Error, analytical laboratory

It is important to understand that this material will not be presented in a theoretical vacuum. Instead, it will be presented in a particular context, consistent with the majority of the author s experience, namely the development of calibrations in an industrial setting. We will focus on working with the types of data, noise, nonlinearities, and other sources of error, as well as the requirements for accuracy, reliability, and robustness typically encountered in industrial analytical laboratories and process analyzers. Since some of the advantages, tradeoffs, and limitations of these methods can be data and/or application dependent, the guidance In this book may sometimes differ from the guidance offered in the general literature. 

Of all the requirements that have to be fulfilled by a manufacturer, starting with responsibilities and reporting relationships, warehousing practices, service contract policies, airhandUng equipment, etc., only a few of those will be touched upon here that directly relate to the analytical laboratory. Key phrases are underlined or are in italics Acceptance Criteria, Accuracy, Baseline, Calibration, Concentration range. Control samples. Data Clean-Up, Deviation, Error propagation. Error recovery. Interference, Linearity, Noise, Numerical artifact. Precision, Recovery, Reliability, Repeatability, Reproducibility, Ruggedness, Selectivity, Specifications, System Suitability, Validation. 

Industrial analytical laboratories search for methodologies that allow high quality analysis with enhanced sensitivity, short overall analysis times through significant reductions in sample preparation, reduced cost per analysis through fewer man-hours per sample, reduced solvent usage and disposal costs, and minimisation of errors due to analyte loss and contamination during evaporation. The experience and criticism of analysts influence the economical aspects of analysis methods very substantially. 

We will begin by taking a look at the detailed aspects of a basic problem that confronts most analytical laboratories. This is the problem of comparing two quantitative methods performed by different operators or at different locations. This is an area that is not restricted to spectroscopic analysis many of the concepts we describe here can be applied to evaluating the results from any form of chemical analysis. In our case we will examine a comparison of two standard methods to determine precision, accuracy, and systematic errors (bias) for each of the methods and laboratories involved in an analytical test. As it happens, in the case we use for our example, one of the analytical methods is spectroscopic and the other is an HPLC method. 

Errors in the analytical laboratory are basically of two types determinate errors and indeterminate errors. Determinate errors, also called systematic errors, are errors that were known to have occurred, or at least were determined later to have occurred, in the course of the lab work. They may arise from avoidable sources, such as contamination, wrongly calibrated instruments, reagent impurities, instrumental malfunctions, poor sampling techniques, errors in calculations, etc. Results from laboratory work in which avoidable determinate errors are known to have occurred must be rejected or, if the error was a calculation error, recalculated. 

Most of the instruments, commonly used in an analytical laboratory, such as UV-Spectrophoto-meter, IR-Spectrophotometer, single—pan electric balance, pH-meter, turbidimeter and nephelometer, polarimeter, refractometer and the like must be calibrated duly, before use so as to eliminate any possible errors. In the same manner all apparatus, namely pipettes, burettes, volumetric flasks, thermometers, weights etc., must be calibrated duly, and the necessary corrections incorporated to the original measurements. 

Expiry of Mobile Phase. Always check the expiry of mobile phase before use. This is one of the most common errors in an analytical laboratory. 

A different method of interpretation is frequently observed between inspection services and analytical laboratories. This is because inspection services are interested mainly in a yes/no answer to questions, such as Has the animal been treated with anabolics or Does the food commodity contain residues above their MRL , in order to proceed to such action as rejection of the food commodity or removal of the test-positive animals from the farm. On the other hand, laboratories mainly use quality criteria to convert analytical results into yes/no answers. This conversion, however, is often obscured by inherent analytical difficulties including estimation of the impact of systematic and random errors and the way of sampling. 

The most common calibration model or function in use in analytical laboratories assumes that the analytical response is a linear function of the analyte concentration. Most chromatographic and spectrophotometric methods use this approach. Indeed, many instruments and software packages have linear calibration (regression) functions built into them. The main type of calculation adopted is the method of least squares whereby the sums of the squares of the deviations from the predicted line are minimised. It is assumed that all the errors are contained in the response variable, T, and the concentration variable, X, is error free. Commonly the models available are Y = bX and Y = bX + a, where b is the slope of the calibration line and a is the intercept. These values are the least squares estimates of the true values. The following discussions are only... 

In other words, the samples have to reflect, without distortion, the piece of information required from the population. Otherwise, the conclusions from the analytical data - the output of the analytical laboratory - about the state of the investigated object are definitely arbitrary and may cause momentous errors in the interpretation of results. Samples of a population must, therefore, be representative according to the specific query. This means that they must be both accurate and reproducible. This implies that the sampling process is affected by errors in each case. The question of representativeness is a question which has to be answered for each individual case in relation to the heterogeneity of the population to be sampled, the accuracy required, and the reproducibility of results. The extent of representativeness is, therefore, highly dependent on the expenditure on sampling and analysis and the time needed for the investigation. 

Reliable evaluation of the potential for human exposure to CDDs depends in part on the reliability of supporting analytical data from environmental samples and biological specimens. Historically, CDD analysis has been both complicated and expensive, and the analytical capabilities to conduct such analysis have been available through only a relatively few analytical laboratories. Limits of detection have improved greatly over the past decade with the use of high-resolution mass spectrometry, improvements in materials used in sample clean-up procedures, and with the use of known labeled and unlabeled chemical standards. Problems associated with chemical analysis procedures of CDDs in various media are discussed in greater detail in Chapter 6. In reviewing data on CDD levels monitored or estimated in the environment, it should be noted that the amount of the chemical identified analytically is not necessarily equivalent to the amount that is bioavailable (see Section 2.3) and that every measurement is accompanied with a certain analytical error. 

Many times, failing blend samples can be attributed to weighing errors or carelessly handling blend samples during their transfer from the processing room to the analytical laboratory. The analytical laboratory should provide pre-Iabeled and tared containers for each sample to be taken. Special care should be taken to not mix up caps and containers. 

The /-test is widely used in analytical laboratories for comparing samples and methods of analysis. Its application, however, relies on three basic assumptions. Firstly, it is assumed that the samples analysed are selected at random. This condition is met in most cases by careful design of the sampling procedure. The second assumption is that the parent populations from which the samples are taken are normally distributed. Fortunately, departure from normality rarely causes serious problems providing sufficient samples are analysed. Finally, the third assumption is that the population variances are equal. If this last criterion is not valid then errors may arise in applying the /-test and this assumption should be checked before other tests are applied. The equality of variances can be examined by application of the F-test. 

The first five of these characteristics are essential to minimize the errors involved in analytical methods. The last three characteristics are just as important as the first five in most analytical laboratories. Because primary standards are often costly and difficult to prepare, secondary standards are often used in day-to-day work. 

Atomic absorption, optical emission and atomic fluorescence as well as plasma mass spectrometry and new approaches such as laser enhanced ionization now represent strong tools for elemental analysis including speciation and are found in many analytical laboratories. Their power of detection, reliability in terms of systematic errors and their costs reflecting the economic aspects should be compared with those of other methods of analysis, when it comes to the development of strategies for solving analytical problems (Table 20). 

The cut-off value for acceptable charge imbalance is empirical and somewhat arbitrary. Freeze and Cherry (1979) reported that analytical laboratories usually consider a charge-balance error of < 5% to be acceptable. However, for dilute solutions such as rain water, the errors are usually higher. 

Some indicators or components of water should be determined directly at the sampling point so that the errors due to sampling and transport of the samples to analytical laboratory are eliminated. Commercial instruments exist which enable one to determine an increasing number of indicators in situ. The first indicator values analysed are odour (sometimes also taste). 

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