Computer-Enhanced Analytical Spectroscopy

I: Optimization and Exploratory Data Analysis.- 1 Development of an AI-Based Optimization System for Tandem Mass Spectrometry.- 1.1. Introduction.- 1.2. Problem Statement.- 1.3. Proposed Method of Solution.- 1.4. Evolution of TQMSTUNE.- 1.4.1. TQMSTUNE Version 1.- 1.4.2. TQMSTUNE Version 2.- 1.4.3. TQMSTUNE Version 3.- 1.5. Knowledge Representation in the TQMS Domain.- 1.5.1. Representation of InstrumentConstruction Knowledge.- 1.5.2. Representation of Procedural Tuning Knowledge.- 1.5.3. Representation of Output Evaluation Procedures.- 1.5.4. Representation of Interfacing Knowledge.- 1.6. Results.- 1.7. Conclusion.- References.- 2 Curve Fitting and Fourier Self-Deconvolution for the Quantitative Representation of Complex Spectra.- 2.1. Introduction.- 2.1.1. Quantitative Analysis of Highly Overlapped Spectra.- 2.1.2. Derivative Spectrometry.- 2.1.3. Fourier Self-Deconvolution.- 2.1.4. Curve-Fitting Unresolved Peaks.- 2.2. Synthetic Spectra.- 2.2.1. Isolated Bands.- 2.2.2. Band Multiplets.- 2.3. Application to Coal Spectrometry.- 2.4. Conclusion.- References.- 3 Evolutionary Factor Analysis in Analytical Spectroscopy.- 3.1. Introduction.- 3.2. Gemperline Method.- 3.3. Vandeginste, Derks, and Kateman (VDK) Method.- 3.4. Gampp, Maeder, Meyer, and Zuberbuhler (GMMZ) Method.- 3.5. Application of EFA to Circular Dichroism (CD) Spectra.- 3.6. Conclusions.- References.- 4 Numerical Extraction of Components from Mixture Spectra by Multivariate Data Analysis.- 4.1. Introduction.- 4.2. Factor Analysis.- 4.2.1. Geometrical Description.- 4.2.2. Mathematical Rationalization.- 4.2.3. Examples.- 4.3. Discriminant Analysis.- 4.3.1. Geometrical Description.- 4.3.2. Mathematical Rationalization.- 4.3.3. Examples.- 4.4. Graphical Rotation.- 4.4.1. Geometrical Description.- 4.4.2. Mathematical Rationalization.- 4.5. The Variance Diagram.- 4.5.1. Geometrical Description.- 4.5.2. Mathematical Rationalization.- 4.5.3. Examples.- 4.6. Calculation of Fractional Concentrations.- 4.6.1. Geometrical Description.- 4.6.2. Mathematical Rationalization.- 4.6.3. Examples.- References.- 5 Simultaneous Multivariate Analysis of Multiple Data Matrices.- 5.1. Introduction.- 5.2. Three-Mode Principal Components Analysis.- 5.2.1. Concepts.- 5.2.2. Algorithms.- 5.2.2.1. Tucker 1.- 5.2.2.2. Tucker 2.- 5.2.2.3. Tucker 3 ("Alternating Least Squares").- 5.2.3. Examples.- 5.2.3.1. GC-MS of Crude Oils.- 5.2.3.2. Predicting Retention in HPLC.- 5.3. Generalized Procrustes Analysis.- 5.3.1. Concepts.- 5.3.2. Algorithms.- 5.3.3. Example: Comparison of Classifications of Staphylococcus Strains Using Binary (+ / -) Biochemical Tests or Fatty Acid Data.- References.- 6 Multivariate Calibration: Quantification of Harmonies and Disharmonies in Analytical Data.- 6.1. Introduction.- 6.1.1. Calibrating an Analytical Instrument Is Like Tuning a Musical Instrument.- 6.1.2. Quantitative Chemometrics.- 6.1.3. Notation.- 6.2. Interferences.- 6.2.1. Chemical Interference in the Samples.- 6.2.2. Physical Interference in the Samples.- 6.2.3. Experimental Interferences from the Measurement Itself.- 6.2.4. Determining Concentrations in the Presence of Interferences.- 6.2.5. The Danger of Outliers.- 6.3. Different Groups of Approaches.- 6.3.1. Linear-Nonlinear.- 6.3.2. Selection-Weighting.- 6.3.3. Different Types of Assumptions.- 6.3.3.1. Causal "Classical" Modeling.- 6.3.3.2. Traditional Statistical "Inverse" Calibration.- 6.3.3.3. Calibration on Latent Variables.- 6.4. Multivariate Calibration by Bilinear "Soft Modeling".- 6.4.1. Introduction.- 6.4.2. Principal Component Regression.- 6.4.3. Partial Least-Squares Regression.- 6.4.4. Outlier Detection (Error Warnings).- 6.5. Example from NIR Diffuse Reflectance Spectroscopy.- 6.6. Conclusion.- References.- II: Spectral Interpretation and Library Search.- 7 Automated Spectra Interpretation and Library Search Systems.- 7.1. Introduction.- 7.2. The Mathematical Model.- 7.3. The General Algorithm.- 7.3.1. Overview.- 7.3.2. Structure Inference.- 7