Spectrochemical Analysis Using Infrared Multichannel Detectors

Spectrochemical Analysis using Infrared Multichannel Detectors Edited by Rohit Bhargava Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign Urbana, IL 61801 USA And Ira W. Levin Laboratory of Chemical Physics, NIDDK National Institutes of Health Bethesda, MD 20892 USA

Blackwell Publishing

Spectrochemical Analysis using Infrared Multichannel Detectors

Analytical Chemistry Series Series Editors: John M. Chalmers and Alan J. Handley A series which presents the current state of the art in chosen sectors of analytical chemistry. Written at professional and reference level, it is directed at analytical chemists, environmental scientists, food scientists, pharmaceutical scientists, earth scientists, petrochemists and polymer chemists. Each volume in the series provides an accessible source of information on the essential principles, instrumentation, methodology and applications of a particular analytical technique. Titles in the series: Inductively Coupled Plasma Spectrometry and its Applications Edited by S.J. Hill Extraction Methods in Organic Analysis Edited by A.J. Handley Design and Analysis in Chemical Research Edited by R.L. Tranter Spectroscopy in Process Analysis Edited by J.M. Chalmers Gas Chromatographic Techniques and Applications Edited by A.J. Handley and E.R. Adlard Chemical Analysis of Contaminated Land Edited by K.C. Thompson and C.P. Nathanail Atomic Spectroscopy in Elemental Analysis Edited by M. Cullen Pharmaceutical Analysis Edited by D.C. Lee and M. Webb Environmental Toxicity Testing Edited by K.C. Thompson, K. Wadhia and A.P. Leibner Spectrochemical Analysis using Infrared Multichannel Detectors Edited by R. Bhargava and I.W. Levin

Spectrochemical Analysis using Infrared Multichannel Detectors Edited by Rohit Bhargava Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign Urbana, IL 61801 USA And Ira W. Levin Laboratory of Chemical Physics, NIDDK National Institutes of Health Bethesda, MD 20892 USA

Blackwell Publishing

© 2005 by Blackwell Publishing Ltd Editorial Offices: Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK Tel: +44 (0)1865 776868 Blackwell Publishing Professional, 2121 State Avenue, Ames, Iowa 50014-8300, USA Tel: +1 515 292 0140 Blackwell Publishing Asia Pty Ltd, 550 Swanston Street, Carlton, Victoria 3053, Australia Tel: +61 (0)3 8359 1011 The right of the Author to be identified as the Author of this Work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. First published by Blackwell Publishing Ltd Library of Congress Cataloging-in-Publication Data Spectrochemical analysis using infrared multichannel detectors/edited by Rohit Bhargava & Ira Levin. p. cm. Includes bibliographical references and index. ISBN-13: 978-1-4051-2504-8 (acid-free paper) ISBN-10: 1-4051-2504-7 (acid-free paper) 1. Infrared spectroscopy. 2. infrared imaging. 3. Spectrum analysis. I. Bhargava, Rohit, 1973– II Levin, Ira, 1935– QD96.I5.S64 2005 535.8’42–dc22 2005009290 ISBN-13: 978-1-4051-2504-8 ISBN-10: 1-4051-2504-7 British Library Cataloguing-in-Publication Data: A Catalogue record for this title is available from the British Library Set in 10/12 pt Times by Newgen Imaging Systems (P) Ltd, Chennai, India Printed and bound in India by Replika Press, Pvt Ltd., Kundli The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp processed using acid-free and elementary chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. For further information on Blackwell Publishing, visit our website: www.blackwellpublishing.com

Contents

Contributors

xi

Preface

xv

1

Fourier transform mid-infrared spectroscopic imaging

1

Rohit Bhargava and Ira W. Levin

2

1.1 1.2

Introduction Fundamentals of FTIR spectroscopy 1.2.1 Interferometer characteristics 1.3 FTIR microspectroscopy using a single-element detector 1.3.1 IR microscopes and point spectroscopy 1.3.2 FTIR mapping 1.3.3 Limitations of FTIR point mapping 1.4 FTIR imaging with multichannel detectors 1.4.1 Imaging with large format array detectors 1.4.2 Interfacing an interferometer to large array detectors 1.4.3 The SNR of imaging spectrometers 1.4.4 The evolving detector array technology 1.5 Raster scanning with linear array detectors 1.5.1 Choice of either small or large detector arrays 1.6 Conclusions References

1 1 2 8 8 11 11 13 13 15 16 19 20 21 22 23

Near-infrared spectral imaging with focal plane array detectors

25

E. Neil Lewis, Linda H. Kidder, Eunah Lee and Kenneth S. Haber 2.1 2.2

Background: single-point near-infrared spectroscopy Development of NIR spectral imaging 2.2.1 History of spectral imaging 2.2.2 FPAs – specifications 2.2.3 Implementation of NIR imaging 2.2.4 Data processing 2.2.5 Comparison of vibrational spectroscopic imaging modalities 2.2.6 Safety in numbers

25 27 27 28 29 31 33 35

vi

CONTENTS

2.3

Examples of NIR spectral imaging capabilities 2.3.1 Sample statistics and FOV 2.3.2 High-throughput applications 2.3.3 Statistics, morphology, abundance – using an internal reference 2.4 Conclusions References 3

Multichannel detection with a synchrotron light source: design and potential

37 37 42 43 51 52

56

G. Larry Carr, Oleg Chubar and Paul Dumas

4

3.1 3.2

Introduction Comparisons of thermal and SR sources 3.2.1 Blackbody radiation 3.2.2 SR as an IR source 3.3 The IR microspectrometer: instrumentation and optical analysis 3.3.1 Microspectrometer system components 3.3.2 Performance: imaging at the diffraction limit 3.3.3 The FPA microscope system 3.4 Combining SR with an FPA microspectrometer 3.4.1 FPA microspectrometer for PSF image deconvolution 3.4.2 SR as an extended IR source 3.5 Summary Acknowledgements References

56 58 59 59 68 68 72 77 80 80 81 82 83 83

Multivariate analysis of infrared spectroscopic image data

85

Scott W. Huffman and Chris W. Brown

5

4.1 4.2

Introduction Preprocessing hyperspectral images 4.2.1 Data compression 4.2.2 Smoothing spectra 4.2.3 Noise in hyperspectral images 4.3 Processing hyperspectral images 4.3.1 Feature extraction 4.3.2 Concentration image maps 4.4 Conclusions References

85 85 86 90 92 101 101 109 113 113

FTIR imaging of multicomponent polymers

115

Jack L. Koenig 5.1

Introduction

115

CONTENTS

5.2 5.3

Imaging requirements for polymer characterization Polymer sampling for FTIR imaging 5.3.1 Transmission measurements 5.3.2 Reflection FTIR imaging measurements 5.3.3 ATR FTIR imaging 5.4 FTIR image analysis 5.4.1 Selection of characteristic spectral stains for each component 5.4.2 Construction of contour plots 5.4.3 Histograms 5.5 Applications of FTIR imaging to complex polymer systems 5.5.1 FTIR imaging of polymer laminate films 5.5.2 Chemical morphology of multi-component polymeric materials 5.5.3 Immiscible polymer blends 5.5.4 Crosslinking-induced phase separation of elastomers 5.5.5 Semicrystalline polymer systems 5.5.6 Semicrystalline polymer blends 5.6 Summary and conclusions References 6

7

vii 115 116 116 118 119 121 122 122 123 126 126 126 132 135 137 139 140 140

Combinatorial approaches to catalyst development with multichannel detectors

143

Christopher M. Snively and Jochen Lauterbach 6.1 Introduction – combinatorial materials development 6.2 Array detection schemes for high-throughput analysis 6.3 FTIR imaging as a high-throughput technique 6.4 Applications 6.4.1 Application I: resin-supported ligands 6.4.2 Application II: adsorbates on catalyst surfaces 6.4.3 Application III: reactor effluent quantification 6.5 Data management 6.6 Summary References

143 145 146 148 148 149 150 151 155 156

Materials analysis systems based on real-time near-IR spectroscopic imaging Martin Kraft, Raimund Leitner and Herwig Mairer 7.1 Introduction 7.2 Data acquisition 7.2.1 Image acquisition 7.2.2 Sample–radiation interaction 7.3 Instrumentation

