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Chemometrics in Spectroscopy
Revised Second Edition
- 2nd Edition - September 30, 2021
- Authors: Howard Mark, Jerry Workman Jr.
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 6 4 - 1
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 7 0 - 2
Chemometrics in Spectroscopy, Revised Second Edition provides the reader with the methodology crucial to apply chemometrics to real world data. The book allows scientists using spe… Read more
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Request a sales quoteChemometrics in Spectroscopy, Revised Second Edition provides the reader with the methodology crucial to apply chemometrics to real world data. The book allows scientists using spectroscopic instruments to find explanations and solutions to their problems when they are confronted with unexpected and unexplained results. Unlike other books on these topics, it explains the root causes of the phenomena that lead to these results. While books on NIR spectroscopy sometimes cover basic chemometrics, they do not mention many of the advanced topics this book discusses.
This revised second edition has been expanded with 50% more content on advances in the field that have occurred in the last 10 years, including calibration transfer, units of measure in spectroscopy, principal components, clinical data reporting, classical least squares, regression models, spectral transfer, and more.
- Written in the column format of the authors’ online magazine
- Presents topical and important chapters for those involved in analysis work, both research and routine
- Focuses on practical issues in the implementation of chemometrics for NIR Spectroscopy
- Includes a companion website with 350 additional color figures that illustrate CLS concepts
Academic and industrial chemists who use NIR spectroscopy in their work; scientists in virtually every field of chemical endeavour, as well as in medicine, biochemistry, food analysis, clothing and related industries, petrochemicals, and more
1. A New Beginning
Section 1 Elementary Matrix Algebra
2. Elementary Matrix Algebra, Part 1: Primitive operations: Addition, Subtraction, Multiplication, Division, Inverse, Transpose
3. Elementary Matrix Algebra, Part 2: Elementary Operations,Inverse of a Matrix
Section 2 Matrix Algebra and Multiple Linear Regression
4. Matrix Algebra and Multiple Linear Regression: Part 1 Quasi-Algebraic Operations, Multiple Linear Regression, The Least Squares Method
5. Matrix Algebra and Multiple Linear Regression: Part 2 When There Are More Equations Than Unknowns, The Power of Matrix Mathematics
6. Matrix Algebra and Multiple Linear Regression: Part 3 The Concept of Determinants
7. Matrix Algebra and Multiple Linear Regression: Part 4 Concluding Remarks, and A Word of Caution
Section 3 Experimental Designs
8. Experimental Designs, Part 1: Introduction
9. Experimental Designs, Part 2: One-way ANOVA
10. Experimental Designs, Part 3: Two-factor Designs
11. Experimental Designs, Part 4: Varying Parameters to Expand the Design
12. Experimental Designs Part 5: One-at-a-time Designs
13. Experimental Designs, Part 6: Sequential designs
14. Experimental Designs, Part 7: β, the Power of a Test
15. Experimental Designs, Part 8: β, the Power of a Test (continued)
16. Experimental Designs, Part 9: Sequential Designs (concluded)
Section 4 Analytic Geometry
17. Analytic Geometry: Part 1: The Basics in Two and Three Dimensions
18. Analytic Geometry: Part 2: Geometric Representation of Vectors and Algebraic Operation
19. Analytic Geometry: Part 3: Reducing Dimensionality
20. Analytic Geometry: Part 4: The Geometry of Vectors and Matrices
Section 5 Regression Techniques
21. Calculating the Solution for Regression Techniques: Part 1: Multivariate Regression Made Simple
22. Calculating the Solution for Regression Techniques: Part 2: Principal Component(s) Regression Made Simple
23. Calculating the Solution for Regression Techniques: Part 3: Partial Least Squares Made Simple
24. Calculating the Solution for Regression Techniques: Part 4: Singular Value Decomposition
25. Interlude: Looking Behind and Ahead
26. A Simple Question
27. Challenges: Unsolved Problems in Chemometrics
Section 6 Linearity in Calibration
28. Linearity in Calibration, Act I: A Thought Experiment Carried Out by Computer Simulation
