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Chemometrics in Spectroscopy
- 2nd Edition - July 12, 2018
- Authors: Howard Mark, Jerry Workman Jr.
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 5 3 0 9 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 5 3 3 0 - 0
Chemometrics in Spectroscopy, Second Edition, provides the reader with the methodology crucial to apply chemometrics to real world data. It allows scientists using spectrosc… Read more
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Request a sales quoteChemometrics in Spectroscopy, Second Edition, provides the reader with the methodology crucial to apply chemometrics to real world data. It allows scientists using spectroscopic instruments to find explanations and solutions to their problems when they are confronted with unexpected and unexplained results. Unlike other books on these topics, it explains the root causes of the phenomena that lead to these results. While books on NIR spectroscopy sometimes cover basic chemometrics, they do not mention many of the advanced topics this book discusses. In addition, traditional chemometrics books do not cover spectroscopy to the point of understanding the basis for the underlying phenomena.
The second edition has been expanded with 50% more content covering advances in the field that have occurred in the last 10 years, including calibration transfer, units of measure in spectroscopy, principal components, clinical data reporting, classical least squares, regression models, spectral transfer, and more.
- Written in the column format of the authors’ online magazine
- Presents topical and important chapters for those involved in analysis work, both research and routine
- Focuses on practical issues in the implementation of chemometrics for NIR Spectroscopy
- Includes a companion website with 350 additional color figures that illustrate CLS concepts
Academic and industrial chemists who use NIR spectroscopy in their work; scientists in virtually every field of chemical endeavour, as well as in medicine, biochemistry, food analysis, clothing and related industries, petrochemicals, and more
Chapter 1 - A New Beginning
Section 1 - Elementary Matrix AlgebraChapter 2 - Elementary Matrix Algebra, Part 1Chapter 3 - Elementary Matrix Algebra, Part 2
Section 2 - Matrix Algebra and Multiple Linear RegressionChapter 4 - Matrix Algebra and Multiple Linear Regression: Part 1Chapter 5 - Matrix Algebra and Multiple Linear Regression: Part 2Chapter 6 - Matrix Algebra and Multiple Linear Regression: Part 3 - The Concept of DeterminantsChapter 7 - Matrix Algebra and Multiple Linear Regression: Part 4 - Concluding Remarks
Section 3 - Experimental DesignsChapter 8 - Experimental Designs, Part I: IntroductionChapter 9 - Experimental Designs, Part II: One-way ANOVAChapter 10 - Experimental Designs, Part III - Two-factor DesignsChapter 11 - Experimental Designs, Part IV:- Varying Parameters to Expand the DesignChapter 12 - Experimental Designs Part V: One-at-a-time DesignsChapter 13 - Experimental Designs, Part VI: Sequential designsChapter 14 - Experimental Designs, Part 7: β, the Power of a TestChapter 15 - Experimental Designs, Part 8: β, the Power of a Test (continued)Chapter 16 - Experimental Designs, Part 9: Sequential Designs concluded
Section 4 - Analytic GeometryChapter 17 - Analytic Geometry: Part 1 - The Basics in Two and Three DimensionsChapter 18 - Analytic Geometry: Part 2 - Geometric Representation of Vectors and Algebraic OperationChapter 19 - Analytic Geometry: Part 3 - Reducing DimensionalityChapter 20 - Analytic Geometry: Part 4 - The Geometry of Vectors and Matrices
Section 5 - Regression TechniquesChapter 21 - Calculating the Solution for Regression Techniques: Part 1 - Multivariate Regression Made SimpleChapter 22 - Calculating the Solution for Regression Techniques: Part 2 - Principal Component(s) Regression Made SimpleChapter 23 - Calculating the Solution for Regression Techniques: Part 3 - Partial Least Squares Made SimpleChapter 24 - Calculating the Solution for Regression Techniques: Part 4 - Singular Value Decomposition Made SimpleChapter 25 - Interlude: Looking Behind and AheadChapter 26 - A Simple QuestionChapter 27 - Challenges: Unsolved Problems in Chemometrics