158 158 158 158 161 162

viii

8

9

CONTENTS

7.4

Real-time data analysis 7.4.1 Pre-processing 7.4.2 Spectral data evaluation 7.5 Integrated image processing 7.6 Material analysis applications 7.6.1 Industrial waste classification and sorting 7.6.2 Surface coating inspection 7.6.3 Food control 7.6.4 Mineralogical material analysis References

164 165 166 168 169 169 172 172 172 173

Industrial applications of near-IR imaging Anthony E. Dowrey, Gloria M. Story and Curtis Marcott

175

8.1 8.2 8.3

Introduction Experimental Application using NIR spectroscopic imaging 8.3.1 Water migration on fabrics 8.3.2 Spray nozzle patterns 8.3.3 Surfactant deposition on a nonwoven substrate 8.3.4 Flavored chips 8.3.5 Lotion distribution on nonwoven paper 8.4 Conclusions Acknowledgements References

175 177 178 178 179 179 181 182 184 187 188

IR spectroscopic imaging

189

Max Diem, Melissa J. Romeo, Susie Boydston-White and Christian Matthäus 9.1 9.2

Introduction: definition and goals of spectral mapping Experimental 9.2.1 Instrumental aspects: PE Spotlight 300 9.2.2 Samples 9.2.3 Spectral maps of individual cells 9.2.4 Spectral maps of ‘smears’ 9.2.5 Spectral maps of tissues 9.2.6 Mathematical analysis 9.3 Results and discussion 9.3.1 Spectral histopathology of lymph nodes 9.3.2 Spectral maps of individual cells 9.3.3 Spectral maps of ‘cell smears’ 9.4 Conclusions Acknowledgements References

189 190 190 191 191 192 192 193 194 194 197 200 202 202 202

CONTENTS

10 FPA imaging and spectroscopy for monitoring chemical changes in tissue

ix

204

Bayden R. Wood and Don McNaughton 10.1 Introduction 10.2 Applications of FTIR tissue imaging to cervical cancer 10.2.1 History of FTIR spectroscopy applied to cervical cancer diagnosis 10.2.2 FTIR point-to-point mapping of cervical tissue 10.2.3 FTIR focal plane array imaging of cervical tissue 10.3 FPA imaging and spectroscopy for monitoring chemical changes associated with collagen-induced arthritis 10.4 Application of FTIR 3D imaging to histology 10.5 Conclusions Acknowledgements References 11 Infrared microscopy and imaging of hard and soft tissues

204 205 205 206 207 224 229 230 231 231 234

Richard Mendelsohn, Adele L. Boskey and Nancy P. Camacho 11.1 Introduction 11.2 IR imaging protocols 11.3 Applications of FTIR microscopy and imaging to tissues 11.3.1 Bone 11.3.2 Skin 11.3.3 Cartilage Acknowledgements References

234 235 235 235 243 249 257 257

12 Mid-infrared imaging applications in agricultural and food sciences 261 Douglas L. Elmore, Carrie A. Lendon, Sean A. Smith and Chad L. Leverette 12.1 Introduction 12.2 Spatially resolved chemical and physical information 12.3 Chemical infrared imaging of protein, carbohydrates and fat in agri-food mixtures 12.4 Sampling 12.5 Chemometrics 12.6 Applications 12.7 Complementary imaging techniques 12.8 Conclusions References

261 264 266 268 270 272 277 278 279

x

CONTENTS

13 Applications of near-infrared imaging for monitoring agricultural food and feed products Vincent Baeten and Pierre Dardenne 13.1 Introduction 13.2 Use of NIR imaging for remote control and monitoring in agriculture 13.2.1 The problem 13.3 NIR imaging for food analysis 13.3.1 The problem 13.4 NIR imaging for feed analysis 13.4.1 The problem 13.5 Conclusion References Index

283 283 284 284 288 288 294 294 297 297 303

Contributors

Dr Vincent Baeten

Département Qualité des Produits Agricoles, Centre Wallon de Recherches Agronomiques, 24 Chaussée de Namur, 5030 – Gembloux, Belgium

Professor Rohit Bhargava

Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, USA

Dr Adele L. Boskey

Mineralized Tissues Laboratory, The Hospital for Special Surgery, 535 E 70th Street, New York, NY 10021, USA

Dr Susie Boydston-White

Department of Chemistry and Biochemistry, Hunter College and Graduate School, City University of New York, New York, NY 10021, USA

Professor Chris W. Brown

Department of Chemistry, University of Rhode Island, Pastore Hall, Kingston RI 02881, USA

Dr Nancy P. Camacho

The Musculoskeletal Imaging and Spectroscopy Laboratory, The Hospital for Special Surgery, 535 E 70th Street, New York, NY 10021, USA

Dr G. Larry Carr

National Synchrotron Light Source (NSLS), Brookhaven National Laboratory, 75 Brookhaven Avenue, Bldg 725B, Upton, NY 11973-5000, USA

xii

CONTRIBUTORS

Dr Oleg Chubar

LURE and Synchrotron SOLEIL, L’Orme des Merisiers, Saint-Aubin - BP 48, 91192 Gif-Sur-Yvette Cedex, France

Dr Pierre Dardenne

Département Qualité des Produits Agricoles, Centre Wallon de Recherches Agronomiques, 24 Chaussée de Namur, 5030 – Gembloux, Belgium

Professor Max Diem

Department of Chemistry and Biochemistry, Hunter College and Graduate School, City University of New York, New York, NY 10021, USA

Mr Anthony E. Dowrey

The Procter & Gamble Company, Miami Valley Laboratories, Cincinnati, OH 45253-8707, USA

Dr Paul Dumas

LURE and Synchrotron SOLEIL, L’Orme des Merisiers, Saint-Aubin - BP 48, 91192 Gif-Sur-Yvette Cedex, France

Dr Douglas L. Elmore

Scientific Resources Center, Cargill Incorporated, 7101 Goodlett Farms Parkway, Cordova, TN 38016, USA

Dr Kenneth S. Haber

Spectral Dimensions Inc, 3416 Olandwood Court, Suite 210, Olney, MD 20832, USA

Dr Scott W. Huffman

Laboratory of Chemical Physics, NIDDK, Building 5, B1-38, National Institutes of Health, Bethesda, MD 20892-0520, USA

Dr Linda H. Kidder

Spectral Dimensions Inc, 3416 Olandwood Court, Suite 210, Olney, MD 20832, USA

Professor Jack L. Koenig Department of Macromolecular Science, Case Western Reserve University, Kent Hale Smith Building, Room 212, 2100 Adelbert Road, Cleveland, Ohio 44106-7202, USA

CONTRIBUTORS

xiii

Dr Martin Kraft

Carinthian Tech Research AG, Europastrasse 4/1, A - 9524 Villach / St. Magdalen, Austria

Professor Jochen Lauterbach

Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA

Dr Eunah Lee

Spectral Dimensions Inc., 3416 Olandwood Court, Suite 210, Olney, MD 20832, USA

Dr Raimund Leitner

Carinthian Tech Research AG, Europastrasse 4/1, A - 9524 Villach / St. Magdalen, Austria

Dr Carrie A. Lendon

Scientific Resources Center, Cargill Incorporated, 7101 Goodlett Farms Parkway, Cordova, TN 38016, USA

Dr Chad L. Leverette

Department of Chemistry and Physics, University of South Carolina Aiken, Aiken, SC 29801-6399, USA

Dr Ira W. Levin

Building 5, B1-32, Laboratory of Chemical Physics, NIDDK, National Institutes of Health, Bethesda, MD 20892-0520, USA

Dr E. Neil Lewis

Spectral Dimensions Inc., 3416 Olandwood Court, Suite 210, Olney, MD 20832, USA

Dr Herwig Mairer

Carinthian Tech Research AG, Europastrasse 4/1, A - 9524 Villach / St Magdalen, Austria

Dr Curtis Marcott

The Procter & Gamble Company, Miami Valley Laboratories, Cincinnati, OH 45253-8707, USA

Dr Christian Matthäus

Department of Chemistry and Biochemistry, Hunter College and Graduate School, City University of New York, New York, NY 10021, USA

Professor Don McNaughton

School of Chemistry, PO Box 23, Monash University, Melbourne, Victoria 3800, Australia

xiv

CONTRIBUTORS

Professor Richard Mendelsohn

Department of Chemistry, Rutgers University, 73 Warren St, Newark, NJ 07102-1811, USA

