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Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton
| Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina |
542.30151
543.015195 |
| Soggetto topico |
Chemometrics
Chemistry, Analytic |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-83864-7
9786610838646 0-470-05778-5 0-470-05777-7 |
| Classificazione | 35.05 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Chemometrics for Scientists; Contents; Preface; 1 Introduction; 1.1 Development of Chemometrics; 1.1.1 Early Developments; 1.1.2 1980s and the Borderlines between Other Disciplines; 1.1.3 1990s and Problems of Intermediate Complexity; 1.1.4 Current Developments in Complex Problem Solving; 1.2 Application Areas; 1.3 How to Use this Book; 1.4 Literature and Other Sources of Information; References; 2 Experimental Design; 2.1 Why Design Experiments in Chemistry?; 2.2 Degrees of Freedom and Sources of Error; 2.3 Analysis of Variance and Interpretation of Errors
2.4 Matrices, Vectors and the Pseudoinverse2.5 Design Matrices; 2.6 Factorial Designs; 2.6.1 Extending the Number of Factors; 2.6.2 Extending the Number of Levels; 2.7 An Example of a Factorial Design; 2.8 Fractional Factorial Designs; 2.9 Plackett-Burman and Taguchi Designs; 2.10 The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction; 2.11 Central Composite Designs; 2.12 Mixture Designs; 2.12.1 Simplex Centroid Designs; 2.12.2 Simplex Lattice Designs; 2.12.3 Constrained Mixture Designs 2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils2.14 Simplex Optimization; 2.15 Leverage and Confidence in Models; 2.16 Designs for Multivariate Calibration; References; 3 Statistical Concepts; 3.1 Statistics for Chemists; 3.2 Errors; 3.2.1 Sampling Errors; 3.2.2 Sample Preparation Errors; 3.2.3 Instrumental Noise; 3.2.4 Sources of Error; 3.3 Describing Data; 3.3.1 Descriptive Statistics; 3.3.2 Graphical Presentation; 3.3.3 Covariance and Correlation Coefficient; 3.4 The Normal Distribution; 3.4.1 Error Distributions; 3.4.2 Normal Distribution Functions and Tables 3.4.3 Applications3.5 Is a Distribution Normal?; 3.5.1 Cumulative Frequency; 3.5.2 Kolmogorov-Smirnov Test; 3.5.3 Consequences; 3.6 Hypothesis Tests; 3.7 Comparison of Means: the t-Test; 3.8 F-Test for Comparison of Variances; 3.9 Confidence in Linear Regression; 3.9.1 Linear Calibration; 3.9.2 Example; 3.9.3 Confidence of Prediction of Parameters; 3.10 More about Confidence; 3.10.1 Confidence in the Mean; 3.10.2 Confidence in the Standard Deviation; 3.11 Consequences of Outliers and How to Deal with Them; 3.12 Detection of Outliers; 3.12.1 Normal Distributions; 3.12.2 Linear Regression 3.12.3 Multivariate Calibration3.13 Shewhart Charts; 3.14 More about Control Charts; 3.14.1 Cusum Chart; 3.14.2 Range Chart; 3.14.3 Multivariate Statistical Process Control; References; 4 Sequential Methods; 4.1 Sequential Data; 4.2 Correlograms; 4.2.1 Auto-correlograms; 4.2.2 Cross-correlograms; 4.2.3 Multivariate Correlograms; 4.3 Linear Smoothing Functions and Filters; 4.4 Fourier Transforms; 4.5 Maximum Entropy and Bayesian Methods; 4.5.1 Bayes' Theorem; 4.5.2 Maximum Entropy; 4.5.3 Maximum Entropy and Modelling; 4.6 Fourier Filters; 4.7 Peakshapes in Chromatography and Spectroscopy 4.7.1 Principal Features |
| Record Nr. | UNINA-9910143727003321 |
Brereton Richard G
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton
| Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina |
542.30151
543.015195 |
| Soggetto topico |
Chemometrics
Chemistry, Analytic |
| ISBN |
1-280-83864-7