Section 6 - Linearity in CalibrationChapter 28 - Linearity in Calibration, Act IChapter 29 - Linearity in Calibration - Act II Scene IChapter 30 - Linearity in Calibration - Act II Scene II: Reader ResponsesChapter 31 - Linearity in Calibration - Act II Scene IIIChapter 32 - Linearity in Calibration - Act II Scene IVChapter 33 - Linearity in Calibration - Act II Scene V
Section 7 - Collaborative Laboratory StudiesChapter 34 - Collaborative Laboratory Studies: Part 1 - A BlueprintChapter 35 - Collaborative Laboratory Studies: Part 2 - Using ANOVAChapter 36 - Collaborative Laboratory Studies: Part 3 - Testing for Systematic ErrorChapter 37 - Collaborative Laboratory Studies: Part 4 - Ranking TestChapter 38 - Collaborative Laboratory Studies: Part 5 - Efficient Comparison of Two MethodsChapter 39 - Collaborative Laboratory Studies: Part 6 - MathCAD Worksheet Text
Section 8 - Analysis of NoiseChapter 40 - Is Noise Brought by the Stork? Analysis of Noise - Part 1Chapter 41 - Analysis of Noise - Part 2Chapter 42 - Analysis of Noise - Part 3Chapter 43 - Analysis of Noise - Part 4Chapter 44 - Analysis of Noise - Part 5Chapter 45 - Analysis of Noise - Part 6Chapter 46 - Analysis of Noise - Part 7Chapter 47- Analysis of Noise - Part 8Chapter 48 - Analysis of Noise - Part 9Chapter 49 - Analysis of Noise - Part 10Chapter 50 - Analysis of Noise - Part 11Chapter 51 - Analysis of Noise - Part 12Chapter 52 - Analysis of Noise - Part 13Chapter 53 - Analysis of Noise - Part 14Chapter 54 - Analysis of Noise - Part 15
Section 9 - DerivativesChapter 55 - Derivatives in Spectroscopy, Part 1 - The Behavior of the Theoretical Derivative Chapter 56 - Derivatives in Spectroscopy, Part 2 - The "True" DerivativeChapter 57 - Derivatives in Spectroscopy, Part 3 - Computing the Derivative (the Savitzky-Golay method) Chapter 58 - Derivatives in Spectroscopy, Part 4 - Calibrating with DerivativesChapter 59 - Corrections and Discussion Regarding Derivatives
Section 10 - Goodness of Fit StatisticsChapter 60 - Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 1 - IntroductionChapter 61 - Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 2 - The Correlation Coefficient Chapter 62 - Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 3 - Computing Confidence Limits for the Correlation CoefficientChapter 63 - Comparison of Goodness-of-Fit Statistics for Linear Regression: Part 4 - Confidence Limits for Slope and Intercept
Section 11 - More About Linearity in CalibrationChapter 64 - Linearity in Calibration, Act III Scene I: Importance of (non)LinearityChapter 65 - Linearity in Calibration, Act III Scene II: A Discussion of the Durbin-Watson Statistic, a Step in the Right DirectionChapter 66 - Linearity in Calibration, Act III Scene III: Other Tests for non-LinearityChapter 67 - Linearity in Calibration, Act III Scene IV: How Test For non-LinearityChapter 68 - Linearity in Calibration: Act III Scene V: Quantifying Non-linearityChapter 69 - Linearity in Calibration, Act III Scene VI: Quantifying Non-linearity, Part II, and a News Flash
Section 12 - Connecting Chemometrics to StatisticsChapter 70 - Connecting Chemometrics to Statistics Part 1: The Chemometrics SideChapter 71 - Connecting Chemometrics to Statistics - Part 2, the Statistics Side
Section 13 - Limitations in Analytical AccuracyChapter 72 - Limitations in Analytical Accuracy: Part 1 - Horwitz's TrumpetChapter 73 - Limitations in Analytical Accuracy: Part 2 - Theories to Describe the Limits in Analytical AccuracyChapter 74 - Limitations in Analytical Accuracy: Part 3 - Comparing Test Results for Analytical UncertaintyChapter 75 - The Statistics of Spectral SearchesChapter 76 - The Chemometrics of Imaging SpectroscopyChapter 77 - Corrections to Analysis of Noise - Part 1Chapter 78 - Corrections to Analysis of Noise - Part 2Chapter 79 - What can NIR predict?