Dr Melissa J. Romeo

Department of Chemistry and Biochemistry, Hunter College and Graduate School, City University of New York, New York, NY 10021, USA

Dr Sean A. Smith

Scientific Resources Center, Cargill Incorporated, 7101 Goodlett Farms Parkway, Cordova, TN 38016, USA

Dr Christopher M. Snively

Department of Materials Science and Engineering, 201 Dupont Hall, University of Delaware, Newark, DE 19716-3106, USA

Ms Gloria M. Story

The Procter & Gamble Company, Miami Valley Laboratories, Cincinnati, OH 45253-8707, USA

Dr Bayden R. Wood

School of Chemistry, PO Box 23, Monash University, Melbourne, Victoria 3800, Australia

Preface A tremendous growth in the utilization of multichannel detectors for infrared (IR) spectroscopy has been observed over the past decade. In some cases, the incorporation of multichannel detectors has significantly changed the practice of IR spectroscopy; while in others, it has provided new opportunities for spectroscopic analyses. Since the underlying premise of spectroscopic analyses is to uncover chemical information, we have devoted this volume to examining the new insights and potential applications enabled by technology based on multichannel detectors. Instead of focusing on the science and technology of detectors or of IR spectroscopy, the chapters illustrate how these detectors are incorporated into spectroscopic instrumentation, the varied numerical techniques required for data analysis and the applications of multichannel-detector-enabled spectroscopy in a variety of fields. This compilation attempts to present the material in a manner that is accessible to readers without extensive background in spectroscopy or of the applications, yet the depth is sufficient to serve as a ready reference to seasoned practitioners in the field. We have not covered in-depth some recently emerging instrumentation and techniques that are likely to become more prevalent. The focus is, instead, on technologies that are mature enough to provide practitioners the tools to undertake spectrochemical analyses. Mindful that a compilation of this nature is always at danger of becoming outdated soon, the contents of this volume seek to provide the reader with an appreciation of the current state of the art as well as a perspective that allows an appreciation of future developments. Multichannel detectors for spectroscopy have moved rapidly from the laboratory to practical applications. Hence, we have also attempted to balance contributions from the ‘laboratory’ and from the ‘field’, providing a taste of both fundamental developments and ‘real-world’ applications to the reader. The first section (Chapters 1–4) provides an overview of instrumentation and data analysis techniques that form the foundation for practical applications. Chapter 1 outlines the development and capabilities of microspectroscopy in the mid-IR spectral region facilitated by multichannel detectors. Chapter 2 provides an overview of technology for the near-IR region and the unique contribution of imaging to near-IR spectral analyses. Utilization of a unique light source, the synchrotron, is discussed in Chapter 3, which details the theoretical aspects of enhanced microscopy and design considerations for future developments. A crucial bridge between instrumentation and applications is the mathematical analysis of large mathematical datasets. Chapter 4 presents several multivariate analysis techniques that are useful for deriving information from datasets.

xvi

PREFACE

The second section of the book details application of instrumentation and numerical analysis to spectroscopic analyses in a number of fields. The applications cover fields such as materials science (Chapters 5–8), biomedical science (Chapters 9– 11) and agricultural and food sciences (Chapters 12 and 13). Chapter 5 details the application of mid-IR FTIR spectroscopic imaging to multicomponent polymeric systems, salient features of data analysis for these systems, and a number of examples. Chapter 6 describes the utility of multichannel detectors to catalyst development and provides examples to demonstrate the translation of laboratory concepts to viable industrial catalysis. Chapter 7 provides an overview, and examples, of the application of near-IR imaging systems to the ‘real world’ in ‘real time’. Issues in the industrial design and analysis of several commercial products are detailed in Chapter 8. The application of mid-IR imaging systems based on multichannel detectors to cells, cell ensembles, and soft tissues are discussed in Chapter 9. Chapter 10 describes the application of focal plane array systems, which contain a very large multichannel advantage, to the rapid analysis of tissue for histopathologic changes and disease diagnosis in soft tissue. Chapter 11 examines the biochemical changes in both soft and hard tissue using IR microscopy based on different multichannel detectors. Chapter 12 provides an overview of the applications of mid-IR systems in food sciences, including the prospects of detailed biochemical descriptions of food through imaging. Chapter 13 provides an overview of the requirements and possibilities of monitoring agricultural materials through near-IR imaging. We would like to acknowledge the contributions of several colleagues over the years to our research efforts in the areas of applying multichannel detectors to spectroscopic analyses. We would also like to acknowledge the superb job by Blackwell (David McDade and Graeme Mackintosh) and Newgen (Mohan Kumar). Last, but not the least, we would like to thank all authors for their enthusiastic efforts contributing to the timely publication of this volume. Rohit Bhargava Ira W. Levin

1

Fourier transform mid-infrared spectroscopic imaging Microspectroscopy with multichannel detectors Rohit Bhargava and Ira W. Levin

1.1 Introduction Infrared (IR) spectroscopy is commonly employed for a range of spectrochemical analyses.1,2 A variety of technical advances over the last 30 years have resulted in the ability to rapidly record IR spectra using interferometers in relatively straightforward configurations. The scope of applications available to this technique has been enhanced by the versatility inherent in its instrumentation and multiplicity of sampling techniques which, when coupled to available data analysis approaches and spectral databases, provide insights in the characterization of molecular properties. IR spectroscopy, however, had been primarily a bulk material technique, since obtaining spectral information from microscopic sample volumes has often proved difficult. The convergence of optical microscopy and IR spectroscopy, however, now permits the high-throughput recording of spatially resolved spectral information, thus providing an attractive approach to assessing the component properties of complex materials. Although the first efforts in IR microscopy occurred more than 50 years ago,3 progress in making microspectroscopy routinely accessible to the larger vibrational spectroscopic community, in general, was slow. Considerable activity over the last 15–20 years, in particular, has resulted in the capability of routinely collecting IR spectra from narrowly delineated sample regions through a combination of IR microscopes, IR interferometers, sensitive detectors and powerful computers.4 These developments have resulted in the emergence of a Fourier transform infrared (FTIR) microspectroscopy subdiscipline.5–11 In the present discussion, we briefly review the evolution of spatially resolved IR spectroscopy in the mid-IR spectral region and the revolutionary changes in practice that are facilitated specifically by the integration of multichannel array detectors. Several applications of FTIR imaging, including its relevance to biological and polymeric materials analyses, are discussed elsewhere in this volume.

1.2 Fundamentals of FTIR spectroscopy Fourier transform infrared microspectroscopy couples both interferometry and microscopy into an integrated instrument. Since interferometry is an important

2

SPECTROCHEMICAL ANALYSIS USING IR DETECTORS

component of the technology, it is instructive to appreciate the salient features of conventional FTIR spectroscopy in the context of microspectroscopy and imaging. Since the fundamentals of FTIR spectrometry, including spectroscopic applications, are summarized in detail elsewhere,12 we briefly describe here the mathematical basis of interferometer data acquisition, the integration of an interferometer with a microscope and the performance metrics useful in spectrochemical analysis.

1.2.1 Interferometer characteristics The Michelson interferometer employed in FTIR spectroscopy utilizes a single source, recombines split radiation beams to realize interference and then measures the interference pattern of a broadband spectrum. Figure 1.1 presents a schematic demonstrating the concept of scanning interferometry. A thin, nonabsorbing beamsplitter divides a beam from a broadband radiation source into two perpendicular directions. These beams, after being reflected by mirrors along the normal, undergo interference at the beamsplitter where one component of the combined beam is reflected back to the source, while the second component is directed either to a detector or to a microscope as modulated light. A change of the relative position of the mirrors, by, for example, repositioning one mirror with the second remaining stationary, results in altering the path differences between the two interfering beams. The intensity of light at the detector due to interference at a single frequency may be written in terms of the wavenumber, ν, ¯ as I  (δ) = 0.5I (¯ν)[1 + cos(2πδ ν)] ¯ I

(1.1)

where, is the intensity directed to the detector, I is the original intensity of the incident beam at the beamsplitter and δ is the mirror retardation. The resultant intensity at the detector consists of a component that is invariant of the path difference and a second sinusoidally varying component. The invariant component, termed the direct current (d.c.) component, results in the addition of a constant value to Fixed mirror M1 M2

From source To source

B

To detector Beam splitter Figure 1.1 Schematic diagram of a Michelson interferometer.

