The Analytical Process

Cahbration is an important focus in analytical chemistry. It is the process that relates instmment responses to chemical concentrations. It consists of two basic steps estimation of the cahbration model parameters, and then prediction for new samples of unknown concentration. Cahbration refers to the step of the analytical process in Figure 2 where measurements are related to concentrations of chemical species or other chemical information. 

Optimization lefeis to the step in the analytical process (Fig. 2) where some sort of treatment is performed on samples to generate taw data which can be in the form of voltages, currents, or other analytical signals. These data have yet to be caUbrated in terms of chemical concentrations. 

Due to their wide range of analytical challenges centralized analytical laboratories are required to adopt a series of QM systems simultaneously. For example, the Competence Center Analytics of BASF AG in Ludwigshafen is certified and accredited to operate under four different QM systems. Undoubtedly, QM systems play a vital role in a modern industrial analytical laboratory. The sale of many products of the chemical industry is not possible without a GLP-certified analytical laboratory. However, in practical tenus the different QM systems can potentially reduce the efficiency of the analytical process and lead to increased costs. 

The presentation will focus on the differences and similarities of these systems as well as problems encountered in their practical use. By looking at the analytical process chain characteristics, such as the reliability and traceability of data, documentation standards and total costs of QM are discussed and evaluated. Suggestions for harmonization of QM-Systems and reduction of bureaucracy will be made, resulting in an improvement of the overall practical applicability and cost reduction of QM. 

We need to transition from quasi-computerized methods, in which the different elements of the analytical process are treated as discrete, paper report tasks, to a comprehensive informatics approach, in which the entire data collection and analysis is considered as a single reusable, extensible, auditable, and reproducible system. Informatics can be defined as the science of storing, manipulating, analyzing, and visualizing information using computer systems. [3]... 

Traditional analytical methods make extensive use of computers, but typically these methods still require constant restructuring of the data and multiple analytical tools. This endless restructuring wastes time and productivity and also makes the analytical processes difficult to document, audit, and reproduce in real time. This situation also makes it difficult to reconstruct and update analyses in real time when new adverse event data become available or when new questions need to be asked. The application of comprehensive data standards allows the use of integrated, reusable software for analyzing adverse event data. This integration facilitates the reproducibility of the results. 

Internal Standards. A compound selected as an internal standard ideally should behave in a manner identical to that of the analyte in all separation steps in the analytical process and should be measured by the same final determination method. Distillation from aqueous systems and solvent partition are the... 

In Section 42.2 we have discussed that queuing theory may provide a good qualitative picture of the behaviour of queues in an analytical laboratory. However the analytical process is too complex to obtain good quantitative predictions. As this was also true for queuing problems in other fields, another branch of Operations Research, called Discrete Event Simulation emerged. The basic principle of discrete event simulation is to generate sample arrivals. Each sample is characterized by a number of descriptors, e.g. one of those descriptors is the analysis time. In the jargon of simulation software, a sample is an object, with a number of attributes (e.g. analysis time) and associated values (e.g. 30 min). Other objects are e.g. instruments and analysts. A possible attribute is a list of the analytical... 

Each chemist working analytically uses (sometimes without any awareness) the analytical process, a scheme (see Figure 1) by which most analytical problems are assessed. The analytical process is a multi-step approach to solving questions by analytical chemistry and includes the following steps ... 

Answer the questions and solve the problem posed at the beginning of the analytical process. 

The above leads to the concept of the quality of an analytical method (not to be confused with the quality of the results). A method should be as simple or as sophisticated as necessary to serve its purpose in yielding reliable results and in answering the questions posed at the beginning of the analytical process. 

All aspects in the analytical process are equally important, and each step should be isolated in method development experiments and/or validation to ensure acceptable quality of results. A good way to evaluate robustness of a method is to alter parameters (e.g., solvent volumes, temperature, pH, sources of reagents) of each step to determine... 

High speed analysis requirements which influence sample preparation demands (being the rate determining step of the analytical process). 

Analyte stability is another source of concern the chemical identity should be preserved all through the analytical process. This is not always guaranteed. Analytes may be oxidised or hydrolysed during extraction. Compound integrity can also suffer in the chromatographic column, through heat-induced decay. Extraction... 

In contrast to classical analysis, the concept of modern analytical chemistry has changed in so far as the problem that has to be solved is included in the analytical process. The analytical chemist is considered as a problem solver (Lucchesi [1980]) and the concept is represented in the form of the analytical trinity (Betteridge [1976]) as shown in Fig. 1.2. 

Today, analytical chemistry has such a wide variety of methods and techniques at its disposal that the search for general fundamentals seems to be very difficult. But independent from the concrete chemical, physical and technical basis on which analytical methods work, all the methods do have one principle in common, namely the extraction of information from samples by the generation, processing, calibration, and evaluation of signals according to the logical steps of the analytical process. 

Analytical chemistry is a problem-solving science. Independent from the concrete analytical method, the course of action, called analytical process, is always very similar. The analytical process starts with the analytical question on the subject of investigation and forms a closed chain to the answer to the problem. Using a proper sampling technique a test sample is taken that is adequately prepared and then measured. The measured data are evaluated on the basis of a correct calibration and then interpreted with regard to the object under study. 

The analytical process in the broader sense is represented in Fig. 2.1. Frequently - in a stricter sense - only the lower part (grey background) from sample through to information (or essential parts from it) are regarded as representing the analytical process. 

The lower part of the analytical process (grey) is - mostly merged as a black box - sometimes known as the chemical measurement process (CMP) see Currie [1985, 1995, 1999]. 

The analytical process in the stricter sense or chemical measurement process, respectively, has a conspicuous similarity with the general information process which is shown in Fig. 2.2. 

Interference plays an analogous role in the course of the analytical process as well as noise, which is mainly manifested by random deviations. 

In some cases the object under study has to be continuously monitored. Then on-line analytical methods are applied by which the system can be directly measured. The analytical process then runs without sampling and sample preparation, as can be seen in Fig. 2.3a. The analytical process is shortened even more in the case of in-line analysis where measurement and... 

Fig. 2.3. The analytical process in case of on-line analysis (a) and in-line analysis (b)... 

In the following, the stages of the analytical process will be dealt with in some detail, viz. sampling principles, sample preparation, principles of analytical measurement, and analytical evaluation. Because of their significance, the stages signal generation, calibration, statistical evaluation, and data interpretation will be treated in separate chapters. 

Sampling is a crucial step of the analytical process, particularly in cases where there are large differences between the material under investigation and the test sample (laboratory sample) with regard to both amounts and properties, especially grain size, fluctuations of quality and inhomogeneities. 

In principle, all measurements are subject to random scattering. Additionally measurements can be affected by systematic deviations. Therefore, the uncertainty of each measurement and measured result has to be evaluated with regard to the aim of the analytical investigation. The uncertainty of a final analytical result is composed of the uncertainties of all the steps of the analytical process and is expressed either in the way of classical statistics by the addition of variances... 

The analytical process is a procedure of gaining information. At first, samples contain only latent information on the composition and structure, namely by their intrinsic properties (Malissa [1984] Eckschlager and Danzer [1994]). By interactions between the sample and the measuring system this information is transformed step by step into signals, measured results and useful chemical information. 

It is not always possible to tell strictly the difference between random and systematic deviations, especially as the latter are defined by random errors. The total deviation of an analytical measurement, frequently called the total analytical error , is, according to the law of error propagation, composed of deviations resulting from the measurement as well as from other steps of the analytical process (see Chap. 2). These uncertainties include both random and systematic deviations, as a rule. 

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