9786610838646 0-470-05778-5 0-470-05777-7 |
| Classificazione | 35.05 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Chemometrics for Scientists; Contents; Preface; 1 Introduction; 1.1 Development of Chemometrics; 1.1.1 Early Developments; 1.1.2 1980s and the Borderlines between Other Disciplines; 1.1.3 1990s and Problems of Intermediate Complexity; 1.1.4 Current Developments in Complex Problem Solving; 1.2 Application Areas; 1.3 How to Use this Book; 1.4 Literature and Other Sources of Information; References; 2 Experimental Design; 2.1 Why Design Experiments in Chemistry?; 2.2 Degrees of Freedom and Sources of Error; 2.3 Analysis of Variance and Interpretation of Errors
2.4 Matrices, Vectors and the Pseudoinverse2.5 Design Matrices; 2.6 Factorial Designs; 2.6.1 Extending the Number of Factors; 2.6.2 Extending the Number of Levels; 2.7 An Example of a Factorial Design; 2.8 Fractional Factorial Designs; 2.9 Plackett-Burman and Taguchi Designs; 2.10 The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction; 2.11 Central Composite Designs; 2.12 Mixture Designs; 2.12.1 Simplex Centroid Designs; 2.12.2 Simplex Lattice Designs; 2.12.3 Constrained Mixture Designs 2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils2.14 Simplex Optimization; 2.15 Leverage and Confidence in Models; 2.16 Designs for Multivariate Calibration; References; 3 Statistical Concepts; 3.1 Statistics for Chemists; 3.2 Errors; 3.2.1 Sampling Errors; 3.2.2 Sample Preparation Errors; 3.2.3 Instrumental Noise; 3.2.4 Sources of Error; 3.3 Describing Data; 3.3.1 Descriptive Statistics; 3.3.2 Graphical Presentation; 3.3.3 Covariance and Correlation Coefficient; 3.4 The Normal Distribution; 3.4.1 Error Distributions; 3.4.2 Normal Distribution Functions and Tables 3.4.3 Applications3.5 Is a Distribution Normal?; 3.5.1 Cumulative Frequency; 3.5.2 Kolmogorov-Smirnov Test; 3.5.3 Consequences; 3.6 Hypothesis Tests; 3.7 Comparison of Means: the t-Test; 3.8 F-Test for Comparison of Variances; 3.9 Confidence in Linear Regression; 3.9.1 Linear Calibration; 3.9.2 Example; 3.9.3 Confidence of Prediction of Parameters; 3.10 More about Confidence; 3.10.1 Confidence in the Mean; 3.10.2 Confidence in the Standard Deviation; 3.11 Consequences of Outliers and How to Deal with Them; 3.12 Detection of Outliers; 3.12.1 Normal Distributions; 3.12.2 Linear Regression 3.12.3 Multivariate Calibration3.13 Shewhart Charts; 3.14 More about Control Charts; 3.14.1 Cusum Chart; 3.14.2 Range Chart; 3.14.3 Multivariate Statistical Process Control; References; 4 Sequential Methods; 4.1 Sequential Data; 4.2 Correlograms; 4.2.1 Auto-correlograms; 4.2.2 Cross-correlograms; 4.2.3 Multivariate Correlograms; 4.3 Linear Smoothing Functions and Filters; 4.4 Fourier Transforms; 4.5 Maximum Entropy and Bayesian Methods; 4.5.1 Bayes' Theorem; 4.5.2 Maximum Entropy; 4.5.3 Maximum Entropy and Modelling; 4.6 Fourier Filters; 4.7 Peakshapes in Chromatography and Spectroscopy 4.7.1 Principal Features |
| Record Nr. | UNINA-9910830136003321 |
|
Brereton Richard G
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton
| Applied chemometrics for scientists [[electronic resource] /] / Richard G. Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina |
542.30151
543.015195 |
| Soggetto topico |
Chemometrics
Chemistry, Analytic |
| ISBN |
1-280-83864-7
9786610838646 0-470-05778-5 0-470-05777-7 |
