Quantitative Spectroscopy: Theory and Practice

The determination of the concentrations of molecules in samples has long been an important application of spectroscopy. In the last 20 years advances in algorithms, computers, instruments, and software have led to a growing interest in this field. These developments mean samples and analytes that were once considered intractable are increasingly yielding usable calibrations. The purpose of this book is to give readers, without an advanced math background, a thorough grounding in the theory and practice of modern quantitative spectroscopic analysis. The author has placed great emphasis on providing the reader with everything they need to know to obtain a fundamental understanding of quantitative spectroscopy.

· Relevant theory is explained in an easy to understand, conversational style. · Actual spectroscopic data and calibrations are used throughout the book to show how real world calibrations are achieved. · The complexities of Factor Analysis (PCR/PLS) algorithms are explained in pictures and words, making them understandable for all. · Written from a spectroscopic rather than a mathematical point of view. · Relevant theory is interspersed with practical discussions in order to make difficult concepts easier to comprehend · It is a comprehensive introduction for novices, and an excellent reference for experts. · Topics on spectroscopy are included to emphasize its importance in quantitative spectroscopy

For industrial technicians, analysts, chemists, engineers and managers who use spectroscopy to quantify the concentrations of molecules in samples. For pharmaceutical, rubber, petroleum, polymer, and chemical companies.

Chapter 1 Fundamentals of Quantitative Molecular Absorption Spectroscopy I. Terms and Definitions II. The Properties of Light III. The Electromagnetic Spectrum IV. Beer's Law V. Variables Affecting the Absorbance and Absorptivity A. The Impact of Temperature on Absorbance B. The Impact of Electronic Structure on Absorptivity C. Summary VI. Gas Phase Quantitative Spectroscopic Analysis Bibliography Appendix: The Quantum Mechanics of Light Absorption A. Wavefunctions and Schrödinger's Equation B. The Particle in a Box C. Transition Probabilities for the Particle in a Box References

Chapter 2 Single Analyte Analysis I. Precision and Accuracy II. Calibration and Prediction with Beer's Law III. Plotting and Analyzing Lines A. Linear Regression B. Statistics for Determining Calibration Quality and Robustness III. Methods for Making Standards and Measuring Spectra A. External Standards B. Internal Standards C. An Experimental Protocol for Single Analyte Analyses IV. Methods for Making Standards and Measuring Spectra A. External Standards B. Internal Standards C. An Experimental Protocol for Single Analyte Determinations V. Measuring Absorbances Properly Peak Heights vs. Peak Areas B. Dealing with Overlapped Peaks C. Correcting for Interferents VI. Avoiding Experimental Errors A. Concentration Outliers B. Absorbance Outliers C. Experimental Errors to Avoid D. Instrumental Deviations from Beer's Law

References and Bibliography Appendix of Experimental Details

Chapter 3 Multiple Components I: Least Squares Methods I. A Data Set for Multi-Component Analysis II. Independent Determination of Multiple Components II. Simultaneous Determination of Multiple Components A. The Additivity of Beer's Law B. Introduction to Matrix Algebra C. The Matrix Form of Beer's Law D. The Benefits of Using Many Absorbances E. Multi-Component Calibrations: Generalities IV. The Classical Least Squares (K Matrix) Method V. The Inverse Least Squares (P Matrix) Method A. The Advantages of the Inverse Beer's Law Formulation B. P Matrix Theory C. An Example ILS Calibration D. Validations and Prediction E. Advantages and Disadvantages of ILS References & Bibliography

Chapter IV Multiple Components II: Chemometric Methods & Factor Analysis I. Introduction A. The Advantages and Disadvantages of Factor Analysis B. Factor Analysis Overview C. A Data Set for Factor Analysis II. Factor Analysis Algorithms A. Principal Components Regression (PCR) B. Partial Least Squares (PLS) C. Algorithm Comparison and Discussion III. Standards Preparation & Training Set Design IV. Spectral Pre-Processing A. Mean Centering B. Spectral Derivatives C. Baseline Correction D. Smoothing E. Spectral Pre-processing: Summary and Guidance F. Choosing Proper Spectral Regions V. Cross Validation: Testing Model Quality A. Spotting Outliers B. Concentration Outliers B. Spectral Outliers VI. Calibration: Choosing the Right Number of Factors A. Actual vs. Predicted Concentration Plots B. Reconstructed Spectra C. Factor Loadings Plots D. The Press Plot VII. Validation VIII. Prediction References & Bibliography

Chapter 5 Implementing, Maintaining, and Fixing Calibrations I. Implementing Calibrations II. Maintaining Calibrations III. Fixing Problem Calibrations Bibliography

Glossary