) 300 $aDescription based upon print version of record. 311 $a0-470-98725-1 320 $aIncludes bibliographical references and index. 327 $aChemometrics for Pattern Recognition; Contents; Acknowledgements; Preface; 1 Introduction; 1.1 Past, Present and Future; 1.2 About this Book; Bibliography; 2 Case Studies; 2.1 Introduction; 2.2 Datasets, Matrices and Vectors; 2.3 Case Study 1: Forensic Analysis of Banknotes; 2.4 Case Study 2: Near Infrared Spectroscopic Analysis of Food; 2.5 Case Study 3: Thermal Analysis of Polymers; 2.6 Case Study 4: Environmental Pollution using Headspace Mass Spectrometry; 2.7 Case Study 5: Human Sweat Analysed by Gas Chromatography Mass Spectrometry 327 $a2.8 Case Study 6: Liquid Chromatography Mass Spectrometry of Pharmaceutical Tablets2.9 Case Study 7: Atomic Spectroscopy for the Study of Hypertension; 2.10 Case Study 8: Metabolic Profiling of Mouse Urine by Gas Chromatography of Urine Extracts; 2.11 Case Study 9: Nuclear Magnetic Resonance Spectroscopy for Salival Analysis of the Effect of Mouthwash; 2.12 Case Study 10: Simulations; 2.13 Case Study 11: Null Dataset; 2.14 Case Study 12: GCMS and Microbiology of Mouse Scent Marks; Bibliography; 3 Exploratory Data Analysis; 3.1 Introduction; 3.2 Principal Components Analysis; 3.2.1 Background 327 $a3.2.2 Scores and Loadings3.2.3 Eigenvalues; 3.2.4 PCA Algorithm; 3.2.5 Graphical Representation; 3.3 Dissimilarity Indices, Principal Co-ordinates Analysis and Ranking; 3.3.1 Dissimilarity; 3.3.2 Principal Co-ordinates Analysis; 3.3.3 Ranking; 3.4 Self Organizing Maps; 3.4.1 Background; 3.4.2 SOM Algorithm; 3.4.3 Initialization; 3.4.4 Training; 3.4.5 Map Quality; 3.4.6 Visualization; Bibliography; 4 Preprocessing; 4.1 Introduction; 4.2 Data Scaling; 4.2.1 Transforming Individual Elements; 4.2.2 Row Scaling; 4.2.3 Column Scaling; 4.3 Multivariate Methods of Data Reduction 327 $a4.3.1 Largest Principal Components4.3.2 Discriminatory Principal Components; 4.3.3 Partial Least Squares Discriminatory Analysis Scores; 4.4 Strategies for Data Preprocessing; 4.4.1 Flow Charts; 4.4.2 Level 1; 4.4.3 Level 2; 4.4.4 Level 3; 4.4.5 Level 4; Bibliography; 5 Two Class Classifiers; 5.1 Introduction; 5.1.1 Two Class Classifiers; 5.1.2 Preprocessing; 5.1.3 Notation; 5.1.4 Autoprediction and Class Boundaries; 5.2 Euclidean Distance to Centroids; 5.3 Linear Discriminant Analysis; 5.4 Quadratic Discriminant Analysis; 5.5 Partial Least Squares Discriminant Analysis; 5.5.1 PLS Method 327 $a5.5.2 PLS Algorithm5.5.3 PLS-DA; 5.6 Learning Vector Quantization; 5.6.1 Voronoi Tesselation and Codebooks; 5.6.2 LVQ1; 5.6.3 LVQ3; 5.6.4 LVQ Illustration and Summary of Parameters; 5.7 Support Vector Machines; 5.7.1 Linear Learning Machines; 5.7.2 Kernels; 5.7.3 Controlling Complexity and Soft Margin SVMs; 5.7.4 SVM Parameters; Bibliography; 6 One Class Classifiers; 6.1 Introduction; 6.2 Distance Based Classifiers; 6.3 PC Based Models and SIMCA; 6.4 Indicators of Significance; 6.4.1 Gaussian Density Estimators and Chi-Squared; 6.4.2 Hotelling's T2; 6.4.3 D-Statistic 327 $a6.4.4 Q-Statistic or Squared Prediction Error 330 $aOver the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques 606 $aChemometrics 606 $aPattern perception 615 0$aChemometrics. 615 0$aPattern perception. 676 $a543.01/5195 700 $aBrereton$b Richard G$0283014 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139922803321 996 $aChemometrics for pattern recognition$91972140 997 $aUNINA