Chemometrics in Spectroscopy book cover

Chemometrics in Spectroscopy

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.

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.

Hardbound, 558 Pages

Published: August 2007

Imprint: Academic Press

ISBN: 978-0-12-374024-3

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

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