Descripciones mejoradas de Syndetics:

This book is aimed at the large number of people who need to use chemometrics but do not wish to understand complex mathematics, therefore it offers a comprehensive examination of the field of chemometrics without overwhelming the reader with complex mathematics.

* Includes five chapters that cover the basic principles of chemometrics analysis.
* Provides two chapters on the use of Excel and MATLAB for chemometrics analysis.
* Contains 70 worked problems so that readers can gain a practical understanding of the use of chemometrics.

Tabla de contenidos provista por Syndetics

• Preface(p. ix)
• Supplementary Information(p. xii)
• Acknowledgments(p. xiii)
• 1 Introduction(p. 1)
• 1.1 Points of View(p. 1)
• 1.2 Software and Calculations(p. 6)
• 1.3 Further Reading(p. 8)
• 1.3.1 General(p. 8)
• 1.3.2 Specific Areas(p. 10)
• 1.3.3 Internet Resources(p. 11)
• 1.4 References(p. 12)
• 2 Experimental Design(p. 15)
• 2.1 Introduction(p. 15)
• 2.2 Basic Principles(p. 19)
• 2.2.1 Degrees of Freedom(p. 19)
• 2.2.2 Analysis of Variance and Comparison of Errors(p. 23)
• 2.2.3 Design Matrices and Modelling(p. 30)
• 2.2.4 Assessment of Significance(p. 36)
• 2.2.5 Leverage and Confidence in Models(p. 47)
• 2.3 Factorial Designs(p. 53)
• 2.3.1 Full Factorial Designs(p. 54)
• 2.3.2 Fractional Factorial Designs(p. 60)
• 2.3.3 Plackett-Burman and Taguchi Designs(p. 66)
• 2.3.4 Partial Factorials at Several Levels: Calibration Designs(p. 69)
• 2.4 Central Composite or Response Surface Designs(p. 76)
• 2.4.1 Setting Up the Design(p. 76)
• 2.4.2 Degrees of Freedom(p. 79)
• 2.4.3 Axial Points(p. 80)
• 2.4.4 Modelling(p. 83)
• 2.4.5 Statistical Factors(p. 84)
• 2.5 Mixture Designs(p. 84)
• 2.5.1 Mixture Space(p. 85)
• 2.5.2 Simplex Centroid(p. 85)
• 2.5.3 Simplex Lattice(p. 88)
• 2.5.4 Constraints(p. 90)
• 2.5.5 Process Variables(p. 96)
• 2.6 Simplex Optimisation(p. 97)
• 2.6.1 Fixed Sized Simplex(p. 97)
• 2.6.2 Elaborations(p. 99)
• 2.6.3 Modified Simplex(p. 100)
• 2.6.4 Limitations(p. 101)
• Problems(p. 102)
• 3 Signal Processing(p. 119)
• 3.1 Sequential Signals in Chemistry(p. 119)
• 3.1.1 Environmental and Geological Processes(p. 119)
• 3.1.2 Industrial Process Control(p. 120)
• 3.1.3 Chromatograms and Spectra(p. 120)
• 3.1.4 Fourier Transforms(p. 120)
• 3.1.5 Advanced Methods(p. 121)
• 3.2 Basics(p. 122)
• 3.2.1 Peakshapes(p. 122)
• 3.2.2 Digitisation(p. 125)
• 3.2.3 Noise(p. 128)
• 3.2.4 Sequential Processes(p. 131)
• 3.3 Linear Filters(p. 131)
• 3.3.1 Smoothing Functions(p. 131)
• 3.3.2 Derivatives(p. 138)
• 3.3.3 Convolution(p. 138)
• 3.4 Correlograms and Time Series Analysis(p. 142)
• 3.4.1 Auto-correlograms(p. 142)
• 3.4.2 Cross-correlograms(p. 145)
• 3.4.3 Multivariate Correlograms(p. 146)
• 3.5 Fourier Transform Techniques(p. 147)
• 3.5.1 Fourier Transforms(p. 147)
• 3.5.2 Fourier Filters(p. 156)
• 3.5.3 Convolution Theorem(p. 161)
• 3.6 Topical Methods(p. 163)
• 3.6.1 Kalman Filters(p. 163)
• 3.6.2 Wavelet Transforms(p. 167)
