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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Applied chemometrics for scientists / / Richard G. Brereton
Applied chemometrics for scientists / / 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-9910876853903321
Brereton Richard G  
Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Chemometrics : data analysis for the laboratory and chemical plant / / Richard Brereton
Chemometrics : 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 543/.007/27
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-9910877856003321
Brereton Richard G  
Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Chemometrics for pattern recognition / / Richard Brereton
Chemometrics for pattern recognition / / Richard Brereton
Autore Brereton Richard G
Edizione [1st ed.]
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
Brereton Richard G  
Chichester, West Sussex, U.K. ; ; Hoboken, NJ, : Wiley, 2009
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Lo trovi qui: Univ. Federico II
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Data Analysis and Chemometrics for Metabolomics
Data Analysis and Chemometrics for Metabolomics
Autore Brereton Richard G
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (435 pages)
ISBN 1-119-63939-5
1-119-63937-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- About the Companion Website -- Chapter 1 Introduction -- 1.1 Chemometrics -- 1.2 Metabolomics -- 1.3 Case Studies -- 1.4 Software -- References -- Chapter 2 Instrumental Methods -- 2.1 Introduction -- 2.2 Coupled Chromatography Mass Spectrometry -- 2.2.1 Chromatography -- 2.2.2 Ionisation and Detection -- 2.2.2.1 GCMS -- 2.2.2.2 LCMS -- 2.2.3 Data Matrices and Peak Tables -- 2.2.4 Step 1 of Creating a Peak Table: Transforming Chromatographic Data to an Aligned Matrix of Peaks -- 2.2.4.1 XCMS -- 2.2.4.2 Multivariate Curve Resolution -- 2.2.4.3 AMDIS -- 2.2.4.4 MZmine -- 2.2.4.5 Other Approaches -- 2.2.4.6 Which Approach is Most Suitable? -- 2.2.5 Step 2 of Creating a Peak Table: Manual Inspection -- 2.2.6 Step 3 of Creating a Peak Table: Identifying Metabolites and Annotating the Peaks -- 2.2.6.1 Experimental Libraries -- 2.2.6.2 Computational Mass Spectral and Retention Libraries -- 2.2.6.3 Expert Systems -- 2.3 Single Wavelength HPLC -- 2.4 Nuclear Magnetic Resonance -- 2.4.1 Fourier Transform Techniques -- 2.4.1.1 FT Principles -- 2.4.1.2 Resolution and Signal-to-Noise -- 2.4.2 Preparing the Transformed Spectra -- 2.4.3 Preparing the Data Table -- 2.4.3.1 Chemometric Approach -- 2.4.3.2 Deconvolution and Identification -- 2.4.4 Identification of Metabolites -- 2.5 Vibrational Spectroscopy -- 2.5.1 Raman Spectroscopy -- 2.5.2 Fourier Transform Infrared Spectroscopy -- Chapter 3 Case Studies -- 3.1 Introduction -- 3.2 Case Study 1: Presymptomatic Study of Humans With Rheumatoid Arthritis Using Blood Plasma and LCMS -- 3.3 Case Study 2: Diagnosis of Malaria in Human Blood Plasma of Children Using GCMS -- 3.4 Case Study 3: Measurement of Triglyclerides in Children's Blood Serum Using NMR.
3.5 Case Study 4: Glucose Intolerance and Diabetes in Humans as Assessed by Blood Serum Using NMR -- 3.6 Case Study 5: Metabolic Changes in Maize Due to Cold as Assessed by NMR -- 3.7 Case Study 6: Effect of Nitrates on Different Parts of Wheat Leaves as Analysed by FTIR -- 3.8 Case Study 7: Rapid Discrimination of Enterococcal Bacteria in Faecal Isolates by Raman Spectroscopy -- 3.9 Case Study 8: Effects of Salinity, Temperature and Hypoxia on Daphnia Magna Metabolism as Studied by GCMS -- 3.10 Case Study 9: Bioactivity in a Chinese Herbal Medicine Studies Using HPLC -- 3.11 Case Study 10: Diabetes in Mice Studied by LCMS -- Chapter 4 Principal Component Analysis -- 4.1 A Simple Example: Matrices, Vectors and Scalars -- 4.2 Visualising the Data Direct -- 4.3 Principal Components Analysis: Scores, Loadings and Eigenvalues -- 4.3.1 PCA -- 4.3.2 Scores -- 4.3.3 Loadings -- 4.3.4 Relationship Between Scores and Loadings -- 4.3.5 Eigenvalues -- 4.3.6 Reducing the Number of PCs -- 4.4 Exploration by PCA of Case Study 5 in Detail: NMR Study of the Effect of Temperature on Maize -- 4.4.1 Variable Plots -- 4.4.2 Scores and Loadings Plots of the Whole Standardised Data -- 4.4.3 Scores and Loadings of the Low-temperature Data -- 4.5 PCA of Different Case Studies -- 4.5.1 Case Study 1: LCMS Studies of Pre-arthritis -- 4.5.2 Case Study 4: NMR of Human Diabetes -- 4.5.3 Case Study 10: LCMS of Diabetes in Mice -- 4.5.4 Case Study 6: FTIR of Effect of Nitrates on Wheat -- 4.5.5 Case Study 3: NMR for Triglycerides in Serum -- 4.5.6 Case Study 7: Raman of Bacterial Faecal Isolates -- 4.6 Transforming the Data -- 4.6.1 Row Scaling -- 4.6.1.1 Row Scaling to Constant Total -- 4.6.1.2 Standard Normal Variates -- 4.6.1.3 Scaling to Reference Standards -- 4.6.2 Column Centring -- 4.6.3 Column Standardisation -- 4.6.4 Logarithmic Transformation -- 4.7 Common Issues.