Moving mirror

FOURIER TRANSFORM MID-IR IMAGING

3

the detector readout signal, which reduces the available dynamic range for large array detectors. The d.c.-component does not contribute, however, to the spectral signal and may be subtracted. The component of the detected signal that changes with mirror retardation is termed the interferogram. In practice, beamsplitters do not reflect and transmit exactly 50% of light; their recorded performance, which varies with wavelength and the detector response, including that of the associated electronics, affects the intensity of the recorded interferogram. Both the prefactor in Equation (1.1) and various instrumental efficiencies are included in a simple equation for the interferogram that relates the observed intensity at the detector to the broadband emission profile of the source, B, as a function of the retardation by  +∞ B(¯ν ) cos(2πδ ν¯ )dν¯ (1.2) I (δ) = −∞

Radiation path differences may be achieved in an interferometer by either a continuous motion of the mirror(s) or by incremental steps that sequentially move a mirror to a specific retardation and then, after a time delay, rapidly move the mirror to the next optical retardation. In this manner, a range of path differences are obtained. It is also instructive to visually examine the interferogram for a broadband spectral source as shown in Fig. 1.2. The large amplitude signal, or the centerburst, contains the majority of spectral frequencies that have been added constructively. Regions away from the centerburst are small in magnitude and are termed wings. Mathematically, the other half of the cosine pair derived from the above equation is given by the integral of the even function I (δ) such that  +∞ I (δ) cos(2π ν¯ δ)dδ (1.3) B(¯ν ) = 2 0

Hence, a spectral profile may be specified completely by the measured interferometric signal at a known sequence of interferometer mirror retardations. The spectral profile is then computationally recovered by measuring the interferogram from zero mirror retardation to an infinitely long retardation at infinitesimally small increments of retardation. It is clear that computational limitations and instrumental considerations require that the interferogram be sampled over a finite number of optical retardations. This practical consideration dictates that the sampling interval determines the measured spectral range while the finite number of mirror retardation steps serve to provide a measure of the spectral resolution achieved. While these details and their relationships are explained elsewhere,12 it suffices for spectroscopic analyses to understand that extended measurements are required to record either high spectral resolution or large spectral bandwidths. Bandpass filters coordinate the spectral output of the source to the measured spectral bandwidth to prevent spurious spectral features through mathematical aliasing.

1.2.1.1 Continuous-scan interferometers In the most common commercial implementation of interferometric measurements, the moving mirror is scanned at a constant velocity v. Consequently, the mirror

4

SPECTROCHEMICAL ANALYSIS USING IR DETECTORS

Signal counts (a.u.)

9000 8000 7000 6000 5000 4000 0

500

1000

1500

2000

Retardation point 4200

Signal (a.u.)

3500 2800 2100 1400 700 0 500 1000 1500 2000 2500 3000 3500 4000 Wavenumber (cm–1)

Absorbance (a.u.)

0.5 0.4 0.3 0.2 0.1 0.0 1000 1500 2000 2500 3000 3500 4000 Wavenumber (cm–1)

Figure 1.2 Interferogram recorded by a d.c.-coupled detector in which the signal counts can vary from 0 to 16 384 (top). Fourier transformation of the recorded interferogram profile yields a single-beam spectrum (middle). Single-beam spectra from a sample can be ratioed point-by-point in the spectral domain to single-beam spectra acquired without a sample in the beam path, yielding absorbance spectra (bottom). The absorbance features in a spectrum can be correlated to the molecular properties of the sample (dark profile), while a featureless spectrum (light profile) denotes the lack of sample in the beam path.

FOURIER TRANSFORM MID-IR IMAGING

5

travels a distance given by vt in a time t. Since the path difference for radiation is twice of the difference in distance between the mirrors, the retardation can be expressed in terms of the velocity and time as δ = δ0 + 2vt

(1.4)

where δ0 is the retardation at the beginning of the observation time. Since the time is set arbitrarily, the point of reference can be taken to be the point of zero retardation, resulting in the optical retardation being set equal to 2vt. Analogous to velocity of the mirror, a rate of change of optical retardation may be termed the optical velocity, which is twice the velocity of the moving mirror. The expression for the interferogram, in terms of these directly measurable processing parameters, reduces to  ∞ B(¯ν) cos(4πvt ν)d ¯ ν¯ (1.5) I (t) = −∞

where t is the time after zero retardation. By comparing the sinusoidal variation of the interference pattern of a single wavenumber to a standard expression for a sinusoidal wave [cos(2πf t)], the characteristic frequency of the signal due to that wavenumber is determined. This characteristic frequency of the interferogram corresponding to a wavenumber, ν, ¯ is given by fν¯ = 2v ν¯

(1.6)

This frequency within the interferogram is termed the Fourier frequency corresponding to that wavenumber; consequently, the spectrum is encoded by the interferometer at different frequencies. The Fourier frequency is a function of the scanning mirror velocity and can be employed to increase the signal-to-noise ratio (SNR) of the commonly employed a.c.-coupled HgCdTe (MCT) detectors. When the Fourier frequency is greater than 1 kHz, the mode of interferometry is termed ‘rapid scan’. For the commonly recorded 4000–650 cm−1 range in the mid-IR spectral region, the rapid-scanning regime necessitates mirror speeds of at least 0.01 cm s−1 . Since the desired spectral range and resolution determine the data recording characteristics and the distance traveled by the interferometer’s moving mirror, the number of data points and their recording interval are easily specified with respect to the mirror motion. These quantities are critical since the detector’s data acquisition speed becomes important in the practice of FTIR spectroscopy when utilizing rapid-scanning interferometers. When mirror velocities are lower than the rapid-scan conditions, the mode of interferometer operation has been termed ‘slow scan’. At low velocities, however, control over mirror motion is difficult in commercial interferometers designed for rapid scanning, resulting in the measurement being susceptible to noise problems. Hence, continuous-scan interferometers generally operate above the rapid-scan limit.

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SPECTROCHEMICAL ANALYSIS USING IR DETECTORS

(a)

(b)

d

d

t

t

Figure 1.3 Modes of the mirror scanning in interferometry (a) continuous-scan mode and (b) step-scan mode.

1.2.1.2 Step-scan interferometers In step-scan interferometers, the interferogram intensity is recorded at a constant retardation and the interferometer is subsequently stepped quickly to the next retardation. One mirror may be moved by itself while the other is held at a constant position in the interferometer, or the two mirrors may be moved in tandem to yield a constant retardation. The advantage of moving the second (usually stationary or fixed) mirror is twofold. First, the primary moving mirror can continue to scan, as in the rapid-scan mode, with no separate feedback, control or hardware modifications being required in comparison to a rapid-scan interferometer. Second, the second mirror may now be moved a short distance in a precise manner. Since control mechanisms are easier to implement and are more accurate for motion over short distances, independent control over the mirrors allows for greater flexibility in rapidly attaining a desired retardation. This configuration affords a greater flexibility in recording data since the retardation and the resultant Fourier frequencies imposed on the interferogram are decoupled from the time domain of data acquisition. Hence, unlike the rapid-scanning interferometry, any detector may be employed with the signal being recorded for any specific time period at each retardation step. Conceptually, in contrast to the rapid-scan implementation, the time period for recording spectral data need not be equal at every retardation. Although this concept has not yet been implemented, longer data recording times could be employed when recording data in the wings of the interferogram in contrast to the time expended in recording the centerburst. While an intensity profile at the detector as a function of retardation may be acquired in a step-scan mode, two major drawbacks affect this method of interferogram acquisition. First, the mirror(s) requires stabilization times with mirror inertia and time constants of the control loop determining this parameter in achieving a given optical retardation. Second, additional hardware and control mechanisms need to be incorporated into the spectrometer, thus increasing instrument cost and complexity. In certain cases, however, the utility of a step-scan instrument justifies this additional expense. Historically, the step-scan approach was favored with slow detectors. With the advent of fast detectors and electronics, step-scan interferometry became