• 3.6.3 Maximum Entropy (Maxent) and Bayesian Methods(p. 168)
• Problems(p. 173)
• 4 Pattern Recognition(p. 183)
• 4.1 Introduction(p. 183)
• 4.1.1 Exploratory Data Analysis(p. 183)
• 4.1.2 Unsupervised Pattern Recognition(p. 183)
• 4.1.3 Supervised Pattern Recognition(p. 184)
• 4.2 The Concept and Need for Principal Components Analysis(p. 184)
• 4.2.1 History(p. 185)
• 4.2.2 Case Studies(p. 186)
• 4.2.3 Multivariate Data Matrices(p. 188)
• 4.2.4 Aims of PCA(p. 190)
• 4.3 Principal Components Analysis: the Method(p. 191)
• 4.3.1 Chemical Factors(p. 191)
• 4.3.2 Scores and Loadings(p. 192)
• 4.3.3 Rank and Eigenvalues(p. 195)
• 4.3.4 Factor Analysis(p. 204)
• 4.3.5 Graphical Representation of Scores and Loadings(p. 205)
• 4.3.6 Preprocessing(p. 210)
• 4.3.7 Comparing Multivariate Patterns(p. 219)
• 4.4 Unsupervised Pattern Recognition: Cluster Analysis(p. 224)
• 4.4.1 Similarity(p. 224)
• 4.4.2 Linkage(p. 227)
• 4.4.3 Next Steps(p. 229)
• 4.4.4 Dendrograms(p. 229)
• 4.5 Supervised Pattern Recognition(p. 230)
• 4.5.1 General Principles(p. 231)
• 4.5.2 Discriminant Analysis(p. 233)
• 4.5.3 SIMCA(p. 243)
• 4.5.4 Discriminant PLS(p. 248)
• 4.5.5 K Nearest Neighbours(p. 249)
• 4.6 Multiway Pattern Recognition(p. 251)
• 4.6.1 Tucker3 Models(p. 252)
• 4.6.2 PARAFAC(p. 253)
• 4.6.3 Unfolding(p. 254)
• Problems(p. 255)
• 5 Calibration(p. 271)
• 5.1 Introduction(p. 271)
• 5.1.1 History and Usage(p. 271)
• 5.1.2 Case Study(p. 273)
• 5.1.3 Terminology(p. 273)
• 5.2 Univariate Calibration(p. 276)
• 5.2.1 Classical Calibration(p. 276)
• 5.2.2 Inverse Calibration(p. 279)
• 5.2.3 Intercept and Centring(p. 280)
• 5.3 Multiple Linear Regression(p. 284)
• 5.3.1 Multidetector Advantage(p. 284)
• 5.3.2 Multiwavelength Equations(p. 284)
• 5.3.3 Multivariate Approaches(p. 288)
• 5.4 Principal Components Regression(p. 292)
• 5.4.1 Regression(p. 292)
• 5.4.2 Quality of Prediction(p. 295)
• 5.5 Partial Least Squares(p. 297)
• 5.5.1 PLS1(p. 298)
• 5.5.2 PLS2(p. 303)
• 5.5.3 Multiway PLS(p. 307)
• 5.6 Model Validation(p. 313)
• 5.6.1 Autoprediction(p. 313)
• 5.6.2 Cross-validation(p. 315)
• 5.6.3 Independent Test Sets(p. 317)
• Problems(p. 323)
• 6 Evolutionary Signals(p. 339)
• 6.1 Introduction(p. 339)
• 6.2 Exploratory Data Analysis and Preprocessing(p. 341)
• 6.2.1 Baseline Correction(p. 341)
• 6.2.2 Principal Component Based Plots(p. 342)
• 6.2.3 Scaling the Data(p. 350)
• 6.2.4 Variable Selection(p. 360)
• 6.3 Determining Composition(p. 365)
• 6.3.1 Composition(p. 365)
• 6.3.2 Univariate Methods(p. 367)
• 6.3.3 Correlation and Similarity Based Methods(p. 372)
• 6.3.4 Eigenvalue Based Methods(p. 376)
• 6.3.5 Derivatives(p. 380)
• 6.4 Resolution(p. 386)
• 6.4.1 Selectivity for All Components(p. 387)
• 6.4.2 Partial Selectivity(p. 392)
• 6.4.3 Incorporating Constraints(p. 396)
• Problems(p. 398)
• Appendices(p. 409)
• A.1 Vectors and Matrices(p. 409)
• A.2 Algorithms(p. 412)
• A.3 Basic Statistical Concepts(p. 417)
• A.4 Excel for Chemometrics(p. 425)
• A.5 Matlab for Chemometrics(p. 456)
• Index(p. 479)