Reliability of Analytical Observations and Measurements

Analytical measurements are fundamentally subject of uncertainty where various types of deviations (errors) can appear and these may be influenced to varying degree. Even when instrument readings are sufficiently accurate, repeated measurements of a sample lead, in general, to measured results which deviate by varying amounts from each other and from the true value of the sample. 

Before the different types of deviations are characterized in detail, some essential terms have to be considered. 

In analytical chemistry, the term error (used in the sense of deviation) is defined by the difference between the test result (xtest) and the true value (x, i.e., the accepted reference value, see ISO 3534-1 [1993] Fleming et al. [1997]). The term may be related both to measured value y and analytical value x which correspond to each other according to the sensitivity factor b of an analytical procedure. 

According to their character and magnitude, the following types of deviations can be distinguished. 

Random deviations (errors) of repeated measurements manifest themselves as a distribution of the results around the mean of the sample where the variation is randomly distributed to higher and lower values. The expected mean of all the deviations within a measuring series is zero. Random deviations characterize the reliability of measurements and therefore their precision. They are estimated from the results of replicates. If relevant, it is distinguished in repeatability and reproducibility (see Sect. 7.1) 

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Both qualitative observations and quantitative measurements cannot be reproduced with absolute reliability. By reason of inevitable deviations, measured results vary within certain intervals and observations, mostly in form of decision tests, may fail. The reliability of analytical tests depends on the sample or the process to be controlled and the amount of the analyte, as well as on the analytical method applied and on the economical expenditure available. 

The procedure of Lifson and Warshel leads to so-called consistent force fields (OFF) and operates as follows First a set of reliable experimental data, as many as possible (or feasible), is collected from a large set of molecules which belong to a family of molecules of interest. These data comprise, for instance, vibrational properties (Section 3.3.), structural quantities, thermochemical measurements, and crystal properties (heats of sublimation, lattice constants, lattice vibrations). We restrict our discussion to the first three kinds of experimental observation. All data used for the optimisation process are calculated and the differences between observed and calculated quantities evaluated. Subsequently the sum of the squares of these differences is minimised in an iterative process under variation of the potential constants. The ultimately resulting values for the potential constants are the best possible within the data set and analytical form of the chosen force field. Starting values of the potential constants for the least-squares process can be derived from the same sources as mentioned in connection with trial-and-error procedures. 

There are several potential sources of error in these methods. The filters routinely used have a relatively high and somewhat variable sulfate content, so that, at concentrations lower than 10 Mg/m and sampling periods less than 24 h, the reliability of tlie sulfate measurement is reduc. Several different types of filtering media adsorb sulfur dioxide during the ftrst few hours of sampling this alters the amount of sulfate observed. This interference can become critical when sampling periods are less than 24 h and the concentration ratio of sulfur dioxide to sulfate is greater than 5 1. Interference can also be introduced by hot-water extraction when reduced sulfur compounds like sulfite are present, because they are oxidized to sulfates in this process. Another possible error source is that some of the various analytic procedures us for sulfate determination may be influenced by other substances also present in the particulate matter. 

Several overall conclusions can be drawn based on the statistical evaluation of the data submitted by the participants of the DR CALUX intra-and interlaboratory validation study. First, differences in expertise between the laboratories are apparent based on the results for the calibration curves (both for the curves as provided by the coordinator and for the curves that were prepared by the participants) and on the differences in individual measurement variability. Second, the average results, over all participants, are very close to the true concentration, expressed in DR CALUX 2,3,7,8-TCDD TEQs for the analytical samples. Furthermore, the interlaboratory variation for the different sample types can be regarded as estimates for the method variability. The analytical method variability is estimated to be 10.5% for analytical samples and 22.0% for sediment extracts. Finally, responses appear dependent on the dilution of the final solution to be measured. This is hypothesized to be due to differences in dose-effect curves for different dioxin responsive element-active substances. For 2,3,7,8-TCDD, this effect is not observed. Overall, based on bioassay characteristics presented here and harmonized quality criteria published elsewhere (Behnisch et al., 2001a), the DR CALUX bioassay is regarded as an accurate and reliable tool for intensive monitoring of coastal sediments. 

The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small but deliberate variations in the analytical procedure parameters. The robustness of the analytical procedure provides an indication of its reliability during normal use. The evaluation of robustness should be considered during development of the analytical procedure. If measurements are susceptible to variations in analytical conditions, the analytical conditions should be suitably controlled or a precautionary statement should be included in the procedure. For example, if the resolution of a critical pair of peaks was very sensitive to the percentage of organic composition in the mobile phase, that observation would have been observed during method development and should be stressed in the procedure. Common variations that are investigated for robustness include filter effect, stability of analytical solutions, extraction time during sample preparation, pH variations in the mobile-phase composition, variations in mobile-phase composition, columns, temperature effect, and flow rate. 

The application of standard electrode potential data to many systems of interest in analytical chemistry is further complicated by association, dissociation, complex formation, and solvolysis equilibria involving the species that appear in the Nemst equation. These phenomena can be taken into account only if their existence is known and appropriate equilibrium constants are available. More often than not, neither of these requirements is met and significant discrepancies arise as a consequence. For example, the presence of 1 M hydrochloric acid in the iron(Il)/iron(llI) mixture we have just discussed leads to a measured potential of + 0.70 V in 1 M sulfuric acid, a potential of -I- 0.68 V is observed and in 2 M phosphoric acid, the potential is + 0.46 V. In each of these cases, the iron(II)/iron(III) activity ratio is larger because the complexes of iron(III) with chloride, sulfate, and phosphate ions are more stable than those of iron(II) thus, the ratio of the species concentrations, [Fe ]/[Fe ], in the Nemst equation is greater than unity and the measured potential is less than the standard potential. If fomnation constants for these complexes were available, it would be possible to make appropriate corrections. Unfortunately, such data are often not available, or, if they are, they are not very reliable. 

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