Research Catalog

Title

Mathematical analysis of spectral orthogonality / John H. Kalivas, Patrick M. Lang.

Author

Kalivas, John H., 1956-

Publication

New York : M. Dekker, ©1994.

Items in the Library & Off-site

Details

Additional Authors

Lang, Patrick M.

Description

xv, 324 pages : illustrations; 24 cm.

Summary

This self-contained resource offers an integrated treatment of multivariate approximation methods used in quantitative spectral analysis - showing how to assess the degree of multicollinearity in a set of spectra and introducing techniques that yield accurate approximations even in the presence of poor spectral orthogonality. Permitting precise quantitative predictions of chemical or physical variables from spectroscopic data sets, Mathematical Analysis of Spectral Orthogonality covers in detail the K- and P-matrix forms of Beer's law...presents a new geometric approximation methodology that includes, as special cases, the methods of least squares, continuum regression, partial least squares, and principal components...demonstrates the utility of methods presented by applying them to real spectroscopic data...furnishes appendixes that address the concepts of linear algebra and multivariate statistics...clarifies the sometimes contradictory nomenclature found in spectral chemical analysis...and much more. With carefully selected citations to current literature, illustrative examples, and numerous figures to enhance learning, Mathematical Analysis of Spectral Orthogonality is a practical day-to-day reference for spectroscopists; analytical, environmental, food, pharmaceutical and forensic chemists and biochemists; applied mathematicians; chemometricians; biologists geologists; and graduate-level students in these disciplines.

Series Statement

Practical spectroscopy series ; v. 17

Uniform Title

Practical spectroscopy ; v. 17.

Subject

• Spectrum analysis > Statistical methods
• Multicollinearity
• Analysis
• Orthogonalität
• Spektroskopie
• Statistik

Bibliography (note)

• Includes bibliographical references and indexes.

Contents

Ch. 1. Spectral Orthogonality. 1.1. Fundamental Models. 1.2. Spectral Orthogonality and Multicollinearity. 1.3. Spectral Orthogonality and Wavelength Selection. 1.4. Multicollinearity Sources. 1.5. Treatment of Multicollinearity -- Ch. 2. Assessment Methodologies. 2.1. K-matrix Analysis. 2.2. P-matrix Analysis. 2.3. Comments -- Ch. 3. Approximation Methodologies. 3.1. K-matrix Analysis. 3.2. P-matrix Analysis. 3.3. Comments -- Ch. 4. K-matrix Analysis Applications. 4.1. Spectral Simulations. 4.2. Spectroscopic Applications -- Ch. 5. P-matrix Analysis Applications. 5.1. Spectral Simulations. 5.2. Spectroscopic Applications. 5.3. Future Direction -- Appendix A: Linear Algebra. A.1. Vector Spaces. A.2. Matrices -- Appendix B: Multivariate Statistics. B.1. Terminology, Concepts, and Formulas -- Appendix C: Additional Applications. C.1. Nonspectroscopic Methods.

ISBN

• 082479155X
• 9780824791551

LCCN

93032079

OCLC

• ocm28709522
• 28709522
• SCSB-2032607

Owning Institutions

Princeton University Library