Applied Chemometrics for Scientists

Contents......Page 3
Preface......Page 10
s and the Borderlines between Other Disciplines......Page 13
Current Developments in Complex Problem Solving......Page 14
APPLICATION AREAS......Page 15
HOW TO USE THIS BOOK......Page 16
LITERATURE AND OTHER SOURCES OF INFORMATION......Page 17
REFERENCES......Page 19
WHY DESIGN EXPERIMENTS IN CHEMISTRY?......Page 21
DEGREES OF FREEDOM AND SOURCES OF ERROR......Page 24
ANALYSIS OF VARIANCE AND INTERPRETATION OF ERRORS......Page 28
MATRICES, VECTORS AND THE PSEUDOINVERSE......Page 32
DESIGN MATRICES......Page 34
FACTORIAL DESIGNS......Page 37
Extending the Number of Levels......Page 40
AN EXAMPLE OF A FACTORIAL DESIGN......Page 41
FRACTIONAL FACTORIAL DESIGNS......Page 44
PLACKETT–BURMAN AND TAGUCHI DESIGNS......Page 47
THE APPLICATION OF A PLACKETT–BURMAN DESIGN TO THE SCREENING OF FACTORS INFLUENCING A CHEMICAL REACTION......Page 49
CENTRAL COMPOSITE DESIGNS......Page 51
MIXTURE DESIGNS......Page 56
Simplex Centroid Designs......Page 57
Constrained Mixture Designs......Page 59
A FOUR COMPONENT MIXTURE DESIGN USED TO STUDY BLENDING OF OLIVE OILS......Page 61
SIMPLEX OPTIMIZATION......Page 63
LEVERAGE AND CONFIDENCE IN MODELS......Page 65
DESIGNS FOR MULTIVARIATE CALIBRATION......Page 70
REFERENCES......Page 74
STATISTICS FOR CHEMISTS......Page 75
ERRORS......Page 76
Sampling Errors......Page 77
Sample Preparation Errors......Page 78
DESCRIBING DATA......Page 79
Descriptive Statistics......Page 80
Graphical Presentation......Page 81
Covariance and Correlation Coefficient......Page 84
THE NORMAL DISTRIBUTION Error Distributions......Page 85
Normal Distribution Functions and Tables......Page 86
Applications......Page 87
Cumulative Frequency......Page 88
Kolmogorov–Smirnov Test......Page 90
Consequences......Page 91
HYPOTHESIS TESTS......Page 92
-TEST......Page 93
-TEST FOR COMPARISON OF VARIANCES......Page 97
CONFIDENCE IN LINEAR REGRESSION......Page 101
Example......Page 102
Confidence of Prediction of Parameters......Page 104
Confidence in the Mean......Page 105
Confidence in the Standard Deviation......Page 107
CONSEQUENCES OF OUTLIERS AND HOW TO DEAL WITH THEM......Page 108
Normal Distributions......Page 112
Linear Regression......Page 113
Multivariate Calibration......Page 115
SHEWHART CHARTS......Page 116
Cusum Chart......Page 118
Multivariate Statistical Process Control......Page 120
REFERENCES......Page 121
SEQUENTIAL DATA......Page 122
CORRELOGRAMS......Page 123
Auto-correlograms......Page 124
Multivariate Correlograms......Page 126
LINEAR SMOOTHING FUNCTIONS AND FILTERS......Page 127
FOURIER TRANSFORMS......Page 131
Bayes’ Theorem......Page 135
Maximum Entropy......Page 136
Maximum Entropy and Modelling......Page 137
FOURIER FILTERS......Page 139
PEAKSHAPES IN CHROMATOGRAPHY AND SPECTROSCOPY......Page 145
Principal Features......Page 146
Lorentzians......Page 147
Asymmetric Peak Shapes......Page 148
DERIVATIVES IN SPECTROSCOPY AND CHROMATOGRAPHY......Page 149
WAVELETS......Page 153
REFERENCES......Page 154
Exploratory Data Analysis......Page 156
Supervised Pattern Recognition......Page 157
PRINCIPAL COMPONENTS ANALYSIS Basic Ideas......Page 158
Method......Page 161
Case Study 2......Page 165
Scores Plots......Page 167
Loadings Plots......Page 168
COMPARING MULTIVARIATE PATTERNS......Page 170
PREPROCESSING......Page 171
UNSUPERVISED PATTERN RECOGNITION: CLUSTER ANALYSIS......Page 178
Modelling the Training Set......Page 182
Test Sets, Cross-validation and the Bootstrap......Page 183
STATISTICAL CLASSIFICATION TECHNIQUES......Page 185
Bivariate and Multivariate Discriminant Models......Page 186
SIMCA......Page 189
K NEAREST NEIGHBOUR METHOD......Page 193
HOW MANY COMPONENTS CHARACTERIZE A DATASET?......Page 196
MULTIWAY PATTERN RECOGNITION......Page 198
Tucker3 Models......Page 199
PARAFAC......Page 200