29. Linearity in Calibration, Act II Scene I: A Firestorm Erupts and A Theoretical Explanation of Linearity
30. Linearity in Calibration, Act II Scene II: Details of Reader Responses
31. Linearity in Calibration, Act II Scene III: Summary of Reader Responses, and Our Commentary on Those Responses
32. Linearity in Calibration, Act II Scene IV: A Summary of Findings and Recommendations for Future Explorations
33. Linearity in Calibration, Act II Scene V: Effect of (Non) Linearity on PLS Algorithm
Section 7 Collaborative Laboratory Studies
34. Collaborative Laboratory Studies: Part 1 - A Blueprint
35. Collaborative Laboratory Studies: Part 2 - Using ANOVA
36. Collaborative Laboratory Studies: Part 3 - Testing for Systematic Error
37. Collaborative Laboratory Studies: Part 4 - Ranking Test
38. Collaborative Laboratory Studies: Part 5 - Efficient Comparison of Two Methods
39. Collaborative Laboratory Studies: Part 6 - MathCad Worksheet Text
Section 8 Analysis of Noise
40. Is Noise Brought by the Stork? Analysis of Noise - Part 1—A Listing of the Sources of Spectroscopic Noise and Their Characteristics
41. Analysis of Noise - Part 2—The analysis of the effect of ‘constant’ detector noise on a transmission measurement
42. Analysis of Noise - Part 3—The Analysis of the Effect of ‘constant’ Detector Noise on the Absorbance, the Relative Absorbance (ΔA/A) and the Optimum Absorbance Value
43. Analysis of Noise - Part 4—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise When the Noise Is Not Negligible Compared to the Signal
44. Analysis of Noise - Part 5—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise When the Reference Energy Approaches Zero
45. Analysis of Noise - Part 6—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise: Comparing the Effect of Noise in the Sample Channel Versus Noise in the Reference Channel
46. Analysis of Noise - Part 7—The Analysis of ‘constant’ Detector Noise on the Kubelka-Munk Function
47. Analysis of Noise - Part 8—Effect of Noise on the Computed Transmittance, Analysis of Uniformly Distributed Noise for Transmittance and Absorbance Values
48. Analysis of Noise - Part 9—Analysis of Poisson-Distributed Noise, Effects on Transmittance and Absorbance Values
49. Analysis of Noise - Part 10—Analysis of Poisson-Distributed Noise, Effects on Relative Absorbance
50. Analysis of Noise - Part 11—Analysis of Poisson-Distributed Noise, When the Noise Is Not Small Compared to the Reference Signal
51. Analysis of Noise - Part 12—Analysis of Poisson-Distributed Noise: Computation of the Transmittance Noise
52. Analysis of Noise - Part 13—Analysis of Poisson-Distributed Noise: Computation of the Absorbance Noise
53. Analysis of Noise - Part 14—Analysis of Noise Proportional to the Signal, Small-Noise Case
54. Analysis of Noise - Part 15—Analysis of Noise Proportional to the Signal, Large-Noise Case
Section 9 - Derivatives
55. Derivatives in Spectroscopy, Part 1 - The Behavior of the Theoretical Derivative
56. Derivatives in Spectroscopy, Part 2 - The "True" Derivative
57. Derivatives in Spectroscopy, Part 3 - Computing the Derivative (the Savitzky-Golay method)
58. Derivatives in Spectroscopy, Part 4 - Calibrating with Derivatives
59. Corrections and Discussion Regarding Derivatives
Section 10 - Goodness of Fit Statistics
60. Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 1 - Introduction
61. Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 2 - The Correlation Coefficient
62. Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 3 - Computing Confidence Limits for the Correlation Coefficient
63. Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 4 - Confidence Limits for Slope and Intercept
Section 11 - More About Linearity in Calibration
64. Linearity in Calibration, Act III Scene I: Importance of (non)Linearity
65. Linearity in Calibration, Act III Scene II: A Discussion of the Durbin-Watson Statistic, a Step in the Right Direction
66. Linearity in Calibration, Act III Scene III: Other Tests for non-Linearity
67. Linearity in Calibration, Act III Scene IV: How Test For non-Linearity
68. Linearity in Calibration: Act III Scene V: Quantifying Non-linearity
69. Linearity in Calibration, Act III Scene VI: Quantifying Non-linearity, Part II, Calculus-Based Approach, and a News Flash