Section 14 - Derivations of Principal ComponentsChapter 80 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part I, Introduction and ReviewChapter 81 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part II, our first attempt: univariate curve fittingChapter 82 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part III, multivariate curve fittingChapter 83 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part IV, the Lagrange MultiplierChapter 84 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part V, Solving the Equations with DeterminantsChapter 85 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Part VI: Solving the Equations Without DeterminantsChapter 86 - The Long, Complicated, Tedious and Difficult Route to Principal Components (or, when you’re through reading this set you’ll know why it’s always done with matrices) - Coda: Applying Constrained Univariate Calculations
Section 15 - Clinical Data ReportingChapter 87 - Statistics and Chemometrics for Clinical Data Reporting - Part 1Chapter 88 - Statistics and Chemometrics for Clinical Data Reporting - Part 2: Using Excel for ComputationsChapter 89 - Statistics and Chemometrics for Clinical Data Reporting - Part 3: Using Excel for Data Plotting
Section 16 - Classical Least Squares (CLS)Chapter 90 - Classical Least Squares, Part 1: Mathematical theoryChapter 91 - Classical Least Squares, Part 2: Mathematical Theory ContinuedChapter 92 - Classical Least Squares, Part 3: Spectroscopic TheoryChapter 93 - Classical Least Squares, Part 4: Spectroscopic Theory ContinuedChapter 94 - Classical Least Squares, Part 5: Experimental ResultsChapter 95 - Classical Least Squares, Part 6: Spectral ResultsChapter 96 - Classical Least Squares, Part 7: Spectral Reconstruction of MixturesChapter 97 - Classical Least Squares, Part 8: Comparison of CLS Values with Known ValuesChapter 98 - Classical Least Squares, Part 9: Spectral Results from a Second LaboratoryChapter 99 - Classical Least Squares, Part 10: Numerical Results from the Second LaboratoryChapter 100 - Classical Least Squares, Part 11: Comparison of Results from the Two Laboratories Continued
Section 17 - Transfer of CalibrationsChapter 101 - Transfer of Calibrations - Part 1Chapter 102 - Chapter 102 - Calibration Transfer - Part 2: The Instrumentation AspectsChapter 103 - Calibration Transfer - Part 3: The Mathematical AspectsChapter 104 - Calibration Transfer - Part 4: Measuring the Agreement Between Instruments Following Calibration TransferChapter 105 - Calibration transfer - Part 5: The Mathematics of Wavelength Standards Used for SpectroscopyChapter 106 - Calibration transfer - Part 6: The Mathematics of Photometric Standards Used for Spectroscopy
Section 18 - The Importance of Units of MeasureChapter 107 - Units of Measure in Spectroscopy, Part 1: ... and Then The Light DawnedChapter 108 - Units of Measure in Spectroscopy, Part 2: It's the VOLUME, Folks!Chapter 109 - Units of Measure in Spectroscopy, Part 3: What Does it all MeanChapter 110 - Units of Measure in Spectroscopy, Part IV: Summary of our FindingsChapter 111 - Units of Measure in Spectroscopy, Part V: The "Mythbusters" and Spectral Reconstruction
Section 19 - The Best Calibration ModelChapter 112 - Choosing the Best Calibration ModelChapter 113 - Optimizing the Regression Bias and Slope
Section 20 - StatisticsChapter 114 - Statistics, Part I: First FoundationChapter 115 - STATISTICS, Part II – Second FoundationChapter 116 - STATISTICS, Part III: Third FoundationChapter 117 - Calibration Transfer Chemometrics, Part 1: Review of the SubjectChapter 118 - Calibration Transfer Chemometrics, Part 2: Overview
Section 21 - OutliersChapter 119 - Classical Least Squares, Part 13: What does it mean?Outliers - Part 1: What are OutliersChapter 120 - Classical Least Squares, Part 13: What does it mean?Outliers - Part 2: Pitfalls in Detecting OutliersChapter 121 - Classical Least Squares, Part 13: What does it mean?Outliers - Part 3: Dealing with Outliers
Section 22 - Spectral Transfer: Making Instruments AgreeChapter 122 - Calibration Transfer Chemometrics, Part 1: Review of the SubjectChapter 123 - Calibration Transfer Chemometrics, Part 2: Review of the Subject
Section 23 - Applying Standard Reference MaterialsChapter 124 - How to Apply Standard Reference Materials, Part 1Chapter 125 - How to Apply Standard Reference Materials, Part 2
