Process Analytical Technology Edited by Katherine A. Bakeev Process Analytical Technology Process Analytical Technology Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries Edited by Katherine A. Bakeev (cid:2)2005byBlackwellPublishingLtd EditorialOffices: BlackwellPublishingLtd,9600GarsingtonRoad,OxfordOX42DQ,UK Tel:+44(0)1865776868 BlackwellPublishingProfessional,2121StateAvenue,Ames,Iowa50014–8300,USA Tel:+15152920140 BlackwellPublishingAsiaPtyLtd,550SwanstonStreet,Carlton,Victoria3053,Australia Tel:+61(0)383591011 TherightoftheAuthortobeidentifiedastheAuthorofthisWorkhasbeenassertedin accordancewiththeCopyright,DesignsandPatentsAct1988. Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrieval system,ortransmitted,inanyformorbyanymeans,electronic,mechanical,photocopying, recordingorotherwise,exceptaspermittedbytheUKCopyright,DesignsandPatentsAct1988, withoutthepriorpermissionofthepublisher. Firstpublished2005 LibraryofCongressCataloging-in-PublicationData Processanalyticaltechnology/editedbyKatherineA.Bakeev. p. cm. Includesbibliographicalreferencesandindex. ISBN-10:1–4051–2103–3(acid-freepaper) ISBN-13:978–1–4051–2103–3(acid-freepaper) 1. Chemicalprocesscontrol—Industrialapplications. 2. Chemistry,Technical. 3. Chemistry, Analytic—Technologicalinnovations. 4. Chemistry,Analytic—Technique. 5. Spectrum analysis. 6. Pharmaceuticalchemistry. I. Bakeev,KatherineA. 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ForfurtherinformationonBlackwellPublishing,visitourwebsite: www.blackwellpublishing.com Contents Contributors xiii Preface xv List of Abbreviations xvii 1 ProcessAnalytical Chemistry:Introduction and Historical Perspective ErnestBaughman 1 1.1 Historicalperspective 2 1.2 Early instrument development 4 1.3 Sampling systems 7 1.4 Examples 8 References 11 2 Implementationof Process AnalyticalTechnologies RobertGuenardand Gert Thurau 13 2.1 Introduction toimplementation ofprocess analytical technologies (PATs) in theindustrial setting 13 2.1.1 Definition ofprocess analytics 14 2.1.2 Differences betweenprocess analyzersand laboratory analysis 14 2.1.3 General industrialdriversfor process analytics 15 2.1.4 Types of applications (R&D vs. Manufacturing) 16 2.1.5 Organizationalconsiderations 17 2.2 Generalized process analytics work process 20 2.2.1 Project identification and definition 22 2.2.2 Analytical application development 24 2.2.3 Design,specifyand procure 24 2.2.4 Implementationin production 26 2.2.5 Routine operation 27 2.2.6 Continuous improvement 28 vi Contents 2.3 Differences between implementationin chemical and pharmaceutical industries 29 2.3.1 Introduction 29 2.3.2 Business model 29 2.3.3 Technical differences 30 2.3.4 Regulatory differences 32 2.4 Conclusions 37 References 37 3 Near-InfraredSpectroscopy for ProcessAnalytical Chemistry: Theory, Technologyand Implementation Michael B.Simpson 39 3.1 Introduction 39 3.2 Theory of near-infraredspectroscopy 44 3.2.1 Molecular vibrations 44 3.2.2 Anharmonicity ofthe potentialwell 45 3.2.3 Combination and overtoneabsorptions in the near-infrared 47 3.2.4 Examples of useful near-infrared absorption bands 48 3.3 Analyser technologies inthe near-infrared 51 3.3.1 The scanninggrating monochromator 51 3.3.2 Light sources and detectors for near-infrared analysers 55 3.3.3 The polychromator photodiode-array analyser 62 3.3.4 The acousto-optic tunable(AOTF) analyser 63 3.3.5 Fourier transform near-infrared analysers 69 3.4 The samplinginterface 77 3.4.1 Introduction 77 3.4.2 Further discussion of samplingissues 84 3.4.3 The use of fibre-optics 86 3.5 Conclusion 88 Bibliography 89 4 Infrared Spectroscopy for ProcessAnalytical Applications John P. Coates 91 Abstract 91 4.1 Introduction 92 4.2 Basic IRspectroscopy 95 4.3 Instrumentation designand technology 97 4.4 ProcessIR instrumentation 100 4.4.1 Commercially available IR instruments 101 4.4.2 Important IRcomponent technologies 108 4.4.3 Newtechnologies for IRcomponents andinstruments 112 4.4.4 Requirementsfor processinfrared analyzers 114 4.4.5 Samplehandling for IR process analyzers 121 4.4.6 Issues for consideration in theimplementation of process IR 124 Contents vii 4.5 Applications of process IRanalyzers 126 4.6 Process IRanalyzers: Areview 127 4.7 Trends anddirections 129 References 130 5 ProcessRaman Spectroscopy Nancy L. Jestel 133 5.1 How Raman spectroscopyworks 133 5.2 When Raman spectroscopy works well andwhen itdoes not 136 5.2.1 Advantages 136 5.2.2 Disadvantages andrisks 138 5.3 What are the special designissues for process Raman instruments? 