| Classificazione | 35.05 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Chemometrics for Scientists; Contents; Preface; 1 Introduction; 1.1 Development of Chemometrics; 1.1.1 Early Developments; 1.1.2 1980s and the Borderlines between Other Disciplines; 1.1.3 1990s and Problems of Intermediate Complexity; 1.1.4 Current Developments in Complex Problem Solving; 1.2 Application Areas; 1.3 How to Use this Book; 1.4 Literature and Other Sources of Information; References; 2 Experimental Design; 2.1 Why Design Experiments in Chemistry?; 2.2 Degrees of Freedom and Sources of Error; 2.3 Analysis of Variance and Interpretation of Errors
2.4 Matrices, Vectors and the Pseudoinverse2.5 Design Matrices; 2.6 Factorial Designs; 2.6.1 Extending the Number of Factors; 2.6.2 Extending the Number of Levels; 2.7 An Example of a Factorial Design; 2.8 Fractional Factorial Designs; 2.9 Plackett-Burman and Taguchi Designs; 2.10 The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction; 2.11 Central Composite Designs; 2.12 Mixture Designs; 2.12.1 Simplex Centroid Designs; 2.12.2 Simplex Lattice Designs; 2.12.3 Constrained Mixture Designs 2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils2.14 Simplex Optimization; 2.15 Leverage and Confidence in Models; 2.16 Designs for Multivariate Calibration; References; 3 Statistical Concepts; 3.1 Statistics for Chemists; 3.2 Errors; 3.2.1 Sampling Errors; 3.2.2 Sample Preparation Errors; 3.2.3 Instrumental Noise; 3.2.4 Sources of Error; 3.3 Describing Data; 3.3.1 Descriptive Statistics; 3.3.2 Graphical Presentation; 3.3.3 Covariance and Correlation Coefficient; 3.4 The Normal Distribution; 3.4.1 Error Distributions; 3.4.2 Normal Distribution Functions and Tables 3.4.3 Applications3.5 Is a Distribution Normal?; 3.5.1 Cumulative Frequency; 3.5.2 Kolmogorov-Smirnov Test; 3.5.3 Consequences; 3.6 Hypothesis Tests; 3.7 Comparison of Means: the t-Test; 3.8 F-Test for Comparison of Variances; 3.9 Confidence in Linear Regression; 3.9.1 Linear Calibration; 3.9.2 Example; 3.9.3 Confidence of Prediction of Parameters; 3.10 More about Confidence; 3.10.1 Confidence in the Mean; 3.10.2 Confidence in the Standard Deviation; 3.11 Consequences of Outliers and How to Deal with Them; 3.12 Detection of Outliers; 3.12.1 Normal Distributions; 3.12.2 Linear Regression 3.12.3 Multivariate Calibration3.13 Shewhart Charts; 3.14 More about Control Charts; 3.14.1 Cusum Chart; 3.14.2 Range Chart; 3.14.3 Multivariate Statistical Process Control; References; 4 Sequential Methods; 4.1 Sequential Data; 4.2 Correlograms; 4.2.1 Auto-correlograms; 4.2.2 Cross-correlograms; 4.2.3 Multivariate Correlograms; 4.3 Linear Smoothing Functions and Filters; 4.4 Fourier Transforms; 4.5 Maximum Entropy and Bayesian Methods; 4.5.1 Bayes' Theorem; 4.5.2 Maximum Entropy; 4.5.3 Maximum Entropy and Modelling; 4.6 Fourier Filters; 4.7 Peakshapes in Chromatography and Spectroscopy 4.7.1 Principal Features |
| Record Nr. | UNINA-9910841605003321 |
|
Brereton Richard G
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton
| Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
540.151
543.0015195 |
| Soggetto topico |
Chemometrics - Data processing
Chemical processes - Statistical methods - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9786610269686
0-470-66760-5 0-470-86324-2 1-280-26968-5 0-470-84574-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemometrics; Contents; Preface; Supplementary Information; Acknowledgements; 1 Introduction; 1.1 Points of View; 1.2 Software and Calculations; 1.3 Further Reading; 1.3.1 General; 1.3.2 Specific Areas; 1.3.3 Internet Resources; 1.4 References; 2 Experimental Design; 2.1 Introduction; 2.2 Basic Principles; 2.2.1 Degrees of Freedom; 2.2.2 Analysis of Variance and Comparison of Errors; 2.2.3 Design Matrices and Modelling; 2.2.4 Assessment of Significance; 2.2.5 Leverage and Confidence in Models; 2.3 Factorial Designs; 2.3.1 Full Factorial Designs; 2.3.2 Fractional Factorial Designs
