Quantification, Validation and Uncertainty in Analytical Sciences
Companion guide explaining all processes in measuring uncertainty in quantitative analytical results
Quantification, Validation and Uncertainty in Analytical Sciences provides basic and expert knowledge by building on the sequence of operations starting from the quantification in analytical sciences by defining the analyte and linking it to the calibration function. Proposing a comprehensive approach to MU (Measurement Uncertainty) estimation, it empowers the reader to apply Method Accuracy Profile (MAP) efficiently as a statistical tool in measuring uncertainty.
The text elucidates several examples and template worksheets explaining the theoretical aspects of the procedure and includes novel method validation procedures that can accurately estimate the data obtained in measurements. It also enables the reader to provide practical insights to improve decision making by accurately evaluating and comparing different analytical methods.
Brings together an interdisciplinary approach with statistical tools and algorithms applied in analytical chemistry and written by two international experts with long-standing experience in the field of Analytical measurements and Uncertainty, Quantification, Validation and Uncertainty in Analytical Sciences includes information on:
The know-how of methods in an analytical laboratory, effective usage of a spurious measurement and methods to estimate errors. Quantification, calibration, precision, trueness, MAP addons, estimating MU for analytical sciences, and uncertainty functions Employing measurement uncertainty, sampling uncertainty, quantification limits, and sample conformity assessment Decision making, uncertainty and standard addition method, and accuracy profile for method comparison
Quantification, Validation and Uncertainty in Analytical Sciences is an ideal resource for every individual quantifying or studying analytes. With several chapters dedicated to MU’s practical use in decision making demonstrating its advantages, the book is primarily intended for professional analysts, although researchers and students will also find it of interest.
List of Figures xi List of Resources xv Preface xvii Glossary of Symbols xxi Acknowledgments xxiii 1 Quantification 1 1.1 Define the Measurand (Analyte) 1 1.2 Calibration Modes 9 1.3 External Calibration (EC) 10 1.4 In-sample Calibration (ISC) 15 1.5 Some New Quantification Techniques 17 2 Calibration 25 2.1 Direct and Inverse Calibration 25 2.2 Least-squares Regression Method 28 2.3 Software Implementation 34 2.4 Calibration: Special Topics 41 2.5 Metrological Approach to Calibration 51 3 Precision 59 3.1 Outputs of Interlaboratory Studies 59 3.2 Analysis of Variance (ANOVA) 64 3.3 Balanced and Unbalanced Experimental Design 71 3.4 Software Implementation 72 4 Trueness 81 4.1 Trueness and True Value 81 4.2 Assessment of Trueness 86 4.3 Proficiency Testing 89 4.4 Control Charts 99 5 Method Validation 105 5.1 Review of Validation Procedures 105 5.2 Method Accuracy Profile (MAP) 113 5.3 Statistical Dispersion Intervals 122 5.4 Accuracy Profile: Special Topics 131 6 Measurement Uncertainty (MU) 149 6.1 Principle of Measurement Uncertainty 149 6.2 General Procedure to Estimating MU 150 6.3 Traceability at the International System of Units 152 6.4 Stage 1. Specify the Measurand 154 6.5 Stage 2. Identify Uncertainty Components 157 6.6 Stage 3. Quantify Uncertainty Sources 158 6.7 Stage 4. Calculate Combined Uncertainty 161 6.8 Calculate Expanded Uncertainty 169 6.9 Round the Result 170 6.10 Accuracy, Total Error, and Uncertainty 171 6.11 Insights on Probability 174 7 Measurement Uncertainty in Analytical Sciences 179 7.1 Published Procedures: An Evaluation 179 7.2 Use Method Accuracy Profile Data 181 7.3 Use Control Charts Data 191 7.4 Use Interlaboratory Comparison Data 200 7.5 Uncertainty Functions 203 7.6 Concept of Coverage Interval 211 8 Measurement Uncertainty and Decision 221 8.1 Framework for Decision-Making 221 8.2 Sample Conformity Assessment 227 8.3 Sampling Uncertainty 232 8.4 Measurement Uncertainty: Special Issues 240 9 MU and Quantification Limits 257 9.1 Definitions and Assessment of LOQ 258 9.2 LOQ as an Expected Relative Uncertainty 260 9.3 Decision Limit and Detection Capability 262 10 Examples of MU Application 271 10.1 Standard Addition Method and Drug Quality 271 10.2 Method Comparison Using Uncertainty 283 11 Conclusions 289 11.1 Role of the Number of Replicates 290 11.2 Traceability to International Units 290 11.3 Education about Uncertainty 292 11.4 Risk Analysis 292 11.5 Harmonization of MU Estimation Procedures 293 Annexes 295 The 10-step MAP Procedure 295 Glossary of Used Terms 295 Acronyms 302 Reference 304 Index 305
Max Feinberg has served as a research director at the National Institute of Agricultural Research (INRA). He has organized and chaired various congresses, served on the editorial boards of several journals, and coordinated various European research projects. Serge Rudaz is full professor at the school of pharmaceutical sciences from the University of Geneva, Switzerland. He is a specialist in the analysis of low molecular weight compounds in biological matrices and an expert in method validation.