Section 12 - Connecting Chemometrics to Statistics
70. Connecting Chemometrics to Statistics: Part 1--The Chemometrics Side
71. Connecting Chemometrics to Statistics: Part 2--the Statistics Side
Section 13 - Limitations in Analytical Accuracy
72. Limitations in Analytical Accuracy: Part 1 - Horwitz's Trumpet
73. Limitations in Analytical Accuracy: Part 2 - Theories to Describe the Limits in Analytical Accuracy
74. Limitations in Analytical Accuracy: Part 3 - Comparing Test Results for Analytical Uncertainty
75. The Statistics of Spectral Searches
76. The Chemometrics of Imaging Spectroscopy
77. Corrections to Analysis of Noise - Part 1: Alternate Analysis of Transmittance Noise in the ‘large noise’ Regime
78. Corrections to Analysis of Noise - Part 2: Alternate Analysis of Absorbance noise in the ‘large noise’ Regime
79. What can NIR predict?
Section 14 - Derivations of Principal Components
80. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 1, Introduction and Review
81. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 2, our first attempt
82. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 3, multivariate curve fitting
83. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 4, the Lagrange Multiplier
84. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 5, Solving the Equations with Determinants
85. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part 6: Solving the Equations Without Determinants
86. The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Coda: Applying Constrained Univariate Calculations
Section 15 - Clinical Data Reporting
87. Statistics and Chemometrics for Clinical Data Reporting - Part 1: Fundamentals
88. Statistics and Chemometrics for Clinical Data Reporting - Part 2: Using Excel for Computations
89. Statistics and Chemometrics for Clinical Data Reporting - Part 3: Using Excel for Data Plotting
Section 16 - Classical Least Squares (CLS)
