Systematic errors specific examples

Fig. 29. Origin of systematic errors in spite of potentially error-free analysis. On-line sampling setups (top) and time trajectories of limiting substrate concentration during sample preparation in the two paradigmatic setups depending on the actual culture density (bottom). Either a filter in bypass loop is used for the preparation of cell-free supernatant (upper part in top insert) or an aliquot of the entire culture is removed using an automatic sampler valve and a sample bus for further inactivation and transport of the samples taken (lower part). Both methods require some finite time for sample transportation from the reactor outlet (at z = 0) to the location where separation of cells from supernatant or inactivation by adding appropriate inactivators (at z = L) takes place. During transport from z = 0 to z = L, the cells do not stop consuming substrate. A low substrate concentration in the reactor (namely s KS) and a maximal specific substrate consumption rate of 3 g g h 1 were assumed in the simulation example to reflect the situation of either a fed-batch or a continuous culture of an industrially relevant organism such as yeast. The actual culture density (in g 1 1) marks some trajectories in the mesh plot. Note that the time scale is in seconds...

Further aspects, pros and cons of WPPF, are discussed in Chapter 5. Here it is important to underline the fact that the validity of profile fitting is limited by the basic assumption of using an a priori selected profile function without any sound hypothesis that the specific functional form is appropriate to the case of study. The consequence of this arbitrary assumption can be quite different. For example, in most practical cases, profile fitting can provide reliable values of peak position and area, whereas the effects on the profile parameters are less known and rarely considered. The arbitrary choice of a profile function tends to introduce systematic errors in the width and shape parameters, which invariably introduce a bias in a following LPA, whose consequences can hardly be evaluated. It is therefore a natural tendency, for complex problems and to obtain more reliable results, to remove the a priori selected profile functions - leading to the following section dedicated to Whole Powder Pattern Modelling methods. 

The first compounds detected are often the most volatile as well as those of low polarity—the interaction with the stationary phase generally increases with polarity. It is often hard to resolve nonpolar peaks efficiently—Ar and N2 is an example—when sampling air unless specifically considered and designed for, the Ar peak will be masked by the nitrogen. (This could introduce a systematic error of 1% or more if not accounted for when the nitrogen is calibrated.) The order in which the compounds appear in the chromatogram is from the most volatile to the least volatile but there are exceptions. It is also important to consider the nature of the compounds and the type of stationary phase of the column. Take as an example the 10 compounds in Table 8.2. 

Systematic errors are normally reflected in a signal as an offset from the true zero value. Such offsets cannot always be eliminated in a specific instrument but, as long as it is known, its contribution can be taken account of during data evaluation. However, the offset may not always be constant, but vary or drift over time such drifts are caused, for example, by thermal changes in the environment or the measurement equipment, or by the (often uncontrollable) variation of some experimental parameter. 

Specific examples of systematic chemical errors include ... 

Specific examples of chemical systematic and random errors... 

Only the systematic errors of the corrosion-rate measurements, those inherent in the theory of the techniques, are treated in this chapter. These errors are a consequence of the assumptions involved in the derivation of Eq. (1), which are enumerated in Section II. 1. Error of the measurement occurs whenever one of the simplifying assumptions is not justified for the existing conditions. Some other errors, as discussed below, arise from further simplifying assumptions, specific for each measurement technique. Each error possibility will be discussed and evaluated separately. Of course, more than one of them will usually have to be considered in a practical situation. Moreover, the random errors of the measurement itself (e.g., current, potential, specimen area, etc.) are seldom negligible (see, for example. Ref. 21, 23, 77), and they should be added to the systematic errors described here. 

An analytical result is never perfect. It is always affected by a certain number of errors categorized as systematic (bias) and random errors. For example, an autosampler commits systematic errors when it injects I.I pL of a sample at a speed of 12 pL.s when the specification is to inject a volume of 1.0 pL at 10 pL.s... 

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