Quantitative Spectroscopy: Theory and Practice

Chapter 1 Fundamentals of Quantitative Molecular Absorption SpectroscopyI. Terms and DefinitionsII. The Properties of LightIII. The Electromagnetic SpectrumIV. Beer's LawV. Variables Affecting the Absorbance and Absorptivity A. The Impact of Temperature on Absorbance B. The Impact of Electronic Structure on Absorptivity C. SummaryVI. Gas Phase Quantitative Spectroscopic AnalysisBibliographyAppendix: 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 ReferencesChapter 2 Single Analyte AnalysisI. Precision and AccuracyII. Calibration and Prediction with Beer's LawIII. Plotting and Analyzing Lines A. Linear Regression B. Statistics for Determining Calibration Quality and RobustnessIII. Methods for Making Standards and Measuring Spectra A. External Standards B. Internal Standards C. An Experimental Protocol for Single Analyte AnalysesIV. Methods for Making Standards and Measuring Spectra A. External Standards B. Internal Standards C. An Experimental Protocol for Single Analyte DeterminationsV. Measuring Absorbances Properly Peak Heights vs. Peak Areas B. Dealing with Overlapped Peaks C. Correcting for InterferentsVI. Avoiding Experimental Errors A. Concentration Outliers B. Absorbance Outliers C. Experimental Errors to Avoid D. Instrumental Deviations from Beer's Law 1. The I0 Problem 2. Completely Resolving Spectral FeaturesReferences and BibliographyAppendix of Experimental Details Chapter 3 Multiple Components I: Least Squares MethodsI. A Data Set for Multi-Component AnalysisII. Independent Determination of Multiple ComponentsII. 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: GeneralitiesIV. The Classical Least Squares (K Matrix) MethodV. 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 ILSReferences & BibliographyChapter IV Multiple Components II: Chemometric Methods & Factor AnalysisI. Introduction A. The Advantages and Disadvantages of Factor Analysis B. Factor Analysis Overview C. A Data Set for Factor AnalysisII. Factor Analysis Algorithms A. Principal Components Regression (PCR) B. Partial Least Squares (PLS) C. Algorithm Comparison and DiscussionIII. Standards Preparation & Training Set DesignIV. 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 RegionsV. Cross Validation: Testing Model Quality A. Spotting Outliers B. Concentration Outliers B. Spectral OutliersVI. Calibration: Choosing the Right Number of Factors A. Actual vs. Predicted Concentration Plots B. Reconstructed Spectra C. Factor Loadings Plots D. The Press PlotVII. ValidationVIII. PredictionReferences & BibliographyChapter 5 Implementing, Maintaining, and Fixing CalibrationsI. Implementing CalibrationsII. Maintaining CalibrationsIII. Fixing Problem CalibrationsBibliographyGlossary