Chemometrics in Spectroscopy

By

  • Howard Mark, Mark Electronics, Suffern, New York, U.S.A.
  • Jerome Workman, Thermo Electron Corporation, Molecular Spectroscopy and Microanalysis, Madison, U.S.A.

Chemometrics in Spectroscopy builds upon the statistical information covered in other books written by these leading authors in the field by providing a broader range of mathematics and progressing into the fundamentals of multivariate and experimental data analysis. Subjects covered in this work include: matrix algebra, analytic geometry, experimental design, calibration regression, linearity, design of collaborative laboratory studies, comparing analytical methods, noise analysis, use of derivatives, analytical accuracy, analysis of variance, and much more are all part of this chemometrics compendium. Developed in the form of a tutorial offering a basic hands-on approach to chemometric and statistical analysis for analytical scientists, experimentalists, and spectroscopists. Without using complicated mathematics, Chemometrics in Spectroscopy demonstrates the basic principles underlying the use of common experimental, chemometric, and statistical tools. Emphasis has been given to problem-solving applications and the proper use and interpretation of data used for scientific research.
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Audience

For graduate students and above and for the practicing analyst or theoretician. Chemometricians, bioinformaticians, econometricians, psychometricians, statisticians, scientists, spectroscopists, analysts, clinicians, researchers, computer scientists, information specialists, and students in the natural sciences will all find the short descriptive chapters both helpful and full of valuable insight for mathematical and philosophical methods and approaches common to all these fields.

 

Book information

  • Published: August 2007
  • Imprint: ACADEMIC PRESS
  • ISBN: 978-0-12-374024-3


Table of Contents

1. A New Beginning
2. Elementary Matrix Algebra, Part 1
3. Elementary Matrix Algebra, Part 2
4. Matrix Algebra and Multiple Linear Regression, Part 1
5. Matrix Algebra and Multiple Linear Regression, Part 2
6. Matrix Algebra and Multiple Linear Regression, Part 3
7. Matrix Algebra and Multiple Linear Regression: Conclusion
8. Experimental Designs - Part 1
9. Experimental Designs - Part 2
10. Experimental Designs - Part 3
11. Analytic Geometry, Part I
12. Analytic Geometry, Part II
13. Analytic Geometry, Part III
14. Analytic Geometry, Part IV
15. Experimental Designs Part IV - Varying Parameters to Expand the Design
16. Experimental Designs Part V - One-at-a-time Designs
17. Experimental Designs Part VI - Sequential Designs
18. Experimental Designs Part VII, The Power of a Test
19. Experimental Designs Part VIII, The Power of a Test (Continued)
20. Experimental Designs Part IX - Sequential Designs Concluded
21. Calculating the Solution for Regression Techniques - Part I: Multivariate Regression Made Simple
22. Calculating the Solution for Regression Techniques - Part II: Principal Component(s) Regression Made Simple
23. Calculating the Solution for Regression Techniques - Part III: Partial Least Squares Regression Made Simple
24. Looking Ahead and Behind
25. A Simple Question
26. Calculating the Solution for Regression Techniques - Part IV: Singular Value Decomposition
27. Linearity in Calibration
28. Challenges: Unsolved Problems in Chemometrics
29. Linearity in Calibration - Act II Scene I
30. Linearity in Calibration - Act II Scene II
31. Linearity in Calibration - Act II Scene III
32. Linearity in Calibration - Act II Scene IV
33. Linearity in Calibration - Act II Scene V
34. A Blueprint for Collaborative Laboratory Studies
35. Using ANOVA for Collaborative Laboratory Studies, Part Two
36. Testing for Systematic Error in Collaborative Laboratory Studies, Part Three
37. Ranking Test for Collaborative Studies, Part Four
38. Efficient Comparison of Two Methods for Collaborative Laboratory Studies
39. More About Collaborative Laboratory Studies, a Brief Recap and Additional Resources, Part Five
40. Is Noise Brought by the Stork?: Analysis of Noise, Part 1
41. Analysis of Noise, Part 2
42. Analysis of Noise, Part 3
43. Analysis of Noise, Part 4
44. Analysis of Noise, Part 5
45. Analysis of Noise, Part 6
46. Analysis of Noise, Part 7
47. Analysis of Noise, Part 8
48. Analysis of Noise, Part 9
49. Analysis of Noise, Part 10
50. Analysis of Noise, Part 11
51. Analysis of Noise, Part 12
52. Analysis of Noise, Part 13
53. Analysis of Noise, Part 14
54. Derivatives in Spectroscopy, Part 1: The Behaviour of Derivatives
55. Derivatives in Spectroscopy, Part 2: The True Derivative
56. Derivatives in Spectroscopy, Part 3: Computing the Derivative
57. Derivatives in Spectroscopy, Part 4: Calibrating with Derivatives
58. Comparison of Goodness of Fit Statistics, Part I
59. Comparison of Goodness of Fit Statistics, Part II
60. Comparison of Goodness of Fit Statistics, Part III
61. Comparison of Goodness of Fit Statistics, Part IV
62. Derivatives in Spectroscopy - Update
63. Linearity in Calibration, Act III, Scene I: The Importance of Non-Linearity
64. Linearity in Calibration, Act III, Scene II: The Durbin-Watson Statistic
65. Linearity in Calibration, Act III, Scene III: Other Tests for Non-Linearity
66. Linearity in Calibration, Act III Scene IV: How To Test For non-Linearity
67. Linearity in Calibration, Act III Scene V: Quantifying Non-linearity
68. Linearity in Calibration, Act III Scene VI: Quantifying Non-linearity, Part II, and a News Flash
69. Connecting Chemometrics to Statistics - Part 1, the Chemometrics Side
70. Connecting Chemometrics to Statistics - Part 2, the Statistics Side
71. Limitations in Analytical Accuracy – Part 1
72. Limitations in Analytical Accuracy – Part 2
73. Limitations in Analytical Accuracy – Part 3
74. The Statistics of Spectral Searches
75. The Chemometrics of Imaging Spectroscopy