2.3.3 Plackett-Burman and Taguchi Designs2.3.4 Partial Factorials at Several Levels: Calibration Designs; 2.4 Central Composite or Response Surface Designs; 2.4.1 Setting Up the Design; 2.4.2 Degrees of Freedom; 2.4.3 Axial Points; 2.4.4 Modelling; 2.4.5 Statistical Factors; 2.5 Mixture Designs; 2.5.1 Mixture Space; 2.5.2 Simplex Centroid; 2.5.3 Simplex Lattice; 2.5.4 Constraints; 2.5.5 Process Variables; 2.6 Simplex Optimisation; 2.6.1 Fixed Sized Simplex; 2.6.2 Elaborations; 2.6.3 Modified Simplex; 2.6.4 Limitations; Problems; 3 Signal Processing; 3.1 Sequential Signals in Chemistry 3.1.1 Environmental and Geological Processes3.1.2 Industrial Process Control; 3.1.3 Chromatograms and Spectra; 3.1.4 Fourier Transforms; 3.1.5 Advanced Methods; 3.2 Basics; 3.2.1 Peakshapes; 3.2.2 Digitisation; 3.2.3 Noise; 3.2.4 Sequential Processes; 3.3 Linear Filters; 3.3.1 Smoothing Functions; 3.3.2 Derivatives; 3.3.3 Convolution; 3.4 Correlograms and Time Series Analysis; 3.4.1 Auto-correlograms; 3.4.2 Cross-correlograms; 3.4.3 Multivariate Correlograms; 3.5 Fourier Transform Techniques; 3.5.1 Fourier Transforms; 3.5.2 Fourier Filters; 3.5.3 Convolution Theorem; 3.6 Topical Methods 3.6.1 Kalman Filters3.6.2 Wavelet Transforms; 3.6.3 Maximum Entropy (Maxent) and Bayesian Methods; Problems; 4 Pattern Recognition; 4.1 Introduction; 4.1.1 Exploratory Data Analysis; 4.1.2 Unsupervised Pattern Recognition; 4.1.3 Supervised Pattern Recognition; 4.2 The Concept and Need for Principal Components Analysis; 4.2.1 History; 4.2.2 Case Studies; 4.2.3 Multivariate Data Matrices; 4.2.4 Aims of PCA; 4.3 Principal Components Analysis: the Method; 4.3.1 Chemical Factors; 4.3.2 Scores and Loadings; 4.3.3 Rank and Eigenvalues; 4.3.4 Factor Analysis 4.3.5 Graphical Representation of Scores and Loadings4.3.6 Preprocessing; 4.3.7 Comparing Multivariate Patterns; 4.4 Unsupervised Pattern Recognition: Cluster Analysis; 4.4.1 Similarity; 4.4.2 Linkage; 4.4.3 Next Steps; 4.4.4 Dendrograms; 4.5 Supervised Pattern Recognition; 4.5.1 General Principles; 4.5.2 Discriminant Analysis; 4.5.3 SIMCA; 4.5.4 Discriminant PLS; 4.5.5 K Nearest Neighbours; 4.6 Multiway Pattern Recognition; 4.6.1 Tucker3 Models; 4.6.2 PARAFAC; 4.6.3 Unfolding; Problems; 5 Calibration; 5.1 Introduction; 5.1.1 History and Usage; 5.1.2 Case Study; 5.1.3 Terminology 5.2 Univariate Calibration |
| Record Nr. | UNINA-9910143558103321 |
|
Brereton Richard G
|
||
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton
| Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
540.151
543.0015195 |
| Soggetto topico |
Chemometrics - Data processing
Chemical processes - Statistical methods - Data processing |
| ISBN |
9786610269686
0-470-66760-5 0-470-86324-2 1-280-26968-5 0-470-84574-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemometrics; Contents; Preface; Supplementary Information; Acknowledgements; 1 Introduction; 1.1 Points of View; 1.2 Software and Calculations; 1.3 Further Reading; 1.3.1 General; 1.3.2 Specific Areas; 1.3.3 Internet Resources; 1.4 References; 2 Experimental Design; 2.1 Introduction; 2.2 Basic Principles; 2.2.1 Degrees of Freedom; 2.2.2 Analysis of Variance and Comparison of Errors; 2.2.3 Design Matrices and Modelling; 2.2.4 Assessment of Significance; 2.2.5 Leverage and Confidence in Models; 2.3 Factorial Designs; 2.3.1 Full Factorial Designs; 2.3.2 Fractional Factorial Designs
