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Near-Infrared Technology in the Agricultural and Food Industries, 2nd Edition

Edited by Phil Williams and Karl Norris 
AACC Press 



Hardcover   312 pages; 223 black and white illustrations; 36  ISBN 1891127241      £166.00
Since the publication of the sold-out first edition more than a decade ago, NIR spectroscopy has become the standard for rapid, accurate analysis of ingredients and constituents used in the manufacture of food. Near Infrared Technology in the Agricultural and Food Industries, 2nd Edition is an indispensable resource that includes revised and updated chapters and current information from a renowned line-up of international experts in the field. This important title has been completely revised. New chapters on implementation, industrial applications, neural networks and a new approach to qualitative NIR analysis make this an essential reference for food scientists who wish to stay current.

NIR spectroscopy has become a key tool in the precise analysis of food components and the prediction of functionality parameters. Food technologists at any level will benefit from the breadth of knowledge and helpful spectra provided in this book. Those new to NIR spectroscopy, will find the book to be an excellent primer. Those currently using NIR spectroscopy will find this updated resource essential for gaining a deeper understanding of all aspects of NIR Technology. This is especially true as the future uses of NIR spectroscopy will include grading and classifying materials and organoleptic-type categorization of materials and foods.

Contents

The Physics of Near-Infrared Scattering, Donald J. Dahm and Kevin D. Dahm I.
Introduction
Physical Principles, A. Absorption, Remission, and Transmission B. Reflection from a Surface C. Absorption, Remission, and Transmission of a Particle D. Formation of a Representative Layer E. Reflection in Regions of Higher Absorption
Illustrations of Diffuse Reflection
Theoretical Considerations in Making Measurements A. Transmission B. Remission
Functional Representation of Absorption and Scatter in a Diffusing Medium A. The Kubelka-Munk (K-M) Equation B. Using Inherently Nonlinear Functions C. Obtaining Absorption and Remission Coefficients from Reflectance Data
Illustrations of K-M Scattering A. Scattering from Plastic Particles B. K-M Scattering
Summary

Chemical Principles of Near-Infrared Technology. Charles E. Miller
Introduction A. Name Dropping B. The Size and Speed of NIR
The Spectroscopy of NIR A. Light Energy B. Vibrational Molecular Energy C. Vibrational Spectroscopy-Made Simple D. Vibrational Spectroscopy-Made Complicated
Chemical Factors Affecting Vibrational Spectra A. The Primary Effect: Functional Group B. Secondary Effects; C. Electronic NIR Spectroscopy
The NIR Complication Factor
NIR Correlation Charts
Conclusion

Data Analysis: Wavelength Selection Methods. William R. Hruschka
Introduction
Calibration, Measurement, and Validation A. Calibration B. Measurement and Validation C. Developing a Calibration Model
Sources of Error A. Sampling Error B. Reference Method Error C. NIR Method Error and Smoothing
Single-Term Linear Regression and the Correlation Plot
Multiterm Linear Regression A. Basic Properties B. Calculation
The Derivative A. Basic Properties B. Calculation
The Fourier Transform A. Basic Properties B. Applications
Other Methods A. Component Spectrum Reconstruction B. Fast Correlation Transform C. Normalizing Spectra D. Discriminant Analysis E. Neural Networks
Conclusion
Appendix

Multivariate Calibration by Data Compression. H. Martens and T. Naes
Introduction A. Multivariate Calibration and Validation B. Calibration C. Validation and Analysis
Linear Prediction and Alternative Ways to Find the Calibration Coefficients A. Linear Analytical (Prediction) Equation B. Multiple Linear Regression as a Calibration Method to Determine the Calibration Coefficients C. Different Classes of Calibration Methods
Statistical Calibration Methods for Multicollinear NIR Data A. The General Model Framework B. Conventional NIR Calibration Methods: Selecting the "Best" Wavelengths C. Hruschka Regression: Selecting the "Best" Calibration Samples D. Fourier Transform Regression: Concentrating the NIR Data to the Main Spectral Features E. PCR: Concentrating the NIR Data to Their Most Dominant Dimensions F. PLSR: Concentrating the NIR Data to Their Most Relevant Dimensions G. Calibration Based on Beer's Model for Mixtures
Analytical Ability and Outlier Detection A. Evaluating Analytical Ability B. The Importance of Outlier Detection C. Analysis of NIR Residuals D. Leverage: Position Relative to the Rest of the Calibration Sample Set E. Analysis of the Chemical Residuals F. Combined Criteria
Data Pretreatment A. Response Linearization B. Multiplicative Scatter Correction
Illustration by Artificial Data A. Artificial Input Data B. Graphical Study of the Input Data C. The Effect of Using Insufficient Range of Calibration Samples D. Using a Complete Calibration Data Set E. PLSR F. Outliers G. Conclusions
Results for Real Data A. The Real Data Sets B. Effect of Overfitting C. Comparison of Some Calibration Methods D. Transformations of NIR Data E. Improvements of the PLS Calibration Method
Discussion A. The Statistical Calibration Methods B. Factors Affecting Choice of Method C. Data Pretreatment D. Error Detection E. Updating
Miscellaneous Topics A. Design Is Central in Calibration B. Linearity Problems C. Other Data Preprocessing Methods D. Graphical Interpretation of NIR Calibration Based on Soft Modeling
Conclusions Appendix A: Abbreviations and Symbols Appendix B: Matrix Operations Illustrated for Multicomponent Analysis

