Preface

Why an Analyst’s Companion? Millions of analyses are carried out every day in laboratories for all sectors of industry and science. Many people are willing to pay for these analyses because they are considered effective in making a scientifically sound decision. Though few publications address the economics of analytical sciences, nonetheless, a report by the European Commission concluded in 2002 that “for every euro devoted to measurement activity, nearly three euros are generated” [1]. But is it easy and simple to use an analytical result, and does it always allow you to make the right decision? Some questions illustrate the risks involved in relying on a result:

1. – How do you know that the laboratory used the method that gave the exact result?
2. – Like any measurement, analysis is subject to error. How can you estimate them?
3. – How can a spurious measurement be used effectively?

This is the right time to explain why and how the concept of measurement uncertainty (MU) can be used to better manage these risks. This also means that a new challenge for analysts is to develop an appropriate method for estimating MU more explicitly applicable to analytical sciences. In this perspective, a tool based on the statistical dispersion intervals called method accuracy profile (MAP) is proposed as the backbone of the book. The theoretical aspects of the MAP procedure and MU estimation are presented in several examples and template worksheets to help analysts quickly grasp this tool.

At the turn of the 1970s, three analytical chemists, Bruce Kowalski, Luc Massart and Svante Wold, conceptualized a discipline they called Chemometrics [2]. Unfortunately, they all have passed away since, but their work is still vivid. Many chemometrics books have been published, proving the added value of statistics to analytical sciences. Some are globally addressing chemometrics [3–5] other are more focused on statistics [6, 7], and others on method validation [8, 9].

This book contributes to the application of chemometrics, but the obvious aim is not to repeat what is available in many valuable publications. Only a few books precisely address measurement uncertainty in analytical sciences [10–12]. They present limited facets and do not propose a more comprehensive approach. The aim of this book is to describe a global procedure for MU estimation, easily applicable in analytical laboratories. In a recent publication, we have exposed in a condensed manner our view of the link between validation and measurement uncertainty [13]. This book develops more extensively and practically our viewpoint.

However, it is not satisfactory to simply propose a modus operandi (even if it is claimed to be universal) for estimating MU when this parameter is still new in analytical sciences and not always well identified by end‐users. Therefore, several chapters are dedicated to its practical use in decision‐making, demonstrating its advantages. These remarks indicate that this book is primarily intended for professional analysts, although researchers and students may find it of interest.

In order to reach this goal, the book is organized around practical responses covering three major questions daily put to analysts when they develop a new method or routinely apply it to unknown samples:

1. – How to quantify the analyte?
2. – How to validate the method?
3. – How to estimate the measurement uncertainty?

How does this book give answers these questions? We use as a roadmap a tool based on the application of statistical dispersion intervals called MAP. The latter was initially conceived for method validation, but it can easily be used for MU estimation. While method validation is often reduced to computing a set of disconnected parameters to be estimated, the MAP approach is more global. It consists in defining the interval where the method is able to produce a given proportion of acceptable results. This perspective is in harmony with the uncertainty approach proposed by metrologists some decades ago that consists in computing the so‐called coverage interval of the result.

The chapters of the book can be read independently. This may explain some redundancies in the quoted publications. But they are structured according to a reading thread illustrated in Figure 1. The thick grey arrow is the backbone. Six main chapters are characterized as rounded angle boxes. Three of them are devoted to measurement uncertainty, as it is a key issue of the book.

Figure 1 How to read this book.

Additional chapters appear as ellipses. They bring two kinds of information. On the one hand, theoretical background, such as precision and trueness parameter estimation and how to compute them, may be useful to better understand statistical developments involved in the method accuracy profile. On the other hand, specific examples of MU applications. One is devoted to the limits of quantification and the challenging question of controlling samples with low analyte concentration, another to method comparison.

Several data sets provide the link between the different chapters. They are used throughout for practical data handling and real software application. The aim of this data‐oriented presentation is to help the analyst apply the proposed techniques in the laboratory, in keeping with the title “Companion.” This also practicality means that numerical applications for all topics covered are presented and illustrated alongside the theoretical considerations. These are based on detailed Microsoft Excel® worksheets or free equivalent, such as OpenOffice® Calc, included with the book. This software is user‐friendly and does not require much explanation, and probably everyone in the laboratory knows how to use it. Although criticized by professional statisticians (for good reasons), this software is extremely helpful for quick and simple statistical computation in a laboratory, and several pitfalls can easily be avoided:

1. – Worksheet cell content is easily modified without any warning. Thus, once created and validated, the best initiative is to protect the worksheet or whole workbook.
2. – The formula inside cell is not visible unless the option to show formulas is on. To help the understanding of the template worksheets developed for this book, all formulas are made visible in the cell next to the resulting. The built‐in function FORMULATEXT is used for this aim. It is only available in the most recent Excel releases.
3. – Confusion may exist between a worksheet and a text editor. Fancy presentation must be avoided, and it is better to embed a worksheet within a text editor rather than trying to do everything with a single software.

The basic use of worksheet software does not allow complex statistical calculation though it contains many built‐in functions, which are used in the following examples. It is possible to use the development environment called Visual Basic for Applications coming with Excel to build more complex programs, but it requires some practice. For the most sophisticated applications, we preferred to provide Python program examples. This software is increasingly popular, and the accuracy of statistical functions is widely recognized. For instance, complex techniques, such as non‐linear or weighted regression techniques, are easily implemented. Python is simpler than professional statistical software. It is developed under a free license, and there is an exceptionally large community of users who can help. The drawback is that it is a patchwork, and many additional modules must be imported to apply some methods. The simplest way to install Python is to download a free package called Anaconda [14] and select the Spyder development environment. Presented examples were programmed in this environment.

References

1. 1 G. Williams (2002). The assessment of the economic role of measurements and testing in modern society. European Measurement Project, Pembroke College, University of Oxford.
2. 2 Wold, S. and Sjöström, M. (1998). Chemometrics, present and future success. Chemometrics and Intelligent Laboratory Systems 44: 3–14.
3. 3 Kowalski, B.R. (1984). Chemometrics: Mathematics and Statistics in Chemistry. Dordrecht: Springer.
4. 4 Massart, D.L. (1997). Handbook of Chemometrics and Qualimetrics Part A. Amsterdam: Elsevier.
5. 5 Vandeginste, B.G.M., Massart, D.L., Buydens, L.M.C. et al. (1998). Handbook of Chemometrics and Qualimetrics Part B. Amsterdam: Elsevier.
6. 6 Ellison, S.L.R., Barwick, V.J., and Farrant, T.J.D. (2009). Practical Statistics for the Analytical Scientist a Bench Guide, 2e. Middlesex: LGC.
7. 7 Miller, J.N., Miller, J.C., and Miller, R.D. (2018). Statistics and Chemometrics for Analytical Chemistry, 6e. England: Pearson Education Limited.
8. 8 Ermer, J. and Miller, J.H.M.B. (2006). Method Validation in Pharmaceutical Analysis. Weinheim: Wiley‐VCH Verlag GmBH.
9. 9 Swartz, M.E. and Krull, I.S. (2012). Handbook of Analytical Validation. Boca Raton, FL: CRC Press.
10. 10 De Bièvre, P. and Günzler, H. (2013). Measurement Uncertainty in Chemical Analysis. Berlin, Heidelberg: Springer.
11. 11 Bulska, E. (2018). Metrology in...