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Fundamentals and analytical applications of multiway calibration /
Fundamentals and Analytical Applications of Multi-Way Calibration presents researchers with a set of effective tools they can use to obtain the maximum information from instrumental data. It includes the most advanced techniques, methods, and algorithms related to multi-way calibration and the ways...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2015]
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| Edición: | First edition. |
| Colección: | Data handling in science and technology ;
29. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Fundamentals and Analytical Applications of Multiway Calibration; Copyright; Contents; Contributors; Preface; References; References; References; Views on Multiway Calibration: Its Past and Future; Chapter 1: Fundamentals of PARAFAC; 1. Introduction; 2. Notation; 3. Second-Order Structure and Multiway Data; 4. The PARAFAC Model; 5. Algorithms; 5.1. Alternating Least-Squares; 5.2. Prediction; 6. PARAFAC Modeling and Validation; 6.1. Data Preprocessing; 6.2. Model Refinement and Constraints; 6.3. Model Diagnostics and Interpretation of Results; 6.3.1. Analysis of the Residuals.
- 6.3.2. Analysis of Spectral Loadings6.3.3. Core Consistency; 6.3.4. Split-Half Analysis; 6.3.5. Cross-Validation; 6.3.6. Departure from Trilinearity; 6.4. Figures of Merit; 6.4.1. Accuracy; 6.4.2. Sensitivity; 6.4.3. Selectivity; 6.4.4. Limit of Detection; 7. Application Example of PARAFAC; Appendix. Some Useful Products and Operators in Three-Way Data Analysis; References; Chapter 2: Usefulness of PARAFAC for the Quantification, Identification, and Description of Analytical Data; 1. Theoretical Elements; 1.1. PARAFAC Model; 1.2. Uniqueness and Partial Uniqueness; 1.3. Tucker3 Model.
- 1.4. PARAFAC2 Model1.5. Model Complexity; 2. Quantification and Identification with Excitation-Emission Molecular Fluorescence Data; 2.1. Case I: Determination of Ciprofloxacin (Added in Human Urine); 2.2. Case II: Determination of Ciprofloxacin (Added in Human Urine of a Patient Being Treated with Mesalazine); 2.3. Case III: Determination of Ciprofloxacin in Human Urine of a Patient Who Is Taking Ciprofloxacin; 3. Quantification and Identification with PTV-GC-MS Data in the Context of Regulated Analysis; 4. Quantification with Data of an Electronic Nose Based on Metal Oxide Sensors.
- 5. Describing the Grape Maturity at Harvest by Means of Physicochemical VariablesAcknowledgments; References; Chapter 3: Multiway Calibration Based on Alternating Multilinear Decomposition; 1. Introduction; 2. Terminology and Nomenclature in Multiway Data Analysis; 2.1. Terminology; 2.2. Nomenclature; 3. Multilinear Models; 3.1. Trilinear Model; 3.2. Quadrilinear Model; 3.3. Quinquelinear Model; 4. Advantages of Multiway Calibration; 4.1. Uniqueness Property; 4.2. Second- and Third-Order Advantages; 4.3. Multiway Cyclic Symmetry; 5. Algorithms for Multiway Calibration.
- 5.1. Second-Order Calibration5.1.1. Parallel Factor Analysis; 5.1.2. Alternating Trilinear Decomposition; 5.1.3. Self-Weighted Alternating Trilinear Decomposition; 5.1.4. Alternating Penalty Trilinear Decomposition; 5.1.5. Alternating Coupled Two-Unequal Residual Functions; 5.1.6. Algorithm Combination Methodology; 5.1.7. Comparison of Algorithms for Second-Order Calibration; 5.2. Third-Order Calibration; 5.2.1. Four-way PARAFAC; 5.2.2. Alternating Quadrilinear Decomposition; 5.2.3. Alternating Penalty Quadrilinear Decomposition.