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7

less popular. Step-scan interferometry, however, presents another advantage. While monitoring transient events that occur repetitiously, the mirror retardation can be held constant as the event is recorded as a function of time; the retardation is then changed with the event being triggered again. In this manner, the temporal evolution of signal from a repeated transient event can be obtained as a function of retardation. This procedure forms the basis of time-resolved FTIR spectroscopy and has been widely employed over the last decade.13–15

1.2.1.3 Performance figures of merit: the SNR The sensitivity and detection limits of an analytical technique are determined by the SNR of the measurement, an important metric for assessing both the instrumental performance and analytic limits of the spectral measurement. Following typical analytical practices, 3 and 10 times the noise have been suggested as limits of detection and of quantification for IR spectroscopy, respectively. The performance of interferometers in the continuous-scan mode, which is simpler compared with that of the step-scan mode, has been analyzed well. The SNR of a spectrum measured using a Michelson interferometer is given by12 SNR =

¯ ∗ξ √ Uν¯ (T )(ν)D t √ AD

(1.7)

where U is the spectral energy density at wavenumber ν¯ from a black-body source maintained at a temperature T . The specific detectivity of the detector, D ∗ , that has a sensing area denoted by AD measures the signal for a specific period of time, t. The throughput, , and efficiency of radiation transmission, ξ , of the interferometer also affect the quality of resulting data. The well-known ‘trading rules’ of FTIR spectroscopy16 provide for an easy understanding of the performance of spectrometers in terms of the experimental configuration and measurement variables. It must be emphasized that the trading rules are valid strictly for rapid-scanning interferometers, but their understanding is helpful in analyzing the performance of other modes of interferometry, as well. For example, an extension of the same principles to understand the complex dependence of SNR on measurement variables for stepscan interferometry is reported elsewhere.17 Most importantly, the SNR depends on the square root of the data acquisition time. Although the SNR of acquired data may be increased in a straightforward manner, an increase in the SNR by a factor, for example, of k, requires that the acquisition time be increased by a factor of k 2 . When the spectral resolution of recorded data (ν¯ ) is increased by a factor of k, the acquisition time must be increased by a factor of k 2 to maintain the SNR of the acquired data. If the throughput is decreased by a factor of k, the SNR decreases by the same proportion. This effect of throughput decrease on the SNR can be compensated by an increase in the data acquisition time by a factor of k 2 . (Though we have assumed that the specific detectivity and the dimensions of the detector change independently in this discussion and in a later discussion in the chapter, the assumption is not strictly valid. The characteristics of the detector change with size and the

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wavelength sensitivity of the detector. As a first approximation for the limited range of sizes of detectors considered in our analyses, the characteristics of the detector may be considered constant.)

1.3 FTIR microspectroscopy using a single-element detector The first reports of a spectrometer interfaced to a microscope18 appeared more than 50 years ago with a commercial instrument being introduced within 6 years of that report. These configurations employed single-beam dispersive spectrometers for spectral measurements and all-reflecting microscope optics for localizing the beam. Single-beam spectra were plotted using a strip-chart recorder and absorbance information was generated by manual calculations of the relative background and sample scans. Clearly, this approach was ill suited to widespread use for rapid analysis of spatially small regions and, as a consequence, gained little acceptance as a major analytical tool. The microspectroscopy technique made no significant advances until the coupling of an interferometer and microscope to a digital computer provided modern Fourier transform spectroscopy, time averaging and mapping. (Though a microcomputer controlled microspectrometer was realized as early as 1978 (NanoSpec 20-IR from Nanometrics, Sunnyvale, CA), the first true FTIR coupling to a microscope was achieved a few years later by Digilab in 1983 based on a microscope built by Spectra-Tech – now, Thermo Electron Corp.) Since the light throughput is used more effectively in an interferometer and since the spectral response is precisely measured with respect to the wavelength, a significant increase is achieved in the SNR results. The SNR increase was also aided by the development of more stable, sensitive, fast-response cryogenic detectors that yielded reproducible measurements, further spurring interest in microspectroscopy. The use of a computer-controlled stage and digital spectral processing allowed for recording data from numerous sampling points on specimens, which facilitated mapping the composition of samples through sequential measurements. These units became commercially available in the 1980s and are, to date, popular analytical tools. Since the state-of-the-art instrumental microscopy configurations using multichannel detectors are derived from these systems, it is instructive to examine their concept and performance.

1.3.1 IR microscopes and point spectroscopy Although the IR microscope is similar to the optical microscope, it differs with respect to its physical construction and the source employed. Since the refractive glass optics of the typical optical microscope are not suitable for IR microscopy, as glass optics do not transmit wavelengths longer than ∼4.5 μm, microscopes for wide-band IR spectroscopy incorporate all-reflecting optics and aspherical reflecting surfaces in a Cassegrain-type configuration for minimizing optical aberrations. Refractive elements, if required for special needs, are constructed from IR transmitting materials that are resistant to moisture (e.g. CaF2 ). Although refracting

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elements may be incorporated, we note that typical IR microscopes operate over a significantly larger bandwidth (>12 μm) than optical microscopes (16 kHz) resulted in dramatic changes in imaging technology (next section). We have recently utilized a moderately large (128 × 128 format) array reading out at high frame rates (∼16 000 Hz), (Bhargava, R., Bartick, E. G., Schwartz, R., Peters H. and Levin I. W., Unpublished results) incorporating the fast readout of small arrays with the multichannel advantages of the larger arrays.

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Although the readout rates of the very small linear array and the 16 384 pixel array are nearly the same, the architecture of the larger array results in a significantly higher noise per pixel. Advances in manufacturing technology and the increasing level of sophisticated applications have resulted in a rapidly improving quality of available detectors. We anticipate that improvements in detector technology will result in continuously better noise characteristics at kHz readout rates similar to single-element MCT detectors. Since there is little advantage in detection gained by increasing the modulation frequency of FTIR spectroscopy beyond 1 kHz, we anticipate that the speed of data recording will become less important while the need for lower noise readout electronics will become apparent for the small integration times at these high frame rates.

1.5 Raster scanning with linear array detectors While large format array detectors have greatly impacted the practice of IR microspectroscopy, their developing sophistication and lack of straightforward handling makes it difficult to translate conventional IR spectroscopy to familiar platforms. The utilization of several key features of conventional FTIR spectroscopy, as, for example, a.c.-coupling, high frequency modulation and filtering, have been impossible. Small linear array detectors have been developed as a compromise between the multichannel advantages of the array detectors and the high fidelity features of FTIR spectroscopy. The design principle underlying these detectors is that they may be employed in a manner similar to conventional single-element detectors but that the spatial resolution would be determined by the optics of the system and would not require apertures, thus trading the combined advantages of imaging and rapid-scan spectroscopy against the multichannel detection advantages of large detectors. Individual elements of the linear array detector are smaller than the typical detector employed for mapping; in contrast to mapping, the optics permit a very limited number of magnifications. For example, a commercial system employs a 16-element detector array and records data from specimen areas of either 6.25 μm × 6.25 μm or 25 μm × 25 μm. A 4X optical magnification allows for convenient change between the two available nominal spatial resolutions. Since the number of detectors is small, their uniformity is relatively high and circuitry can be independent. Further, the readout electronics do not need to be miniaturized at the expense of quality and provide, pixel-for-pixel, significantly superior performance compared with large focal plane array detectors. Another commercial implementation utilizes a two-column detector, providing for an increased multichannel detection advantage. Analogous to point mapping systems, linear array detectors require an associated optical microscope and a high precision stage for specimen positioning. The image is built one linear element at a time, while the sample is moved to raster across the entire area of interest; hence, these systems are termed ‘raster scanning’ imaging spectrometers.