Section 24 - More About CLSChapter 126 - More About CLS, Part 1: Expanding the ConceptChapter 127 - More About CLS, Part 2: Spectral Results & CLS (not requiring constituent values)Chapter 128 - More About CLS, Part 3: Expanding the Analysis to Include Concentration Information (PCR & PLS)
- No. of pages: 1090
- Language: English
- Edition: 2
- Published: July 12, 2018
- Imprint: Academic Press
- Paperback ISBN: 9780128053096
- eBook ISBN: 9780128053300
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Howard Mark
Howard Mark is President of Mark Electronics, Suffern, New York. He was previously affiliated as a Senior Scientist at Technicon Instrument Corp. in Tarrytown, New York. He holds a B.S. degree from City College of New York, an M.A. from City University of New York, and a PhD from New York University. His professional interests include instrument development, especially for spectroscopy; statistical and chemometric data analysis; and Custom software development, especially for implementation of data analysis algorithms. He received the 2003 Eastern Analytical Symposium Award for Achievement in Near Infrared Spectroscopy. He holds 6 U.S patents and has published 2 books and numerous book chapters. He has acted as Associate editor for the Handbook of Vibrational Spectroscopy, Wiley (2001). He has served as Past president of Council for Near-Infrared Spectroscopy (CNIRS), Treasurer of the New York section of the Society for Applied Spectroscopy, and as Past Chair of the New York section of the Society for Applied Spectroscopy. In addition he acts as Contributing editor and member of the Editorial Advisory Board of Spectroscopy. He has published over 150 peer-reviewed papers dealing with design and development of scientific instrumentation, new concepts in computerized instrumentation and data analysis.
Affiliations and expertise
Mark Electronics, Suffern, NY, USAJW
Jerry Workman Jr.
Jerome (Jerry) J. Workman, Jr. is Executive Vice President of Research & Engineering for Unity Scientific and Process Sensors Corporation; Certified Core Adjunct Professor at National University, CA; and Principal at Biotechnology Business Associates. He was formerly Vice President of Technology Research for Masimo Corporation; Director of Research, Technology & Applications Development for Molecular Spectroscopy & Microanalysis for ThermoFisher Scientific; Chief Technical Officer and Vice President of Research & Engineering at Argose Inc.; Senior Research Fellow at Kimberly-Clark Analytical Science & Technology; and Principal Scientist at Perkin-Elmer.
Dr. Workman has played a major role in defining and developing over 20 scientific instrument advancements with novel software improvements for successful commercial use for start-ups to major corporations. He has more than 55 U.S. and international patent applications (since 1998); 20 U.S. and international patents issued, and multiple trade secrets. He has a total of 475 technical publications; and 18 reference books on a broad range of spectroscopy, chemometrics, and data processing techniques. He has received awards from the Eastern Analytical Symposium, ASTM International, Coblentz Society; as well as multiple fellowships, technical, and government appointments. He has taught annual courses in NIR spectroscopy, chemometrics, and statistics for the Association of Official Analytical Chemists, the American Chemical Society, the Instrument Society of America, and the Federation of Analytical Chemists and Spectroscopy Societies, and at several universities and corporations.
He holds a BA degree cum laude in natural sciences, and an MA in biological sciences from Saint Mary's University of Minnesota, and a PhD degree with high commendation in biological chemistry from Columbia Pacific University. He is a graduate of the Columbia Senior Executive Program and also holds Columbia Business School Certificates in Executive Development (CIED) and in Business Excellence (CIBE). He also holds a Certificate in Strategy and Innovation from the M.I.T. Sloan School. He is listed in Who's Who in the World, Who's Who in America, and Who's Who in Science and Engineering.
Affiliations and expertise
Unity Scientific and Process Sensors Corporation, Milford, MA; National University, San Diego, CA; and Biotechnology Business Associates, Milford, MA, USARead Chemometrics in Spectroscopy on ScienceDirect