4.7.1 Missing Data -- 4.7.2 Quality Control Samples -- 4.7.3 Variable Reduction -- Chapter 5 Statistical Basics -- 5.1 Use of P Values and Hypothesis Testing -- 5.2 Distributions and Significance -- 5.2.1 Simulated Case Study -- 5.2.2 The Normal (z) Distribution and p Values -- 5.2.3 t-Distribution and Degrees of Freedom -- 5.2.4 χ2-Distribution -- 5.2.5 F-Distribution and Hotelling's T2 -- 5.3 Multivariate Calculation of P Values and the Mahalanobis Distance -- 5.4 Discriminatory Variables -- 5.5 Conclusions -- Chapter 6 Choosing Samples -- 6.1 Motivation -- 6.2 Design of Experiments -- 6.2.1 Factors, Response and Coding -- 6.2.2 Replicates -- 6.2.3 Statistical Designs -- 6.2.3.1 Fully Crossed Designs -- 6.2.3.2 Two-level Full Factorial Designs -- 6.2.3.3 Fractional Factorial Designs -- 6.3 Sampling Designs -- 6.3.1 Simple Random Sampling -- 6.3.2 Systematic Sampling -- 6.3.3 Stratified Sampling -- 6.3.4 Cluster Sampling -- 6.3.5 Multi-stage Sampling -- Chapter 7 Determining the Provenance of a Sample -- 7.1 Pattern Recognition -- 7.2 Preliminary Processing Prior to Classification -- 7.3 Simulated Case Studies -- 7.4 Two-Class Classifiers -- 7.4.1 Linear Discriminant Analysis -- 7.4.2 Partial Least Squares Discriminant Analysis -- 7.4.2.1 PLSDA for Equal Class Sizes -- 7.4.2.2 PLSDA for Unequal Class Sizes -- 7.4.2.3 OPLS -- 7.5 One-Class Classifiers -- 7.5.1 Quadratic Discriminant Analysis -- 7.5.2 SIMCA -- 7.5.2.1 Disjoint PCA -- 7.5.2.2 D- and Q-statistics -- 7.5.2.3 Limits and Decisions -- 7.6 Multiclass Classifiers -- 7.6.1 LDA as a Multiclass Classifier -- 7.6.2 PLSDA as a Multiclass Classifier -- 7.6.2.1 One Versus All -- 7.6.2.2 One Versus One -- 7.6.2.3 PLS2DA -- 7.6.3 Multilevel PLSDA -- 7.7 Validation, Optimisation and Performance Indicators -- 7.7.1 Classification Performance -- 7.7.1.1 Two Classes -- 7.7.1.2 Multiclasses.
7.7.1.3 One-Class Models -- 7.7.2 Validation -- 7.7.3 Optimisation -- Chapter 8 Multivariate Calibration -- 8.1 Introduction -- 8.2 Partial Least Squares Regression -- 8.3 Training and Test Sets -- 8.4 Optimisation: Number of PLS Components -- Chapter 9 Selecting the Most Significant Variables and Markers -- 9.1 Introduction -- 9.2 Univariate Approaches -- 9.3 Loadings, Weights And Vip Scores -- 9.3.1 Principal Component Loadings -- 9.3.2 PLSDA Loadings and Weights -- 9.3.3 VIP Scores -- 9.3.4 P Values -- 9.3.5 Multilevel PLSDA -- 9.4 Selectivity Ratios -- 9.5 Volcano Plots -- Chapter 10 Which Factors are Most Significant -- 10.1 Introduction -- 10.2 Terminology and Definitions -- 10.3 Single Factor (One-Way - One-Factor) Anova Test And Regression -- 10.3.1 Balanced Design at Two Levels -- 10.3.1.1 Degrees of Freedom -- 10.3.1.2 ANOVA Test -- 10.3.1.3 Regression -- 10.3.1.4 The t-test -- 10.3.2 Unbalanced Design at Two Levels -- 10.3.3 Multiple One-Way Design with Two Levels: Multilinear Regression -- 10.3.4 Multilevel Designs -- 10.3.4.1 One-Way Multilevel ANOVA Test -- 10.3.4.2 One-Way Multilevel Regression with Dummy Variables: Unrelated Groups -- 10.3.4.3 One-Way Multilevel Multilinear Regression: Related Groups -- 10.3.4.4 Comparison and Interpretation -- 10.4 Multiple Factor (Multiway) ANOVA Test and Regression -- 10.4.1 Simulated 2 × 3 Case Study: ANOVA Test and Regression -- 10.4.1.1 ANOVA Test -- 10.4.1.2 Regression with Dummy Variables -- 10.4.2 Two-level Multiway Factorial Designs -- 10.5 ASCA -- 10.5.1 Simulated Dataset -- 10.5.2 Case Study: Environmental Effect on Daphnia -- Index -- EULA.
Record Nr. UNINA-9910877060303321
Brereton Richard G  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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