2.3.3 Plackett-Burman and Taguchi Designs2.3.4 Partial Factorials at Several Levels: Calibration Designs; 2.4 Central Composite or Response Surface Designs; 2.4.1 Setting Up the Design; 2.4.2 Degrees of Freedom; 2.4.3 Axial Points; 2.4.4 Modelling; 2.4.5 Statistical Factors; 2.5 Mixture Designs; 2.5.1 Mixture Space; 2.5.2 Simplex Centroid; 2.5.3 Simplex Lattice; 2.5.4 Constraints; 2.5.5 Process Variables; 2.6 Simplex Optimisation; 2.6.1 Fixed Sized Simplex; 2.6.2 Elaborations; 2.6.3 Modified Simplex; 2.6.4 Limitations; Problems; 3 Signal Processing; 3.1 Sequential Signals in Chemistry 3.1.1 Environmental and Geological Processes3.1.2 Industrial Process Control; 3.1.3 Chromatograms and Spectra; 3.1.4 Fourier Transforms; 3.1.5 Advanced Methods; 3.2 Basics; 3.2.1 Peakshapes; 3.2.2 Digitisation; 3.2.3 Noise; 3.2.4 Sequential Processes; 3.3 Linear Filters; 3.3.1 Smoothing Functions; 3.3.2 Derivatives; 3.3.3 Convolution; 3.4 Correlograms and Time Series Analysis; 3.4.1 Auto-correlograms; 3.4.2 Cross-correlograms; 3.4.3 Multivariate Correlograms; 3.5 Fourier Transform Techniques; 3.5.1 Fourier Transforms; 3.5.2 Fourier Filters; 3.5.3 Convolution Theorem; 3.6 Topical Methods 3.6.1 Kalman Filters3.6.2 Wavelet Transforms; 3.6.3 Maximum Entropy (Maxent) and Bayesian Methods; Problems; 4 Pattern Recognition; 4.1 Introduction; 4.1.1 Exploratory Data Analysis; 4.1.2 Unsupervised Pattern Recognition; 4.1.3 Supervised Pattern Recognition; 4.2 The Concept and Need for Principal Components Analysis; 4.2.1 History; 4.2.2 Case Studies; 4.2.3 Multivariate Data Matrices; 4.2.4 Aims of PCA; 4.3 Principal Components Analysis: the Method; 4.3.1 Chemical Factors; 4.3.2 Scores and Loadings; 4.3.3 Rank and Eigenvalues; 4.3.4 Factor Analysis 4.3.5 Graphical Representation of Scores and Loadings4.3.6 Preprocessing; 4.3.7 Comparing Multivariate Patterns; 4.4 Unsupervised Pattern Recognition: Cluster Analysis; 4.4.1 Similarity; 4.4.2 Linkage; 4.4.3 Next Steps; 4.4.4 Dendrograms; 4.5 Supervised Pattern Recognition; 4.5.1 General Principles; 4.5.2 Discriminant Analysis; 4.5.3 SIMCA; 4.5.4 Discriminant PLS; 4.5.5 K Nearest Neighbours; 4.6 Multiway Pattern Recognition; 4.6.1 Tucker3 Models; 4.6.2 PARAFAC; 4.6.3 Unfolding; Problems; 5 Calibration; 5.1 Introduction; 5.1.1 History and Usage; 5.1.2 Case Study; 5.1.3 Terminology 5.2 Univariate Calibration |
| Record Nr. | UNINA-9910831073603321 |
|
Brereton Richard G
|
||
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton
| Chemometrics [[electronic resource] ] : data analysis for the laboratory and chemical plant / / Richard Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
540.151
543.0015195 |
| Soggetto topico |
Chemometrics - Data processing
Chemical processes - Statistical methods - Data processing |
| ISBN |
9786610269686
0-470-66760-5 0-470-86324-2 1-280-26968-5 0-470-84574-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemometrics; Contents; Preface; Supplementary Information; Acknowledgements; 1 Introduction; 1.1 Points of View; 1.2 Software and Calculations; 1.3 Further Reading; 1.3.1 General; 1.3.2 Specific Areas; 1.3.3 Internet Resources; 1.4 References; 2 Experimental Design; 2.1 Introduction; 2.2 Basic Principles; 2.2.1 Degrees of Freedom; 2.2.2 Analysis of Variance and Comparison of Errors; 2.2.3 Design Matrices and Modelling; 2.2.4 Assessment of Significance; 2.2.5 Leverage and Confidence in Models; 2.3 Factorial Designs; 2.3.1 Full Factorial Designs; 2.3.2 Fractional Factorial Designs
