Uncertainty in identification

Table 1 Maximum tolerance windows for relative ion intensities to ensure appropriate uncertainty in identification (adapted from the WADA Laboratory International Standard)...

Qualitative analysis methods should have well-grounded and generally adopted quantitative reliability estimations. At first the problem was formulated by N.P. Komar in 1955. Its actuality increased when test methods and identification software systems (ISS) entered the market. Metrological aspects evolution for qualitative analysis is possible only within the scope of the uncertainty theory. To estimate the result reliability while detecting a substance X it is necessary to calculate both constituents of uncertainty the probability of misidentifications and the probability of unrevealing for an actual X. There are two mutual complementary approaches to evaluate uncertainties in qualitative analysis, just as in quantitative analysis ... 

The user can find suitable materials in a number of different ways. For instance any of the above measurands can be chosen and a search made within a specific matrix type. A list of the measurand values in all materials of the selected matrix classification sorted by decreasing concentration will be produced, including the uncertainties in percent, the certification status and the material identification code. Other search methods are possible, selection by material gives a table with values of all measurands in the chosen material in alphabetical order and additional information about the price, the unit size, the issuing date, the supphers and the exact material name. A further option is to list all materials from a producer. 

In some cases, confirming identification of components obtained from soil, such as pesticides, is essential. Thus, the uncertainty in some analyses needs to be addressed. This can be accomplished by identifying the components using two entirely different methods such as IR spectroscopy and MS. Although GC-IR-MS methods can positively identify separated components, the IR component of the system is not nearly as sensitive as are the GC and MS components. This detracts from the usefulness of this method. However, in cases where the level of analyte is not limiting, which frequently occurs in soil extracts, this can be an excellent method to use. Also, with modern concentration techniques, it is neither difficult nor time-consuming to concentrate analytes to a level that is identifiable by IR spectroscopy [17,18],... 

Note that careful evaluation and minimization of uncertainties and errors in CTMs is requested to enable the application of these CTMs to the study of observed changes in 03 as small as < 1.5 %/yr. However, actually 03 concentrations are simulated by the models within 20-50%. Chemical reaction rates are also uncertain, for instance in the 90 s determinations of the rates of CH4 and CH3CC13 reactions with OH suggested that these reactions are about 20% slower than believed. Similarly OH reaction with N02 which is an important sink for NOx in the troposphere is measured to be 10-30% lower than earlier estimates [23]. Thus, the past years a number of studies (mainly based on Monte Carlo simulations) focused on the identification and evaluation of the importance of various chemical reactions on oxidant levels to highlight topics crucial for error minimization. Temperature dependence of reaction rates can also introduce a 20-40% uncertainty in 03 and H20 computations in the upper troposphere. It has been also shown that 03 simulations are particularly sensitive to the photolysis rates of N02 and 03 and to PAN chemistry. 

Individual compound identification in all GC methods with the exception of GC/MS relies on the compound retention time and the response from a selective or non-selective detector. There is always a degree of uncertainty in a compound s identity and quantity, particularly when non-selective detectors are used or when the sample matrix contains interfering chemicals. To reduce this uncertainty, confirmation with a second column or a second detector is necessary. Analyses conducted with universal detectors (mass spectrometer or diode array) do not require confirmation, as they provide highly reliable compound identification. 

The main problem in evaluating the uncertainty of measurements in coulometry lies in identification of important uncertainty sources and estimation of their contribution (Table 2). With very low instrumental uncertainty, other factors become limiting to the achievable uncertainty, mainly those connected to the chemical processes in the cell and the homogeneity of the material. 

In order to tackle the problem of uncertainties in the available model, nonlinear robust and adaptive strategies have been developed, while, in the absence of full state measurements, output-feedback control schemes can be adopted, where the unmeasurable state variables can be estimated by resorting to state observers. The development of model-based nonlinear strategies has been fostered by the development of efficient experimental identification methods for nonlinear models and by significantly improved capabilities of computer-control hardware and software. 

D-QSAR. Since compounds are active in three dimensions and their shape and surface properties are major determinants of their activity, the attractiveness of 3D-QSAR methods is intuitively clear. Here conformations of active molecules must be generated and their features captured by use of conformation-dependent descriptors. Despite its conceptual attractiveness, 3D-QSAR faces two major challenges. First, since bioactive conformations are in many cases not known from experiment, they must be predicted. This is often done by systematic conformational analysis and identification of preferred low energy conformations, which presents one of the major uncertainties in 3D-QSAR analysis. In fact, to date there is no computational method available to reliably and routinely predict bioactive molecular conformations. Thus, conformational analysis often only generates a crude approximation of active conformations. In order to at least partly compensate for these difficulties, information from active sites in target proteins is taken into account, if available (receptor-dependent QSAR). Second, once conformations are modeled, they must be correctly aligned in three dimensions, which is another major source of errors in the system set-up for 3D-QSAR studies. 

The classical adaptive control scheme is shown in Figure 2.58. Its goal is to use online identification through artificial intelligence (Al), neural networks, and fuzzy logic to adapt the model to the actual process. Al and model predictive control (MPC) can tolerate inaccuracy and uncertainty in the model, and online training can continuously improve the model. 

The objective of this monograph is to provide an overview on the nature and characterization of uncertainty in exposure assessments, including guidance on the identification of sources of uncertainty, its expression and application, not only in risk assessment, but also in risk management decisions, delineation of critical data gaps and communication to decisionmakers and the public. 

The objective of an uncertainty analysis is to determine differences in the output of the assessment due to the combined uncertainties in the inputs and to identify and characterize key sources of uncertainty. To this end, a first step in the treatment of the uncertainty in an exposure study consists of the identification of the sources of uncertainty that are relevant for the study. 

Knowledge of key sources of uncertainty in exposure estimates helps guide additional data collection to reduce uncertainty in order to improve the precision of the estimates. For example, the identification of key sources of uncertainty can be used to prioritize information-gathering efforts for the most important inputs. Because uncertainty results from lack of knowledge, an effective approach to its reduction is to obtain more knowledge, through additional measurements or the development of more precise and accurate measurement methods. 

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