Save up to 30% on Elsevier print and eBooks with free shipping. No promo code needed.
Save up to 30% on print and eBooks.
Practical Three-Way Calibration
1st Edition - March 15, 2014
Authors: Alejandro Olivieri, Graciela M. Escandar
Language: English
Hardback ISBN:9780124104082
9 7 8 - 0 - 1 2 - 4 1 0 4 0 8 - 2
eBook ISBN:9780124104549
9 7 8 - 0 - 1 2 - 4 1 0 4 5 4 - 9
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
Purchase options
LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code is needed.
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
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
No. of pages: 330
Language: English
Edition: 1
Published: March 15, 2014
Imprint: Elsevier
Hardback ISBN: 9780124104082
eBook ISBN: 9780124104549
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,Argentina
GE
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, Argentina
Read Practical Three-Way Calibration on ScienceDirect