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1.5.1 Choice of either small or large detector arrays Small array detectors clearly demonstrated advantages over the prevalent large arrays when they were first commercially introduced. The development led to other vendors adopting the paradigm in small square arrays (16×16) or proposing other rectangular array formats (16 × 1 or 16 × 2). The small detector arrays afford many advantages: simpler rapid-scan interferometry, a.c.-coupling of detection and software and hardware filtering. Since the field of view is limited, the sample must be raster scanned to image a large area. The inefficiency of stepping the stage sequentially to record data is partly offset by the gain in efficiency in Fourier transforming the data during the stepping period. Thus, the rastering time of the stage is offset by the processing time for large format arrays in comparing resulting data in spectral, rather than interferometric, format. Just as point microscopy systems have an advantage over imaging systems in recording data from a single point, smaller linear detectors clearly have an advantage for data acquisition from small spatial areas. Similarly, there is no substitute for large array detectors if data from large sample areas are to be acquired rapidly. The comparison between linear array and two-dimensional array detectors is more interesting when data are acquired from large sample areas and data quality, not time for data acquisition, is a constraining variable. We have utilized a performance metric that is suitable for comparing the imaging performance for large spatial areas of two different systems. The figure of merit we employ is the number of pixels per minute (pixpm) that can be recorded for specified data quality (SNR of absorbance spectra) at defined spectral and spatial resolution, apodization and wavelength bandpass. Since the SNR √ of recorded data can be anticipated to scale with the data acquisition time (as t), the adjustment allows for easy comparisons of detectors with different sizes and performance. Consider the comparison between two detectors in terms of the ratio of their pixpm output as    SNR2 2 n2 t1 (1.12) R21 = n1 t 2 SNR1 where R21 is the ratio of the pixpm for detector 2 to detector 1, ni is the number of pixels measured in a single interferometer sweep, in time ti , by detector i. The SNR of a pixel for the detectors is indicated by SNRi . Hence, the relative performance of the detectors is dictated by a linear dependence on the number of pixels and time for data acquisition but by the square of the SNR. Smaller linear arrays that can rapidly record data and record data at higher SNR due to their various advantages discussed above may overcome the multichannel detection advantage of very large array detectors. The relatively poor SNR of large array detectors arises from both the high noise characteristics of the miniaturized electronics of the array pixels, the lack of modulation advantages of rapid scanning as well as the need to distribute source energy over a wider spatial area on the specimen. Larger arrays are being manufactured and with improvements in FPA electronics making data acquisition faster and increasing the SNR, larger arrays are likely to dominate future instrumentation.

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The comparison in the future will likely be determined by the need to distribute source intensity for larger arrays compared with the need to raster scan for small array detectors.

1.6 Conclusions Fourier transform infrared spectroscopic imaging, utilizing multichannel detectors, has considerably expanded the scope of IR spectrochemical analyses. The challenges of incorporating array detectors in imaging spectrometers have been actively addressed and many configurations now provide high quality data that enable sophisticated molecular analyses with a structural underpinning. The direction in this field is clearly shifting from efforts enabling high fidelity data to be recorded to approaches employing the recorded data for sophisticated analyses. The scope of analyses is being extended further, for example, by the recent development of timeresolved imaging57,58 (Fig. 1.6) and by the use of the ATR configuration59,60 to enhance temporal and spatial resolution capabilities, respectively. We anticipate that the evolving array technology will soon provide both large detectors with frame rates in the range of several tens of kHz leading to, for example, video rate spectroscopic imaging and cheaper arrays that allow for the introduction of imaging systems that (a)

(b)

FPA sampling Droplet bulk FOV average Defect Domain boundary Matrix

0.45

Time (ms)

0.40 I=I(=i, )

0.35

0.30

0.25 10

100 Time (ms)

Figure 1.6 (a) In a time-resolved imaging configuration, the interferogram is obtained by observing the magnitude of signal at a constant retardation in the step-scan mode. Cyclic events are excited by a stimulus and the intensity of the interferogram as a function of time is recorded for every optical retardation. The stimulus and response can be observed in the figure. The characteristic profile of events may be measured by stimulating multiple times to obtain a composite profile that contains a sufficient number of measurements to accurately reproduce the reponse using a multiple-pass approach to accurately record the data. (b) Average absorbance profiles during the relaxation of orientation for the different regions of a composite demonstration differences in the response of dispersed droplets, defects, domain boundaries and the embedding matrix. A nonspatially resolved measurements of the composite would provide a profile similar to the average of the FOV. The best fit lines are superimposed for all except the average of the FOV.

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are only incrementally more expensive than their point mapping counterparts. We also anticipate the introduction of instrumentation in which FTIR imaging is integrated with other forms of microscopy, as, for example, fluorescence techniques, to provide comprehensive, detailed characterizations of complex materials.

References [1] Chalmers, J. M. and Griffiths, P. R. (eds) (2002) Handbook of Vibrational Spectroscopy, John Wiley & Sons, Chichester. [2] Painter, P. C., Coleman, M. M. and Koenig, J. L. (1982) The Theory of Vibrational Spectroscopy and its Application to Polymeric Materials, John Wiley & Sons, New York. [3] Barer, R., Cole, A. R. H. and Thompson, H. W. (1949) Nature 163, 198. [4] Harthcock, M. A. and Atkin, S. C. (1998) Appl. Spectrosc. 42, 3. [5] Kwiatkoski, J. M. and Reffner, J. A. (1987) Nature 328, 837. [6] Reffner, J. A., Martoglio, P. A. and Williams, G. P. (1995) Rev. Sci. Inst. 66, 1298. [7] Jamin, N., Dumas, P., Monicut, J. et al. (1998) Proc. Natl. Acad. Sci. 95, 4837. [8] Wetzel, D. L. and LeVine, S. M. (1999) Science 285, 1224. [9] Chalmers, J. M., Everall, N. J., Hewitson, K. et al. (1998) Analyst 123, 579. [10] Bhargava, R., Wang, S. Q. and Koenig, J. L. (2003) Adv. Polym. Sci. 163, 137. [11] Diem, M., Romeo, M., Boydson-White, S., Miljkovic, M. and Matthaus, C. (2004) Analyst 129, 880. [12] Griffiths, P. R. and de Haseth, J. A. (1986) Fourier Transform Infrared Spectrometry, WileyInterscience, New York. [13] Sakai, H. and Murphy, R. E. (1978) Appl. Opt. 17, 1342. [14] Palmer, R. A., Manning, C. J., Rzepiela, J. A., Widder, J. M. and Chao, J. L. (1989) Appl. Spectrosc. 43, 193. [15] Uhmann, W., Becker, A., Taran C. and Siebert, F. (1991) Appl. Spectrosc. 45, 390. [16] Griffiths, P. R. (1972) Anal. Chem. 44, 1909. [17] Bhargava, R. and Levin, I. W. (2001) Anal. Chem. 73, 5157. [18] Burch, C. R. (1947) Proc. Phys. Soc. 59, 41. [19] A general discussion of sampling techniques for microscopy may be found in Allen, T. J. (1992) Vib. Spectrosc. 3, 217. [20] Augerson, C. C. (1998) Appl. Spectrosc. 52, 1353. [21] Tungol, M. W., Bartick, E. G. and Montaser, A. (1993) Appl. Spectrosc. 47, 1655. [22] Lang, P. L., Katon, J. E., Schiering, D. W. and O’keefe, J. F. (1986) Polym. Mater. Sci. Eng. 54, 381. [23] Harrick, N. J., Milosevic, M. and Berets, S. L. (1991) Appl. Spectrosc. 45, 944. [24] Gentner, J. M. and Wentrup-Byrne, E. (1999) Spectrochim. Acta. A-Mol. Biol. 55, 2281. [25] Lewis, L. and Sommer, A. J. (1999) Appl. Spectrosc. 53, 375. [26] Mirabella, F. M., Jr. (1987) Applications of microscopic Fourier transform infrared spectrophotometry sampling techniques for the analysis of polymer systems. In The Design, Sample Handling and Applications of Infrared Microscopes (P. B. Rousch, ed.), American Society for Testing and Materials, Philadelphia, PA, pp. 74–83. [27] For example, an application of a concept integrating optical microscopy and spectroscopy for microspectroscopic analyses can be found in Norman, M. L., Gagnon, A. M., Reffner, J. A., Schiering, D. W. and Allen, J. D. (2004) Proc. SPIE 5269, 143. [28] Sommer, A. J. and Katon, J. E. (1991) Appl. Spectrosc. 45, 1663. [29] Messerschmidt, R. G. (1987) Photometric considerations on the design, sample handling and applications of infrared microscopes. In The Design, Sample Handling and Applications of Infrared Microscopes (P. B. Rousch, ed.), American Society for Testing and Materials, Philadelphia, PA, pp. 12–26. [30] Sahlin, J. J. and Peppas, N. A. (1997) J. Appl. Polym. Sci. 63, 103. [31] Born, M. and Wolf, E. (1980) Principles of Optics, 6th edn, Pergamon Press, Elmsford, New York. [32] Heimann, P. A. and Urstadt, R. (1990) Appl. Opt. 29, 495.

