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       IM Publications LLP :: NIR Spectroscopy :: Near-Infrared Technology in the Agricultural and Food Industries


1. A User-Friendly Guide to Multivariate Calibration and Classification

2. Interpreting Diffuse Reflectance and Transmittance: A Theoretical Intro. to Absorp. Spec. of Scattering Mat.

3. Multivariate Data Analysis-in Practice 5 Edition + CD

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Near-Infrared Technology in the Agricultural and Food Industries
Near-Infrared Technology in the Agricultural and Food Industries 
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.

Since the publication of the first edition, 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 categorisation of materials and foods


  • The Physics of Near-Infrared Scattering
  • I. Introduction
  • II. 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
  • III. Illustrations of Diffuse Reflection
  • IV. Theoretical Considerations in Making Measurements A. Transmission B. Remission
  • V. 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
  • VI. Illustrations of K-M Scattering A. Scattering from Plastic Particles B. K-M Scattering
  • VII. Summary
  • Chemical Principles of Near-Infrared Technology.
  • I. Introduction A. Name Dropping B. The Size and Speed of NIR
  • II. The Spectroscopy of NIR A. Light Energy B. Vibrational Molecular Energy C. Vibrational Spectroscopy—Made Simple D. Vibrational Spectroscopy—Made Complicated
  • III. Chemical Factors Affecting Vibrational Spectra A. The Primary Effect: Functional Group B. Secondary Effects
  • IV. Electronic NIR Spectroscopy
  • V. The NIR Complication Factor
  • VI. NIR Correlation Charts
  • VII. Conclusion
  • Data Analysis: Wavelength Selection Methods.
  • I. Introduction
  • II. Calibration, Measurement, and Validation A. Calibration B. Measurement and Validation C. Developing a Calibration Model
  • III. Sources of Error A. Sampling Error B. Reference Method Error C. NIR Method Error and Smoothing
  • IV. Single-Term Linear Regression and the Correlation Plot
  • V. Multiterm Linear Regression A. Basic Properties B. Calculation
  • VI. The Derivative A. Basic Properties B. Calculation
  • VII. The Fourier Transform A. Basic Properties B. Applications
  • VIII. Other Methods A. Component Spectrum Reconstruction B. Fast Correlation Transform C. Normalizing Spectra D. Discriminant Analysis E. Neural Networks
  • IX. Conclusion Appendix
  • Multivariate Calibration by Data Compression.
  • I. Introduction A. Multivariate Calibration and Validation B. Calibration C. Validation and Analysis
  • II. 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
  • III. 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
  • IV. 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; V. Data Pretreatment A. Response Linearization B. Multiplicative Scatter Correction
  • VI. 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
  • VII. 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
  • VIII. Discussion A. The Statistical Calibration Methods B. Factors Affecting Choice of Method C. Data Pretreatment D. Error Detection E. Updating
  • IX. 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
  • X. Conclusions Appendix A: Abbreviations and Symbols Appendix B: Matrix Operations Illustrated for Multicomponent Analysis.
  • Neural Networks in Near-Infrared Spectroscopy.
  • I. Introduction
  • II. Feed Forward Neural Network Trained by Back-Propagation of Error
  • III. An Example of a Feed Forward Network
  • IV. The Data Flow in the Feed Forward Network
  • V. Training the Network — Tuning the Weights
  • VI. How to Present Data to the Neural Network
  • VII. Monitoring the Training Process
  • VIII. The Feed Forward Network Used for Classification
  • IX. Kohonen Self-Organizing Maps
  • X. The Architecture of the Kohonen Network
  • XI. A Training Algorithm for Kohonen Networks
  • XII. Neural Networks—Advantages and Disadvantages A. Disadvantages B. Advantages
  • XIII. Conclusions
  • Near-Infrared Instrumentation.
  • I. Introduction
  • II. Components of NIR Systems A. Lenses and Mirrors: Collecting Radiation B. Radiation Sources C. Monochromators D. Filters E. Detectors
  • III. Computerized Spectrophotometry: The COMP/SPEC A. General Design B. Optomechanical C. Optoelectronic D. Digital Interface
  • IV. Performance of the COMP/SPEC A. Photometric Noise B. Wavelength Precision C. Fourier Analysis of Instrument Performance
  • V. 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.
  • I. Introduction
  • II. Electronic Wavelength Switching: Diode Array Instruments
  • III. Electronic Wavelength Switching: Acousto-Optic Tunable Filter Spectrometer
  • IV. FT-NIR Instruments
  • V. Grating Monochromator Instruments
  • VI. Interference Filter Instruments
  • VII. Discrete Source Instruments
  • LEDs Plus Filters
  • VIII. Special Purpose Instruments
  • IX. Imaging;
  • X. Summary
  • Implementation of Near-Infrared Technology.
  • I. Introduction
  • II. Calibration Development A. Implementation Steps B. Monitoring Instrument Performance
  • III. 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.
  • I. The Philosophy of Error
  • II. 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.
  • I. Introduction A. Laboratory NIR Measurements B. Industrial Manufacturing Requirements C. Industrial NIR Measurement Requirements
  • II. Sampling Requirements A. Liquids B. Solids C. Slurries
  • III. Quantitative Analysis A. Calibration Development B. Spectral Manipulation C. Calibration Models D. Validation E. Calibration Maintenance
  • IV. Qualitative Analysis A. Library Development B. Validation C. Maintenance
  • V. Conclusions
  • Method Development and Implementation of Near-Infrared Spectroscopy in Industrial Manufacturing Processes.
  • I. Introduction
  • II. Process Measurement Requirements A. Process Type B. Sample Collection and Analysis
  • III. Process Sample Interface . Liquids B. Solids C. Suspensions and Emulsions
  • IV. Process Instrumentation A. Process Analyzer Configurations B. NIR Instrumentation C. NIR/Process Operator Interface
  • V. Quantitative Analysis A. Sample Selection B. Calibration Modeling Methods C. Validation—D. Maintenance
  • VI. Qualitative Analysis A. Process Requirements
  • VII. Conclusions
  • Analytical Application to Fibrous Foods and Commodities.
  • I. Introduction
  • II. Structure and Composition of Forages
  • III. The Analysis of Forages
  • IV. NIR as an Analytical Method
  • V. Advantages of the Chemometric Method
  • Qualitative Near-Infrared Analysis.
  • I. Introduction
  • II. Data Pretreatments
  • III. Mahalanobis Distances
  • IV The Polar Qualification System
  • V. Principal Components
  • IVI. Soft Independent Modeling of Class Analogies
  • VII. K-Nearest Neighbors
  • VIII. Correlation Coefficient
  • IX. Bootstrap Error-Adjusted Single Sample Technique.
  • Near-Infrared Spectra
  • Key to Near-Infrared Spectra
  • Appendix A: Spectra of Agricultural Products and By-Products
  • Index

AUTHOR(S)/ED(S) Phil Williams, Karl Norris
BINDING Hardback
AVAILABILITY In stock for immediate despatch




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