Cargando…
Practical three-way calibration /
Practical Three-Way Calibration is an introductory-level guide to the complex field of analytical calibration with three-way instrumental data. With minimal use of mathematical/statistical expressions, it walks the reader through the analytical methodologies with helpful images and step-by-step expl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
2014.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Practical Three-Way Calibration; Copyright; Dedication; Contents; Preface; References; Foreword; Acknowledgments; Chapter 1
- Calibration Scenarios; 1.1 Calibration; 1.2 Univariate calibration; 1.3 Multivariate calibration; 1.4 Nomenclature for data and calibrations; 1.5 Nomenclature for constituents and samples; 1.6 Multiway calibration; 1.7 Why multiway calibration?; 1.8 Analytical advantages; References; Chapter 2
- Data Properties; 2.1 Data properties; 2.2 Bilinear data; 2.3 Normalization and concentration effects; 2.4 A word of caution on bilinearity; 2.5 Nonbilinear data.
- 2.6 Trilinear data2.7 Nontrilinear data; 2.8 Transforming three-way data into matrix data; 2.9 Normalization and concentration effects; 2.10 Classification of three-way data; 2.11 Importance of classifying three-way data; References; Chapter 3
- Experimental Three-way/Second-order Data; 3.1 Generation of three-way data; 3.2 Matrix fluorescence spectroscopy; 3.3 Chromatography with spectral detection; 3.4 Other second-order instrumental data; 3.5 Data organization in files; 3.6 Samples for calibration and validation; References; Chapter 4
- The MVC2 Software.
- 4.1 Methods, models, algorithms and software4.2 The MVC2 software; 4.3 The MVC2 data examples; 4.4 The EEFM_data example; 4.5 Plotting EEFM_data matrices; 4.6 The LCDAD_data example; 4.7 Plotting LCDAD_data matrices; 4.8 Further MVC2 features; References; Chapter 5
- Parallel Factor Analysis: Trilinear Data; 5.1 Trilinear modeling and decomposition; 5.2 Uniqueness and the second-order advantage; 5.3 Processing the EEFM_data example; 5.4 PARAFAC analysis of a test sample; 5.5 Estimating the number of components; 5.6 Analyte quantitation in the test sample; 5.7 Analysis of the remaining samples.
- 5.8 Profiles for potential interferents5.9 Further processing options; 5.10 Multiple-sample processing; 5.11 Concluding remarks; 5.12 Homework 1; 5.13 Homework 2; References; Chapter 6
- Analytical Figures of Merit; 6.1 Definition of figure of merit; 6.2 Importance of analytical figures of merit; 6.3 Sensitivity; 6.4 Selectivity; 6.5 Analytical sensitivity; 6.6 Prediction uncertainty; 6.7 Limit of detection; 6.8 Limit of quantitation; 6.9 The complete PARAFAC report; 6.10 Final considerations; References; Chapter 7
- Parallel Factor Analysis: Nontrilinear Data of Type 1.
- 7.1 An apparent contradiction7.2 Description of the data set; 7.3 PARAFAC study of a test sample; 7.4 Increasing the number of PARAFAC components; 7.5 Study of the remaining samples; 7.6 Other separation data and what to do; 7.7 A PARAFAC variant for chromatographic data; 7.8 PARAFAC2 calibration with the LCDAD_data; 7.9 Chromatographic alignment; 7.10 Homework; References; Chapter 8
- Multivariate Curve Resolution-Alternating Least-Squares; 8.1 Multivariate curve resolution-alternating least-squares; 8.2 Estimating the number of components; 8.3 MCR-ALS initialization; 8.4 Constraints.