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[33] Treado, P. J., Levin, I. W. and Lewis, E. N. (1994) Appl. Spectrosc. 48, 607; Lewis, E. N. and Levin, I. W. (1995) Appl. Spectrosc. 49, 672. [34] Bennett, C. L., Carter, M., Fields, D. and Hemandez, J. (1937) Imaging Fourier transform spectrometer. In Proceedings of the Imaging Spectrometry of the Terrestrial Environment, 14–15 April 1993, Orlando, FL, SPIE 191. [35] Dereniak, E. L. and Boreman, G. D. (1996) Infrared Detectors and Systems, John Wiley & Sons, New York. [36] Rogalski, A. (2002) Infrared Phys. Technol. 43, 187. [37] Bhargava, R., Fernandez, D. C., Schaeberle, M. D. and Levin, I. W. (2000) Appl. Spectrosc. 54, 1743. [38] The scattering contribution of the image results in a baseline offset in absorbance spectra, as detailed in Bhargava, R., Wang, S. Q. and Koenig, J. L. (1998) Appl. Spectrosc. 52, 323, which must be corrected for quantitative spectroscopy. [39] Snively, C. M., Katzenberger, S., Oskarsdottir, G. and Lauterbach, J. (1999) Opt. Lett. 24, 1841. [40] Huffman, S. W., Bhargava, R. and Levin, I. W. (2002) Appl. Spectrosc. 56, 965. [41] Snively, C. M. and Koenig, J. L. (1999) Appl. Spectrosc. 53, 170. [42] Bhargava, R. and Levin, I. W. (2001) Anal Chem. 73, 5157. [43] Bhargava, R., Schaeberle, M. D., Fernandez, D. C. and Levin, I. W. (2001) Appl. Spectrosc. 55, 1079. [44] Hirschfeld, T. (1979) Appl. Spectrosc. 33, 525. [45] Bhargava, R., Fernandez, D. C., Schaeberle, M. D. and Levin, I. W. (2001) Appl. Spectrosc. 55, 1580. [46] Bhargava, R. and Levin, I. W. (2002) Anal. Chem. 74, 1429. [47] Bhargava, R., Ribar, T. and Koenig, J. L. (1999) Appl. Spectrosc. 53, 1313. [48] Bhargava, R., Wang, S. Q. and Koenig, J. L. (2000) Appl. Spectrosc. 54, 486. [49] Bhargava, R., Wang, S. Q. and Koenig, J. L. (2000) Appl. Spectrosc. 54, 1690. [50] Rogalski, A. (2003) Prog. Quant. Electron. 27, 59. [51] Lewis, E. N., Gorbach, A. M., Marcott, C. and Levin, I. W. (1996) Appl. Spectrosc. 50, 263. [52] Lewis, E. N., Treado, P. J., Reeder, R. C. et al. (1995) Anal. Chem. 67, 3377. [53] Carter, M., Bennett, C. L., Fields, D. J. and Lee, F. (1995) Proc. SPIE 2480, 380. [54] Lewis, E. N., Kidder L.H., Arens, J. F., Peck, M. C. and Levin, I. W. 1997 Appl. Spectrosc. 51, 563. [55] Kidder, L. H., Levin, I. W., Lewis, E. N., Kleiman, V. D. and Heilweil, E. J. (1997) Opt. Lett. 22, 742. [56] Colarusso, P., Kidder, L. H., Levin, I. W., Fraser, J. C., Arens, J. F. and Lewis, E. N. (1998) Appl. Spectrosc. 52, 106A. [57] Bhargava, R. and Levin, I. W. (2003) Appl. Spectrosc. 57, 357. [58] Bhargava, R. and Levin, I. W. (2003) Macromolecules 36, 92. [59] Sommer, A. J., Tisinger, L. G., Marcott, C. and Story, G. M. (2001) Appl. Spectrosc. 55, 252. [60] Chan, K. L. A. and Kazarian, S. G. (2003) Appl. Spectrosc. 57, 381; Chan, K. L. A., Hammond, S. V. and Kazarian, S. G. (2003) Anal. Chem. 75, 2140.

2

Near-infrared spectral imaging with focal plane array detectors E. Neil Lewis, Linda H. Kidder, Eunah Lee and Kenneth S. Haber

2.1 Background: single-point near-infrared spectroscopy The analytical near-infrared (NIR) region spans the approximate range of 700–2500 nm, where absorptions arising from overtones and combination bands −H, N− −H and C− −H stretching and bending fundamentals are found. The relof O− atively weak absorptivity of these overtones and combination bands compared with the fundamentals in the mid-infrared (MIR) region is one of the primary reasons that NIR spectroscopy has become a workhorse analytical technique in a variety of fields. In the MIR region, the strong absorptions of the fundamental vibrational bands demands rigorous sampling techniques to limit the amount of material interacting with the incoming radiation. The necessity for thin sectioning, KBr pellet preparation or the use of special sampling accessories, such as ATR, implies that the quality of the MIR data will have at least some dependence on the skill of the person preparing the samples or taking the measurement. In the NIR region, where sample absorptivities are 1–2 orders of magnitude less, intact samples can be characterized using transmittance or diffuse reflectance. In contrast to the MIR where thick samples are often impenetrable, NIR excels at characterizing intact agricultural samples, whole pharmaceutical tablets, powders and other native solid state or liquid samples with little to no sample preparation. The strength of NIR absorption bands is not constant across the spectral range, but rather increases from shorter to longer wavelengths, creating an inverse relationship between penetration depth and wavelength. For shorter NIR wavelengths, where absorptivities are relatively weak, the penetration depth of the NIR radiation is significant. As a rule of thumb, these wavelengths are used when increased penetration depth is required and decreased specificity can be tolerated, such as for protein and moisture determination in grain1,2 and noninvasive in vivo clinical imaging.3 Where greater molecular specificity is more important than penetration depth, however, longer wavelengths that probe the first overtones and combinations are preferred. In addition to being significantly less intense, NIR spectral features tend to be broad and overlapped. Historically, this made band assignments more difficult than in the MIR, and slowed the adoption of NIR relative to this technique. Despite these difficulties, however, as early as 1922–9, researchers at UCLA and Johns Hopkins

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studying the NIR spectra of organic compounds were able to assign spectral bands to functional groups.4 The US Bureau of standards also worked on making band assignments,5 and by 1945 enough information had been gathered to make the technique of interest in the chemical industry. Willis in the ICI Plastics division worked in the NIR region in the mid- to late 1940s, initially with the same type of equipment used in the 1920s – a spot galvanometer with photographic recording.5 In the mid-1950s the Grubb–Parsons grating spectrometer became available, and was used widely for industrial analysis in the NIR spectral region.5 A review by Whetsel highlights NIR spectroscopy during this early period.6 The era of NIR spectroscopy from the 1920s through the 1970s has been described as the ‘classical analysis’ period, where the analytical approach was to monitor the behavior and appearance of specific spectral features assigned to specific functional groups. This approach is familiar to practitioners of MIR imaging and Raman vibrational spectroscopy, and has significant merit and applicability for techniques with sharp and specific spectral features. This analytical methodology however, does not draw on the strengths of NIR spectroscopy, and as such, it did not give the technique any distinct advantages over other analytical approaches, and with the advent of high performance liquid chromatography (HPLC) in the 1970s, NIR spectroscopy was ‘temporarily’ displaced as an industrial analytical tool. At about this time, NIR spectroscopy was first being investigated for agricultural applications in seminal work by Karl Norris.7–9 Initial attempts to quantify agricultural samples were based on the analysis of solvent extractions of sample components, an approach that is obviously destructive, and requires significant operator skill. Quickly, however, the merits of NIR spectroscopy as a direct, nondestructive analytical technique were recognized,10 and the development of ‘correlation-based NIR analysis’,5 the methodology familiar to modern practitioners of the technique, took root and blossomed. As testament to the ease of operability, early quantitative analyses of grain samples collected with crude instrumentation operated by unsophisticated users were able to provide results comparable in accuracy to wetchemical methods. In 1975, the Canadian Grain Commission adopted NIR as an approved method for protein quantification, based on the work by Williams.11 The significant change from classical analysis to correlation-based analysis was enabled by a confluence of interrelated advances in hardware, software and applications knowledge. An iterative cycle of instrumental and computational advances, development of sophisticated chemometric algorithms and an increased understanding of applications particularly well suited to the technique worked in concert to further the applicability of the method in the agricultural area. The credibility demonstrated by the establishment of NIR analyses in the agricultural field, with continuing development in instrumentation, software and chemometric approaches, aided the rapid proliferation of NIR spectrochemical analyses to other fields of study. As an example, applications of NIR spectroscopy have expanded rapidly in the pharmaceutical industry in the last decade and it is now considered a standard analytical technique in the industry.12 Additional application areas include fine chemicals and chemical production, food and beverages, textiles, polymer science, biotechnology,

