Computer-Enhanced Analytical Spectroscopy

Thomas L. Isenhour

ISBN 10: 146845370X / ISBN 13: 9781468453706

Editore: Springer US, 2012

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Druck auf Anfrage Neuware - Printed after ordering - June 1986 brought together some of the world's leaders in computer enhanced analytical spectroscopy at Snowbird, Utah, for what the attendees decided to call 'The First Hidden Peak Symposium.' With the remarkable advances in both computer hardware and software, it is interesting to observe that, while many computational aspects of spectroscopic analysis have become routine, some of the more fundamental problems remain unsolved. The group that assembled included many of those who started trying to interpret chemical spectroscopy when computers were ponderous, slow, and not very accessible, as well as newcomers who never knew the day that spectrometers were delivered without attached computers. The synergism was excellent. Many new ideas, as well as this volume, resulted from interactions among the participants. The conclusion was that progress would be made on more fundamen tal problems now that hardware, software, and mathematics were coming together on a more sophisticated level. The feeling was that the level of sophistication is now adequate and that it is only a matter of time before automated spectral interpretation surpasses all but the most advanced human experts.

Codice articolo 9781468453706

Dati bibliografici

• Titolo: Computer-Enhanced Analytical Spectroscopy
• Editore: Springer US
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 146845370X
• ISBN 13: 9781468453706
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 254 L x 178 A x 16 P (millimetri) 254 millimetri di larghezza per 178 millimetri di altezza per 16 millimetri di profondità
• Peso: 554 grammi

Descrizione articolo

Riassunto

June 1986 brought together some of the world's leaders in computer­ enhanced analytical spectroscopy at Snowbird, Utah, for what the attendees decided to call "The First Hidden Peak Symposium." With the remarkable advances in both computer hardware and software, it is interesting to observe that, while many computational aspects of spectroscopic analysis have become routine, some of the more fundamental problems remain unsolved. The group that assembled included many of those who started trying to interpret chemical spectroscopy when computers were ponderous, slow, and not very accessible, as well as newcomers who never knew the day that spectrometers were delivered without attached computers. The synergism was excellent. Many new ideas, as well as this volume, resulted from interactions among the participants. The conclusion was that progress would be made on more fundamen­ tal problems now that hardware, software, and mathematics were coming together on a more sophisticated level. The feeling was that the level of sophistication is now adequate and that it is only a matter of time before automated spectral interpretation surpasses all but the most advanced human experts.

Contenuti

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.- 7.3.3. Consistency Check.- 7.3.4. Structure Assembly.- 7.3.5. Spectra Prediction and Comparison.- 7.3.6. Discussion.- 7.4. Library Search Systems.- 7.4.1. Overview.- 7.4.2. The Model.- 7.4.3. Spectral Feature Selection.- 7.4.4. Implementation.- 7.4.5. Composition of the Reference Data Base.- References.- 8 Carbon-13 Nuclear Magnetic Resonance Spectrum Simulation.- 8.1. Introduction.- 8.2. Methodology.- 8.2.1. Data Entry and Problem Definition.- 8.2.2. Molecular Mechanics Model Building.- 8.2.3. Molecular Structure Descriptor Generation.- 8.2.4. Development and Evaluation of Chemical Shift Models.- 8.2.5. Spectrum Simulation.- 8.3. Example Studies.- 8.3.1. Steroids.- 8.3.2. Substituted Cyclopentanes and Cyclopentanols.- 8.4. Conclusion.- References.- 9 The Evolution of an Automated IR Spectra Interpretation System.- 9.1. Introduction.- 9.2. Early Explorations.- 9.3. The Research Phase.- 9.4. Unsatisfactory Results.- 9.5. Steps toward Interpretation.- 9.6. Separation of Interpretation and Search.- 9.7. The Experts.- 9.8. Product Development.- 9.9. An Applications Example.- 9.10. Conclusion.- References.- 10 Novel Advances in Pattern Recognition and Knowledge-Based Methods in Infrared Spectroscopy.- 10.1. Identifying a Compound by IR Spectroscopy.- 10.1.1. Searching Collections of Spectra.- 10.1.1.1. The Role of Computers in Spectral Searching.- 10.2. Interpretation of IR Spectra.- 10.2.1. Carboxylic Acid Interpretations.- 10.2.2. The Role of Computers in IR Spectral Interpretation.- 10.2.2.1. Pattern Recognition.- 10.2.2.2. Knowledge-Based Systems.- 10.3. Current Status of PAIRS Package.- 10.3.1. PAIRS Interpretation of Carboxylic Acids.- 10.4. Conclusion.- References.- 11 Library Storage and Retrieval Methods in Infrared Spectroscopy.- 11.1. Introduction.- 11.2. Existing Computerized Databases.- 11.3. Storage Methods for Infrared Spectra.- 11.3.1. Magnetic Tape.- 11.3.2. High-Performance Disk Drives.- 11.3.3. Small Winchester Disk Drives.- 11.3.4. Floppy Disk Drives.- 11.3.5. CD ROM Disk Drives.- 11.4. Spectral Searching and Retrieval Methods.- 11.5. Spectral Information Management Systems.- 11.6. Conclusion.- References.- 12 Synergistic Use of Infrared, 13C Nuclear Magnetic Resonance, and Mass Spectral Data in Analysis Schemes for the Identification of Organic Mixture Components.- 12.1. Identification Schemes for Multisource Spectral Data.- 12.2. Algorithms for IR and MS Data.- 12.2.1. GC/IR/MS Instrumentation.- 12.2.2. Combined IR and MS Library Search Results.- 12.2.3. IR/MS Algorithms with Auxiliary MS Data.- 12.2.3.1. Chemical Ionization IR/MS.- 12.2.3.2. Accurate Mass Measurement IR/MS.- 12.3. 13C NMR Applications in Organic Mixture Analysis.- 12.3.1. Quantitative 13C NMR for the Analysis of Mixtures.- 12.3.2. NMR Data Used for the Identification of Unknowns.- 12.3.3. Use of 13C NMR to Confirm GC/MS Search Results.- 12.3.3.1. MS/NMR Algorithm.- 12.3.3.2. MS/NMR Applications to Mixtures.- 12.3.4. Analysis of Petroleum Distillates by Q13C NMR.- References.