Practical Three-Way Calibration
- 1st Edition - April 8, 2014
- Latest edition
- Authors: Alejandro Olivieri, Graciela M. Escandar
- Language: English
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 mathemati… Read more
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 explanations. Unlike other books on the subject, there is no need for prior programming experience and no need to learn programming languages. Easy-to-use graphical interfaces and intuitive descriptions of mathematical and statistical concepts make three-way calibration methodologies accessible to analytical chemists and scientists in a wide range of disciplines in industry and academia.
- Numerous detailed examples of slowly increasing complexity
- Exposure to several different data sets and techniques through figures and diagrams
- Computer program screenshots for easy learning without prior knowledge of programming languages
- Minimal use of mathematical/statistical expressions
Chemists; chemical engineers; pharmacists; environmental, forensics, atmospheric, and life scientists; graduate level students in these disciplines
- Dedication
- Preface
- 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
- 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 data
- 2.7. 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
- 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
- Other second-order instrumental data
- 3.5. Data organization in files
- 3.6. Samples for calibration and validation
- Chapter 4. The MVC2 Software
- 4.1. Methods, models, algorithms and software
- 4.2. 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
- 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 interferents
- 5.9. Further processing options
- 5.10. Multiple-sample processing
- 5.11. Concluding remarks
- 5.12. Homework 1
- 5.13. Homework 2
- Chapter 6. Analytical Figures of Merit
- 6.1. Definition of figure of merit
- 6.2. Importance of analytical figures of merit
- 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
- Chapter 7. Parallel Factor Analysis: Nontrilinear Data of Type 1
- 7.1. An apparent contradiction
- 7.2. 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
- 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
- MCR–ALS analysis of the LCDAD_data set
- 8.6. Analyte prediction in the test samples
- 8.7. Analyte prediction in all test samples simultaneously
- 8.8. Analytical figures of merit
- 8.9. Conclusion
- 8.10. Homework 1
- 8.11. Homework 2
- Chapter 9. Partial Least-Squares with Residual Bilinearization
- 9.1. Introduction
- 9.2. Unfolded partial least-squares
- 9.3. Estimating the number of calibration components
- 9.4. Residual bilinearization
- 9.5. The EEFM_data set: cross-validation
- 9.6. The EEFM_data set: RBL and prediction
- 9.7. Analytical figures of merit
- 9.8. The LCDAD_data set
- 9.9. The EEFM_IF_data set
- 9.10. U-PLS calibration in the EEFM_IF_data set
- 9.11. RBL in the EEFM_IF_data set
- 9.12. Other RBL methodologies
- 9.13. Other Nontrilinear Type 2 data
- 9.14. The Cinderella type 3 data
- 9.15. Conclusion
- 9.16. Homework 1
- 9.17. Homework 2
- 9.18. Homework 3
- Chapter 10. Three-way/Second-order Standard Addition
- 10.1. Why standard addition?
- 10.2. The EEFM_SA example
- 10.3. Processing the EEFM_SA data set with PARAFAC
- 10.4. Processing the EEFM_SA data set with MCR–ALS
- 10.5. Can the EEFM_SA data set be processed with U-PLS/RBL?
- Chapter 11. Third-order/Four-way Calibration and Beyond
- 11.1. Third-order/four-way data
- 11.2. Generation of third-order/four-way data
- Classification of third-order/four-way data
- 11.4. Algorithms
- 11.5. Data points in each mode
- 11.6. Fourth-order/five-way data
- 11.7. Figures of merit
- 11.8. Further higher-order advantages
- Chapter 12. Application Example: PARAFAC
- 12.1. Trilinear data
- 12.2. What algorithm should be chosen?
- 12.3. A literature EEFM example
- 12.4. How was the whole experiment designed?
- 12.5. How were the calibration concentrations chosen?
- 12.6. How were the validation concentrations chosen?
- 12.7. How were the wavelength ranges chosen?
- 12.8. PARAFAC processing using MVC2: validation samples
- 12.9. What happens for a smaller number of components?
- 12.10. What happens for a larger number of components?
- 12.11. Analyte prediction
- 12.12. Why analyzing test samples?
- 12.13. PARAFAC processing using MVC2: test samples
- 12.14. Why analyzing real samples?
- Chapter 13. Application Example: MCR–ALS
- 13.1. Nontrilinear data of Type 1
- 13.2. How to solve this problem?
- 13.3. Why coupling multivariate calibration to a separative method?
- 13.4. A literature example
- 13.5. Which are the difficulties of aligning chromatographic bands in this complex system?
- 13.6. What algorithm should be chosen?
- 13.7. How was the whole experiment designed?
- 13.8. Preparing the calibration and validation sets
- 13.9. How were the validation concentrations chosen?
- 13.10. How were the measuring ranges selected?
- 13.11. MCR–ALS processing using MVC2: validation samples
- 13.12. Why test samples should be analyzed?
- 13.13. MCR–ALS processing using MVC2: test samples
- 13.14. Analysis of real samples
- 13.15. Conclusion
- Chapter 14. Application Example: U–PLS/RBL
- 14.1. Nontrilinear data of Type 2
- 14.2. How to solve this problem?
- 14.3. What algorithm should be chosen?
- 14.4. An experimental literature example
- 14.5. How was the whole experiment designed?
- 14.6. Preparing the calibration and validation sets
- 14.7. How to choose the wavelength ranges?
- 14.8. U-PLS processing using MVC2: validation samples
- 14.9. Analysis of samples with potential interferences
- 14.10. Analysis of real samples
- 14.11. Conclusion
- Chapter 15. Solutions to Homework
- Homework to Chapter 5
- Homework to Chapter 7
- Homework to Chapter 8
- Homework to Chapter 9
- 15.5. Conclusion
- Index
- Edition: 1
- Latest edition
- Published: April 8, 2014
- Language: English
AO
Alejandro Olivieri
Alejandro C. Olivieri was born in Rosario, Argentina, on July 28, 1958. He is a member of the Institute of Chemistry of Rosario, and professor of the Department of Analytical Chemistry of the National University of Rosario. Degree in Industrial Chemistry (Catholic Faculty of Chemistry and Engineering, 1982), Doctor (Faculty of Biochemical and Pharmaceutical Sciences, University of Rosario, 1986), fellow of the National Council for Scientific and Technical Research (CONICET). About 250 publications, books and book chapters. John Simon Guggenheim Memorial Foundation Fellow (2001-2002). Konex Platinum Award (Konex Foundation, Argentina, 2013) for his contributions to analytical chemistry. Current interest: chemometrics in analytical chemistry.
Affiliations and expertise
Universidad Nacional de Rosario, Instituto de Química Rosario (CONICET-UNR), Facultad de Ciencias Bioquímicas y Farmacéuticas,ArgentinaGE
Graciela M. Escandar
Graciela M. Escandar was born in Rosario city, Argentina. She obtained her B.Sc. in Biochemistry in 1983 and her Ph.D. in 1992, both from the Faculty of Biochemical and Pharmaceutical Sciences, University of Rosario, where she was full professor in the Department of Analytical Chemistry, and fellow of the National Research Council of Argentina (CONICET). She has published about 130 scientific papers in well-known international journals, reviews, books and book chapters, and supervised seven Ph.D. Theses. Her research focuses on the development of new green methodologies based on molecular luminescence in organized media and solid-phase for the determination of analytes of environmental and pharmaceutical interest.
Affiliations and expertise
Universidad Nacional de Rosario, nstituto de Química Rosario (CONICET-UNR), Facultad de Ciencias Bioquímicas y Farmacéuticas, ArgentinaRead Practical Three-Way Calibration on ScienceDirect