Neural Networks in Near-Infrared Spectroscopy. Claus Borggaard
Introduction
Feed Forward Neural Network Trained by Back-Propagation of Error
An Example of a Feed Forward Network
The Data Flow in the Feed Forward Network
Training the Network - Tuning the Weights
How to Present Data to the Neural Network
Monitoring the Training Process
The Feed Forward Network Used for Classification
Kohonen Self-Organizing Maps
The Architecture of the Kohonen Network
A Training Algorithm for Kohonen Networks
Neural Networks-Advantages and Disadvantages A. Disadvantages B. Advantages
Conclusions

Near-Infrared Instrumentation. W. F. McClure
Introduction
Components of NIR Systems A. Lenses and Mirrors: Collecting Radiation B. Radiation Sources C. Monochromators D. Filters E. Detectors
Computerized Spectrophotometry: The COMP/SPEC A. General Design B. Optomechanical C. Optoelectronic D. Digital Interface
Performance of the COMP/SPEC A. Photometric Noise B. Wavelength Precision C. Fourier Analysis of Instrument Performance
Software for COMP/SPEC A. COMP/SPEC File Structure B. Scanning/Analysis C. Analytical Software Package D. Computerized Spectrophotometric Analytical System.

Contemporary Near-Infrared Instrumentation. David L. Wetzel
Introduction;
Electronic Wavelength Switching: Diode Array Instruments;
Electronic Wavelength Switching: Acousto-Optic Tunable Filter Spectrometer
FT-NIR Instruments
Grating Monochromator Instruments
Interference Filter Instruments; Discrete Source Instruments: LEDs Plus Filters
Special Purpose Instruments
Imaging; X. Summary

Implementation of Near-Infrared Technology. P. C. Williams
Introduction
Calibration Development A. Implementation Steps B. Monitoring Instrument Performance
Simplified Approach to the Interpretation of Calibration Efficiency A. Accuracy and Precision B. Statistical Terms Necessary to the Evaluation of Accuracy and Precision C. The Calibration (k) Constants D. NIR Reflectance Software E. Cross-Validation F. Interpretation of PLS Calibrations for Functionality

Variables Affecting Near-Infrared Spectroscopic Analysis. Philip C. Williams and Karl Norris The Philosophy of Error
Sources of Error in NIR Testing A. Factors Associated with the Instrument B. Factors Associated with the Sample C. Operational Factors D. Outliers E. Possible Origin of Outliers

Method Development and Implementation of Near-Infrared Spectroscopy in Industrial Manufacturing Support Laboratories. Paul J. Brimmer and Jeffrey W. Hall
Introduction A. Laboratory NIR Measurements B. Industrial Manufacturing Requirements C. Industrial NIR Measurement Requirements
Sampling Requirements A. Liquids B. Solids C. Slurries
Quantitative Analysis A. Calibration Development B. Spectral Manipulation C. Calibration Models D. Validation E. Calibration Maintenance
Qualitative Analysis A. Library Development B. Validation C. Maintenance;
Conclusions

Method Development and Implementation of Near-Infrared Spectroscopy in Industrial Manufacturing Processes. Paul J. Brimmer, Frank A. DeThomas, and Jeffrey W. Hall
Introduction
Process Measurement Requirements A. Process Type B. Sample Collection and Analysis
Process Sample Interface . Liquids B. Solids C. Suspensions and Emulsions
Process Instrumentation A. Process Analyzer Configurations B. NIR Instrumentation C. NIR/Process Operator Interface
Quantitative Analysis A. Sample Selection B. Calibration Modeling Methods C. Validation-D. Maintenance
Qualitative Analysis A. Process Requirements
Conclusions

Analytical Application to Fibrous Foods and Commodities. F. E. Barton, II and S. E. Kays
Introduction
Structure and Composition of Forages
The Analysis of Forages
NIR as an Analytical Method
Advantages of the Chemometric Method

Qualitative Near-Infrared Analysis. Howard Mark
Introduction
Data Pretreatments
Mahalanobis Distances
The Polar Qualification System
Principal Components
Soft Independent Modeling of Class Analogies
K-Nearest Neighbors
Correlation Coefficient
Bootstrap Error-Adjusted Single Sample Technique.
Near-Infrared Spectra Key to Near-Infrared Spectra Appendix A: Spectra of Agricultural Products and By-Products Index
To find similar publications, click on a keyword below:
American Association of Cereal Chemists : agriculture & forestry : analytical methods : food science : spectroscopy : technology

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