90. Classical Least Squares, Part 1: Mathematical theory
91. Classical Least Squares, Part 2: Mathematical Theory Continued
92. Classical Least Squares, Part 3: Spectroscopic Theory
93. Classical Least Squares, Part 4: Spectroscopic Theory Continued
94. Classical Least Squares, Part 5: Experimental Results
95. Classical Least Squares, Part 6: Spectral Results
96. Classical Least Squares, Part 7: Spectral Reconstruction of Mixtures
97. Classical Least Squares, Part 8: Comparison of CLS Values with Known Values
98. Classical Least Squares, Part 9: Spectral Results from a Second Laboratory
99. Classical Least Squares, Part 10: Numerical Results from the Second Laboratory
100. Classical Least Squares, Part 11: Comparison of Results from the Two Laboratories (Continued)
Section 17 - Transfer of Calibrations
101. Transfer of Calibrations - Part 1: An Overview
102. Calibration Transfer - Part 2: The Instrumentation Aspects
103. Calibration Transfer - Part 3: The Mathematical Aspects
104. Calibration Transfer - Part 4: Measuring the Agreement Between Instruments Following Calibration Transfer
105. Calibration Transfer - Part 5: The Mathematics of Wavelength Standards Used for Spectroscopy
106. Calibration Transfer - Part 6: The Mathematics of Photometric Standards Used for Spectroscopy
Section 18 - The Importance of Units of Measure
107. Units of Measure in Spectroscopy, Part 1: ... and Then The Light Dawned
108. Units of Measure in Spectroscopy, Part 2: It's the VOLUME, Folks!
109. Units of Measure in Spectroscopy, Part 3: What Does it all Mean
110. Units of Measure in Spectroscopy, Part IV: Summary of our Findings
111. Units of Measure in Spectroscopy, Part V: The "Mythbusters" and Spectral Reconstruction
Section 19 - The Best Calibration Model
112. Choosing the Best Calibration Model
113. Optimizing the Regression Model: The Challenge of Intercept/Bias and Slope “Correction"
Section 20 - Statistics
114. Statistics, Part 1: First Foundation: Probability Theory
115. STATISTICS, Part 2: Second Foundation: Analysis of Variance
116. STATISTICS, Part 3: Third Foundation: Least Squares
117. How to Select the Appropriate Degrees of Freedom for Multivariate Calibration
118. Bias and Slope Correction
Section 21 - Outliers
119. Outliers—Part 1: What Are Outliers?
120. Outliers—Part 2: Pitfalls in Detecting Outliers
121. Outliers—Part 3: Dealing With Outliers
Section 22 - Spectral Transfer: Making Instruments Agree
122. Calibration Transfer Chemometrics, Part 1: Review of the Subject
123. Calibration Transfer Chemometrics, Part 2: Review of the Subject
Section 23 - Applying Standard Reference Materials
124. Using Reference Materials, Part 1: Standards for Aligning the X-Axis
125. Using Reference Materials, Part 2: Aligning the Y-Axis
Section 24 - More About CLS
126. More About CLS, Part 1: Expanding the Concept
127. More About CLS, Part 2: Spectral Results & CLS (not requiring constituent values)
128. More About CLS, Part 3: Expanding the Analysis to Include Concentration Information (PCR & PLS)
- No. of pages: 1092
- Language: English
- Edition: 2
- Published: September 30, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323911641
- eBook ISBN: 9780323911702
HM
Howard Mark
Howard Mark is President of Mark Electronics, Suffern, New York. He was previously affiliated as a Senior Scientist at Technicon Instrument Corp. in Tarrytown, New York. He holds a B.S. degree from City College of New York, an M.A. from City University of New York, and a PhD from New York University. His professional interests include instrument development, especially for spectroscopy; statistical and chemometric data analysis; and Custom software development, especially for implementation of data analysis algorithms. He received the 2003 Eastern Analytical Symposium Award for Achievement in Near Infrared Spectroscopy. He holds 6 U.S patents and has published 2 books and numerous book chapters. He has acted as Associate editor for the Handbook of Vibrational Spectroscopy, Wiley (2001). He has served as Past president of Council for Near-Infrared Spectroscopy (CNIRS), Treasurer of the New York section of the Society for Applied Spectroscopy, and as Past Chair of the New York section of the Society for Applied Spectroscopy. In addition he acts as Contributing editor and member of the Editorial Advisory Board of Spectroscopy. He has published over 150 peer-reviewed papers dealing with design and development of scientific instrumentation, new concepts in computerized instrumentation and data analysis.
Affiliations and expertise
Mark Electronics, Suffern, NY, USAJW
Jerry Workman Jr.
Jerome (Jerry) J. Workman, Jr. is Executive Vice President of Research & Engineering for Unity Scientific and Process Sensors Corporation; Certified Core Adjunct Professor at National University, CA; and Principal at Biotechnology Business Associates. He was formerly Vice President of Technology Research for Masimo Corporation; Director of Research, Technology & Applications Development for Molecular Spectroscopy & Microanalysis for ThermoFisher Scientific; Chief Technical Officer and Vice President of Research & Engineering at Argose Inc.; Senior Research Fellow at Kimberly-Clark Analytical Science & Technology; and Principal Scientist at Perkin-Elmer.
Dr. Workman has played a major role in defining and developing over 20 scientific instrument advancements with novel software improvements for successful commercial use for start-ups to major corporations. He has more than 55 U.S. and international patent applications (since 1998); 20 U.S. and international patents issued, and multiple trade secrets. He has a total of 475 technical publications; and 18 reference books on a broad range of spectroscopy, chemometrics, and data processing techniques. He has received awards from the Eastern Analytical Symposium, ASTM International, Coblentz Society; as well as multiple fellowships, technical, and government appointments. He has taught annual courses in NIR spectroscopy, chemometrics, and statistics for the Association of Official Analytical Chemists, the American Chemical Society, the Instrument Society of America, and the Federation of Analytical Chemists and Spectroscopy Societies, and at several universities and corporations.
He holds a BA degree cum laude in natural sciences, and an MA in biological sciences from Saint Mary's University of Minnesota, and a PhD degree with high commendation in biological chemistry from Columbia Pacific University. He is a graduate of the Columbia Senior Executive Program and also holds Columbia Business School Certificates in Executive Development (CIED) and in Business Excellence (CIBE). He also holds a Certificate in Strategy and Innovation from the M.I.T. Sloan School. He is listed in Who's Who in the World, Who's Who in America, and Who's Who in Science and Engineering.
Affiliations and expertise
Unity Scientific and Process Sensors Corporation, Milford, MA; National University, San Diego, CA; and Biotechnology Business Associates, Milford, MA, USARead Chemometrics in Spectroscopy on ScienceDirect