140 5.3.1 Safety 141 5.3.2 Laserwavelength selection 142 5.3.3 Laserpowerand stability 142 5.3.4 Sampleinterface/probes 143 5.3.5 Spectrometer 144 5.3.6 Communications 146 5.3.7 Maintenance 147 5.4 Where Raman spectroscopy is being used 147 5.4.1 Reaction monitoring 147 5.4.2 In-process aid or quality-monitoring tool 155 5.4.3 Product properties 161 5.4.4 Mobileor field uses 161 5.5 What is thecurrent state of Raman spectroscopy? 161 5.5.1 Publication reluctance 162 5.5.2 Technique maturity and long-term performance 163 5.5.3 Lack of widespreadknowledge andexperience 163 References 163 6 UV-Vis for On-Line Analysis Lewis C. Baylor and Patrick E.O’Rourke 170 6.1 Introduction 170 6.2 Theory 171 6.2.1 Chemical concentration 171 6.2.2 Color 172 6.2.3 Film thickness 173 6.3 Instrumentation 173 6.4 Sampleinterface 174 6.4.1 Cuvette/vial 174 6.4.2 Flow cells 175 6.4.3 Insertion probe 176 6.4.4 Reflectance probe 177 viii Contents 6.5 Acomplete process analyzer 177 6.6 Applications 178 6.6.1 Gas analysis – toluene 178 6.6.2 Liquid analysis– nickel 180 6.6.3 Solid analysis – extruded plasticcolor 181 6.6.4 Film thickness–polymer 182 6.6.5 Dissolution testing 183 6.6.6 Liquid analysis– vessel cleaning 185 References 186 7 Near-InfraredChemical Imaging as a Process Analytical Tool E. NeilLewis, Joseph W. Schoppelrei, Eunah Lee, and Linda H. Kidder 187 7.1 The processanalytical technology(PAT) initiative 187 7.2 The role of near-infraredchemical imaging (NIR-CI) in thepharmaceutical industry 188 7.2.1 Characterizationof solid dosage forms 188 7.2.2 ‘Apicture is worth a thousand words’ 189 7.3 The development ofimaging spectroscopy 190 7.3.1 Spatially resolved spectroscopy – mapping 190 7.3.2 The infrared focal-plane array (FPA) 190 7.3.3 Wavelength selection 191 7.3.4 The benefits ofNIRspectroscopy 191 7.3.5 NIRimaginginstrumentation 192 7.4 Chemical imagingprinciples 194 7.4.1 The hypercube 195 7.4.2 Data analysis 196 7.4.3 Spectral correction 197 7.4.4 Spectral pre-processing 197 7.4.5 Classification 198 7.4.6 Imageprocessing 200 7.5 PAT applications 201 7.5.1 ‘Self-calibrating’ high-throughput content uniformity measurements 201 7.5.2 High-throughput applications: Counterfeit screening/quality assurance 204 7.5.3 Defeating sampledilution: Finding theneedle in the haystack 206 7.5.4 Advanced dosage delivery systems 209 7.6 Processing case study one: Estimating ‘abundance’ofsample components 210 7.6.1 Experimental 211 7.6.2 Spectral correction andpre-processing 211 7.6.3 Analysis 211 7.6.4 Conclusions 217 Contents ix 7.7 Processing casestudytwo:Determining blend homogeneity through statistical analysis 217 7.7.1 Experimental 218 7.7.2 Observing visual contrast in theimage 219 7.7.3 Statistical analysis of theimage 219 7.7.4 Blenduniformity measurement 221 7.7.5 Conclusions 222 7.8 Final thoughts 223 Acknowledgements 223 References 223 8 Chemometrics in Process AnalyticalChemistry Charles E.Miller 226 8.1 Introduction 226 8.1.1 Whatis chemometrics? 226 8.1.2 Whatdoesit doforanalytical chemistry? 227 8.1.3 Whatabout process analytical chemistry? 228 8.1.4 Some history 228 8.1.5 Some philosophy 229 8.2 The building blocks of chemometrics 230 8.2.1 Notation 230 8.2.2 A bit of statistics 231 8.2.3 Linear regression 233 8.2.4 Multiplelinear regression (MLR) 236 8.2.5 Datapre-treatment 237 8.2.6 Datacompression 243 8.2.7 Spatial sample representation 249 8.2.8 Experimental design 250 8.3 Quantitative model building 254 8.3.1 ‘Inverse’multiplelinear regression 254 8.3.2 Classicalleast squares (CLS) 257 8.3.3 Principal component regression(PCR) 259 8.3.4 Projection to latent structures (PLS)regression 262 8.3.5 Artificial neural networks(ANN) 264 8.3.6 Other quantitative model-building tools 267 8.3.7 Overfitting and model validation 267 8.3.8 Improving quantitative model performance 274 8.4 Outliers 277 8.4.1 Whatarethey, and why should wecare? 277 8.4.2 Outliers in calibration 277 8.4.3 Outliers in prediction 283 8.5 Qualitativemodelbuilding 285 8.5.1 Spacedefinition 286 8.5.2 Measures ofdistance in thespace 287