REFERENCES......Page 201
INTRODUCTION......Page 203
Classical Calibration......Page 205
Inverse Calibration......Page 206
Calibration Equations......Page 208
Graphs......Page 209
MULTIVARIATE CALIBRATION AND THE SPECTROSCOPY OF MIXTURES......Page 212
MULTIPLE LINEAR REGRESSION......Page 216
PRINCIPAL COMPONENTS REGRESSION......Page 218
PARTIAL LEAST SQUARES......Page 221
Autoprediction......Page 224
Test Sets......Page 225
Unfolding......Page 227
Trilinear PLS1......Page 228
N-PLSM......Page 229
REFERENCES......Page 230
INTRODUCTION......Page 231
PREPARING THE DATA Preprocessing......Page 232
Variable Selection......Page 234
CHEMICAL COMPOSITION OF SEQUENTIAL DATA......Page 238
UNIVARIATE PURITY CURVES......Page 240
Correlation Coefficients......Page 244
Distance Measures......Page 245
EVOLVING AND WINDOW FACTOR ANALYSIS......Page 246
Expanding Windows......Page 247
Fixed Sized Windows......Page 248
DERIVATIVE BASED METHODS......Page 249
DECONVOLUTION OF EVOLUTIONARY SIGNALS......Page 251
Selectivity: Finding Pure Variables......Page 252
Multiple Linear Regression......Page 253
Partial Selectivity......Page 254
ITERATIVE METHODS FOR RESOLUTION......Page 256
THE STUDY OF EQUILIBRIA USING SPECTROSCOPY......Page 259
SPECTROSCOPIC MONITORING OF REACTIONS......Page 262
Mid Infrared Spectroscopy......Page 263
Near Infrared Spectroscopy......Page 264
UV/Visible Spectroscopy......Page 265
Summary of Main Data Analysis Techniques......Page 266
KINETICS AND MULTIVARIATE MODELS FOR THE QUANTITATIVE STUDY OF REACTIONS......Page 267
Constraints and Combining Information......Page 271
Three-way Analysis......Page 272
Multivariate Tools for Design, Data Acquisition and Analysis......Page 273
Continuous Improvement and Knowledge Management Tools......Page 274
REFERENCES......Page 275
INTRODUCTION......Page 276
USE OF STATISTICAL DESIGNS FOR IMPROVING THE PERFORMANCE OF SYNTHETIC REACTIONS......Page 278
SCREENING FOR FACTORS THAT INFLUENCE THE PERFORMANCE OF A REACTION......Page 280
OPTIMIZING THE PROCESS VARIABLES......Page 284
Simplex Centroid and Lattice Designs......Page 287
Constraints......Page 289
Ratios......Page 292
Models......Page 294
Genomics, Proteomics and Metabolomics......Page 296
Chemical Taxonomy......Page 297
TAXONOMY......Page 298
Discriminant Function......Page 300
Several Classes......Page 302
Limitations......Page 305
MAHALANOBIS DISTANCE......Page 306
BAYESIAN METHODS AND CONTINGENCY TABLES......Page 309
SUPPORT VECTOR MACHINES......Page 312
DISCRIMINANT PARTIAL LEAST SQUARES......Page 315
MICRO-ORGANISMS......Page 317
Mid Infrared Spectroscopy......Page 318
Further Measurements......Page 320
Pyrolysis Mass Spectrometry......Page 321
MEDICAL DIAGNOSIS USING SPECTROSCOPY......Page 322
Coupled Chromatography......Page 323
REFERENCES......Page 326
INTRODUCTION......Page 328
SEQUENCE ALIGNMENT AND SCORING MATCHES......Page 329
SEQUENCE SIMILARITY......Page 331
Diagrammatic Representations......Page 333
Evolutionary Theory and Cladistics......Page 334
Phylograms......Page 335
PHYLOGENETIC TREES......Page 336
REFERENCES......Page 338
INTRODUCTION......Page 339
SCALING IMAGES......Page 341
Multiway Image Preprocessing......Page 342
FILTERING AND SMOOTHING THE IMAGE......Page 343
PRINCIPAL COMPONENTS FOR THE ENHANCEMENT OF IMAGES......Page 345
REGRESSION OF IMAGES......Page 348
ALTERNATING LEAST SQUARES AS EMPLOYED IN IMAGE ANALYSIS......Page 353
MULTIWAY METHODS IN IMAGE ANALYSIS......Page 355
REFERENCES......Page 357
Adulteration......Page 358
Image Analysis......Page 359
HOW TO DETERMINE THE ORIGIN OF A FOOD PRODUCT USING CHROMATOGRAPHY......Page 360
Calibration......Page 361
Exploratory Methods......Page 362
Chemical Composition......Page 363
Sensory Panels......Page 364
VARIMAX ROTATION......Page 366
CALIBRATING SENSORY DESCRIPTORS TO COMPOSITION......Page 372
REFERENCES......Page 375
Index......Page 376