Chemometrics: Theory and Application

Title Page......Page 1
Copyright......Page 2
ACS Symposium Series......Page 3
FOREWORD......Page 4
PdftkEmptyString......Page 0
PREFACE......Page 5
1 Advances in the Application of Optimization Methodology in Chemistry......Page 7
Automated Continuous Flow System......Page 9
Chemical System......Page 10
Objective Function......Page 12
Mapping......Page 14
Discussion......Page 15
Literature Cited......Page 18
2 ARTHUR and Experimental Data Analysis: The Heuristic Use of a Polyalgorithm......Page 20
Definitions......Page 21
Pattern Recognition: New Techniques that Utilize Analytical Error......Page 22
Introduction to Data Analysis Using ARTHUR......Page 25
Application of Display Techniques to Obsidian Data.......Page 30
Conclusion......Page 36
Bayesian Classification......Page 38
Intermeasurement Correlations......Page 39
Interpattern Distance Matrix Calculations......Page 40
Principal Component Factor Analysis......Page 42
Statistical Isolinear Multicategory Analysis (SIMCA)......Page 45
Unsupervised Clustering......Page 47
Classification Based on Interpattern Distance......Page 48
Feature Selection by Weighting......Page 49
Linear Discriminant Analysis......Page 52
Utilities......Page 55
Literature Cited......Page 57
3 Abstract Factor Analysis—A Theory of Error and Its Application to Analytical Chemistry......Page 59
Theory of Error......Page 60
Residual Standard Deviation - The Real Error......Page 63
Pythagorean Relationship......Page 64
Testing the Theory with Mathematical Models......Page 65
Imbedded Error Function......Page 67
Testing IE and IND Function Using Model Data......Page 70
Applications......Page 71
Literature Cited......Page 78
4 The Unique Role of Target-Transformation Factor Analysis in the Chemometric Revolution......Page 79
Target Transformations......Page 81
Ramifications of TTFA......Page 84
Literature Cited......Page 85
Experimental......Page 86
Results and Discussion......Page 87
Literature Cited......Page 122
6 Analysis of the Electron Spin Resonance of Spin Labels Using Chemometric Methods......Page 123
Methodology......Page 124
Spin Labels in Inclusion Crystals......Page 126
Spin Labels in Model Membrane Systems......Page 129
Conclusions......Page 131
Literature Cited......Page 132
7 Automatic Elucidation of Reaction Mechanisms in Stirred-Pool Controlled-Potential Chronocoulometry......Page 133
The Chemical Problem......Page 139
The Starting Point......Page 141
The Body of the Program......Page 143
Results and Discussion......Page 155
Conclusion......Page 157
Literature Cited......Page 158
Introduction to Nonlinear Regression Analysis......Page 159
Example 1: pKa's of a Dibasic Substance from Absorbance Measurements......Page 161
Example 2: Antibacterial Potency of Nitrophenols......Page 165
Example 3: Antibacterial Potency of N-benzyl Erythromycins(15)......Page 167
Literature Cited......Page 170
9 A Computer System for Structure-Activity Studies Using Chemical Structure Information Handling and Pattern Recognition Techniques......Page 171
Introduction to Pattern Recognition......Page 172
Applications of Pattern Recognition to Structure-Activity Relations......Page 179
Structure-Activity Studies Using Pattern Recognition......Page 180
Barbiturate Study......Page 190
Literature Cited......Page 195
10 Enthalpy-Entropy Compensation: An Example of the Misuse of Least Squares and Correlation Analysis......Page 198
Chemical Theory......Page 199
Statistical Theory......Page 200
Application to Chemical Examples......Page 216
Literature Cited......Page 222
I. Introduction......Page 225
II. Methodology......Page 226
The Lilliefors Test of Normality.......Page 229
The Kolmogorov-Smirnov Test--Nonparametric Testing of Two Distributions for Differences or Similarity.......Page 235
III. Examples of How to Avoid Lying with Statistics......Page 238
IV. Conclusions......Page 243
Literature Cited......Page 247
1. Introduction......Page 249
3. The Idea of Classes; Experimental Design and Similarity Models......Page 253
4. Graphical Representation......Page 255
5.2 Relations Between Objects and Classes.......Page 257
5.3 Relation Between Classes.......Page 258
6.1 Describing a Class by Mathematical Model.......Page 259
6.2 Principal Components (PC) Models As Similarity Models.......Page 260
6.3 Parameter Estimation in the PC Model (1).......Page 262
6.4 The Number of Components (A) in PC Models.......Page 263
6.5 Information Contained in the Residuals.......Page 264
6.5.2 The Modelling Power of a Variable.......Page 266
6.5.4 Discriminatory Power of a Variable.......Page 267
6.5.7 Distance Between Two Classes.......Page 268
6.6 Classification of New Objects (Test Set).......Page 269
6.7 Missing Data.......Page 271
6.8 Graphical Methods.......Page 272
6.9.1 Classification of Exo and Endo 2-Substituted Norbornanes.......Page 273
7. Applications......Page 279
8. Future Developments......Page 281
9. Discussion......Page 284
Literature Cited......Page 286
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