2.3.3 Plackett-Burman and Taguchi Designs2.3.4 Partial Factorials at Several Levels: Calibration Designs; 2.4 Central Composite or Response Surface Designs; 2.4.1 Setting Up the Design; 2.4.2 Degrees of Freedom; 2.4.3 Axial Points; 2.4.4 Modelling; 2.4.5 Statistical Factors; 2.5 Mixture Designs; 2.5.1 Mixture Space; 2.5.2 Simplex Centroid; 2.5.3 Simplex Lattice; 2.5.4 Constraints; 2.5.5 Process Variables; 2.6 Simplex Optimisation; 2.6.1 Fixed Sized Simplex; 2.6.2 Elaborations; 2.6.3 Modified Simplex; 2.6.4 Limitations; Problems; 3 Signal Processing; 3.1 Sequential Signals in Chemistry 3.1.1 Environmental and Geological Processes3.1.2 Industrial Process Control; 3.1.3 Chromatograms and Spectra; 3.1.4 Fourier Transforms; 3.1.5 Advanced Methods; 3.2 Basics; 3.2.1 Peakshapes; 3.2.2 Digitisation; 3.2.3 Noise; 3.2.4 Sequential Processes; 3.3 Linear Filters; 3.3.1 Smoothing Functions; 3.3.2 Derivatives; 3.3.3 Convolution; 3.4 Correlograms and Time Series Analysis; 3.4.1 Auto-correlograms; 3.4.2 Cross-correlograms; 3.4.3 Multivariate Correlograms; 3.5 Fourier Transform Techniques; 3.5.1 Fourier Transforms; 3.5.2 Fourier Filters; 3.5.3 Convolution Theorem; 3.6 Topical Methods 3.6.1 Kalman Filters3.6.2 Wavelet Transforms; 3.6.3 Maximum Entropy (Maxent) and Bayesian Methods; Problems; 4 Pattern Recognition; 4.1 Introduction; 4.1.1 Exploratory Data Analysis; 4.1.2 Unsupervised Pattern Recognition; 4.1.3 Supervised Pattern Recognition; 4.2 The Concept and Need for Principal Components Analysis; 4.2.1 History; 4.2.2 Case Studies; 4.2.3 Multivariate Data Matrices; 4.2.4 Aims of PCA; 4.3 Principal Components Analysis: the Method; 4.3.1 Chemical Factors; 4.3.2 Scores and Loadings; 4.3.3 Rank and Eigenvalues; 4.3.4 Factor Analysis 4.3.5 Graphical Representation of Scores and Loadings4.3.6 Preprocessing; 4.3.7 Comparing Multivariate Patterns; 4.4 Unsupervised Pattern Recognition: Cluster Analysis; 4.4.1 Similarity; 4.4.2 Linkage; 4.4.3 Next Steps; 4.4.4 Dendrograms; 4.5 Supervised Pattern Recognition; 4.5.1 General Principles; 4.5.2 Discriminant Analysis; 4.5.3 SIMCA; 4.5.4 Discriminant PLS; 4.5.5 K Nearest Neighbours; 4.6 Multiway Pattern Recognition; 4.6.1 Tucker3 Models; 4.6.2 PARAFAC; 4.6.3 Unfolding; Problems; 5 Calibration; 5.1 Introduction; 5.1.1 History and Usage; 5.1.2 Case Study; 5.1.3 Terminology 5.2 Univariate Calibration |
| Record Nr. | UNINA-9910841331903321 |
|
Brereton Richard G
|
||
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemometrics for pattern recognition [[electronic resource] /] / Richard Brereton
| Chemometrics for pattern recognition [[electronic resource] /] / Richard Brereton |
| Autore | Brereton Richard G |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K. ; ; Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (524 p.) |
| Disciplina | 543.01/5195 |
| Soggetto topico |
Chemometrics
Pattern perception |
| ISBN |
1-282-18631-0
9786612186318 0-470-74646-7 0-470-74647-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemometrics 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
2.8 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 3.2.2 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 4.3.1 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 5.5.2 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 6.4.4 Q-Statistic or Squared Prediction Error |
| Record Nr. | UNINA-9910139922803321 |
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Brereton Richard G
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| Chichester, West Sussex, U.K. ; ; Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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