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earth sciences and mineralogy, medicinal and clinical chemistry, petroleum and fuels research, environmental sciences, and paper and pulp. Workman has compiled an extensive review of NIR applications in these areas.13 In summary, NIR spectroscopy is currently a rugged, laboratory and process technique, delivering realtime analyses across a variety of application areas. The limited sample preparation requirements and the development of automated and rapid mathematical analysis enhance the quality of spectrochemical analyses and alleviate the need for analysis by highly trained personnel, making NIR spectroscopy an extremely useful tool.

2.2 Development of NIR spectral imaging In the last 16 years spectral imaging has evolved from a specialized analytical technique employing the point by-point synthesis of spectral maps to a mix of technologies in routine use in industrial, academic and government settings.14–19 Paralleling the development of single-point NIR spectroscopy as described above, imaging instrumentation has become more economically accessible because of iterative advances in hardware components, computational capabilities, electronics and chemometric algorithms. With increased accessibility and capability, the utility of imaging approaches to specific types of sample characterization has become more apparent and potential targets for imaging studies are more easily classified as such.

2.2.1 History of spectral imaging Collecting spatially resolved spectral information has evolved from sequential point mapping approaches to global imaging, and this evolution has been enabled by the availability of two-dimensional (2D) detectors and optical quality wavelength filters. One of the first 2D detectors available to analytical scientists was the CCD, with sensitivity between 180 and 1100 nm. Although developed at about the same time as the CCD, the infrared (IR) sensitive analog, the focal plane array (FPA) detector was not commercially available until much later because its development was driven primarily by strategic military purposes and therefore classified. Even before 2D detectors had been developed though, IR spectroscopists were looking for means to collect spatially localized spectra. In 1945, Barer et al. published results from the first IR microscope.20 The next significant step toward collecting IR spectral images or maps took place over forty years later in 1988 when Harthcock and Atkin published a description of a sequential point mapping approach using a traditional single-detector element, and scanning the sample point by point.21 The development of IR microscopy and the advantages of multichannel detectors is reviewed further in Chapter 1. The introduction of 2D detectors, starting with CCDs, into the scientific sector enabled the collection of spatially resolved spectral information simultaneously rather than via this point-by-point approach. The earliest work employed silicon

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CCDs with spectral response to 1100 nm, either coupled with narrowband interference filters providing multispectral capability22 or with optical quality wavelength filters to perform hyperspectral imaging.23 This latter implementation employed a silicon CCD and acousto-optic tunable filter (AOTF) to record NIR spectra and images in the visible and short wavelength NIR region, from 800 to 1100 nm. The wavelength range was subsequently extended to 1900 nm using a monochromator and NIR sensitive camera.24 The first laboratory-based NIR spectral imaging instrument to encompass the entire NIR spectral range (from 1000 to 2500 nm) coupled a 128 × 128 indium antimonide (InSb) FPA to an AOTF.25 Laboratory-based MIR imaging implementations employing tunable dielectric filters and step-scan interferometers as wavelength selection devices were developed at about the same time.26,27 The first commercially viable, laboratory-based Fourier transform infrared (FTIR) spectral imaging measurements were performed with a 128 × 128 pixel InSb detector optically windowed from 3 to 5 μm and a step-scan FTIR spectrometer.28 Within a year of the publication of the first experimental results using a step-scan interferometer, a commercial unit was available (Bio-Rad Stingray, now Digilab/Varian). As with single-point NIR spectroscopy, NIR spectral imaging has been commercialized relatively slowly, despite being demonstrated earlier.25 Perhaps because NIR spectroscopy was traditionally utilized more for bulk identification or as a process stream monitor, the potential applications for an imaging implementation were not immediately apparent. Its utility did not go entirely unnoticed though, and several government and academic researchers were actively engaged in the development of NIR spectral imaging instrumentation.29–31

2.2.2 FPAs – specifications There are a variety of FPA detectors available that are sensitive in the NIR spectral region. The optimal choice of detectors depends on several factors: desired wavelength range, whether the application will be laboratory based or part of a process environment, the sensitivity needed to adequately differentiate sample spectra and price. The figure of merit most often used to describe detector performance is specific detectivity or D ∗ , which is the inverse of noise equivalent power (NEP), normalized for detector area and unit bandwidth. NEP is defined as the radiant power that produces a signal-to-dark-current noise ratio of unity. Indium gallium arsenide (InGaAs) arrays have a combination of attributes that make them very desirable for short wavelength applications. Detectors based on these arrays have the highest detectivity (D ∗ = ∼6 × 1012 cm Hz0.5 W−1 ) and operating temperature of available arrays. They require only modest thermoelectric cooling, making them particularly well suited for on-line or at-line settings as they do not require liquid nitrogen or other more expensive cooling implementations. These characteristics make them some of the least expensive detectors operative in this wavelength range. Arrays in the 320×256 format are abundant and economical, and even relatively large arrays (640 × 512) are readily available. There is no perfect solution, of course, and the wavelength range across which standard InGaAs is

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responsive does not cover the entire NIR spectral range, but only ranges from ∼0.9 to 1.7 μm. There have been efforts to develop an extended range InGaAs array, but these prototypes have not yet demonstrated sufficiently satisfactory performance for spectral imaging applications. Indium antimonide (InSb) array detectors extend to cover the long wavelength NIR spectral range, and are sensitive from ∼1.0 to 5.4 μm wavelength range. Although InSb detectors have higher detectivity (D ∗ > 4×1011 cm Hz0.5 W−1 ) than platinum–silicide (PtSi), lead-sulfide (PbS) or mercury–cadmium–telluride (MCT) arrays, they must be cooled to liquid nitrogen temperatures or below. Stirling coolers can be used on InSb cameras for applications where liquid nitrogen cooling would be inconvenient. Because their sensitivity increases out to 5 μm, use in the short wave IR requires the use of an optical filter that blocks the longer wavelength light, a so-called cold filter. Large arrays, up to 1024 × 1024, have been fabricated, but are prohibitively expensive for commercial implementations. The bandgap and therefore the wavelength response of MCT detectors can be tuned by adjusting the ratio of mercury vacancy sites to cadmium donor sites according to the following stoichiometry – Hg1−x Cdx Te. A common doping scheme results in a detector that is responsive between ∼0.8 and 2.5 μm, with some of the highest detectivities for MCT achieved in this spectral range (D ∗ ∼ = 3 × 1011 cm Hz0.5 W−1 ). Cooling requirements are not as severe as for InSb arrays, and the shorter wave cutoff makes a cold filter unnecessary. Large format arrays can be made from this material. PtSi arrays are sensitive over the 1.0–5.0 μm wavelength range. These are silicon-based arrays in which the platinum–silicon junction in each pixel forms a Schottky barrier diode. Large, exceptionally uniform arrays can be made from this material, but the quantum efficiency is 1.9 would indicate contamination with a probability of error of 0.025 (red number). This statistical test is one of many and provides an example of how spectroscopic imaging data can be analyzed and used to group image pixels with a degree of confidence.

4.3.2 Concentration image maps The final goal of most image processing is to produce concentration maps of each component of the heterogeneous mixture. This is very difficult without at least a limited amount of preprocessing, which was discussed in Section 4.2. The idea of producing concentration maps is to extract pure component spectra from the multitude of spectra obtained with the image and then to find the concentration of each component across the image. These methods are generally referred to as self-modeling.1–19 Most of the currently accepted methods try to find good initial guesses for the spectra of the individual components and then refine these initial

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