Quantitation decision limit

They define the 3 limit as the decision limit and the 6 a,/ limit as the detection limit. They define further a determination limit, at which quantitative determinations of the component with a defined standard deviation can be made. This determination limit is defined as... 

Noise, as well as affecting the appearance of a spectrum, influences the sensitivity of an analytical technique and for quantitative analysis the S/N ratio is of fundamental importance. Analytical terms dependent on the noise contained in the signal are the decision limit, the detection limit, and the determination limit. Instrument manufacturers often quote these analytical figures of merit and knowledge of their calculation is important in evaluating and comparing instrument performance in terms of analytical sensitivity. 

Figure 2.5 provides a graphical view of the terms used to define the decision limit, Xc and detection limit, x. The decision limit, x, is a specific concentration level for a targeted analyte above which one may decide whether or not the result of analytical measurement indicated detection. The detection limit, x j, is a specific concentration level for a targeted analyte above which one may rely upon to lead to detection. A third limit, known as a determination limit, or using more recent jargon from the EPA, the practical quantitation limit, is a specific concentration at which... 

Quantitative risk analysis is subject to several theoretical limitations. Table 13 lists five of the most global limitations of QRA. Some of these may be relatively unimportant for a specific study, and others may be minimized through care in execution and by limiting one s expectations about the applicability of the results. However, you must respect these limitations when chartering a QRA study and when using the results for decision-making purposes. 

In the past, qualitative approaches for hazard evaluation and risk analysis have been able to satisfy the majority of decision makers needs. In the future, there will be an increasing motivation to use QRA. For the special situations that appear to demand quantitative support for safety-related decisions, QRA can be effective in increasing the manager s understanding of the level of risk associated with a company activity. Whenever possible, decision makers should design QRA studies to produce relative results that support their information requirements. QRA studies used in this way are not subject to nearly as many of the numbers problems and limitations to which absolute risk studies are subject, and the results are less likely to be misused. 

Quantitative risk analysis (QRA) is a powerful analysis approach used to help manage risk and improve safety in many industries. When properly performed with appropriate respect for its theoretical and practical limitations, QRA provides a rational basis for evaluating process safety and comparing improvement alternatives. However, QRA is not a panacea that can solve all problems, make decisions for a manager, or substitute for existing safety assurance and loss prevention activities. Even when QRA is preferred, qualitative results, which always form the foundation for QRA, should be used to verify and support any conclusions drawn from QRA. 

The aim of most screening methods is to produce a yes/no decision, concerning whether the concentration of a certain substance in a sample exceeds a given limiting concentration or not. For instance, if the concentration of a substance lies below a permitted maximum concentration then there is probably no need to analyse the sample. However, if the content is in the region of or above the permitted limit, then the result must be confirmed by means of an exact quantitative determination. 

Similar considerations were taken into account throughout the process of designing the study and committing the design to a protocol. In addition to analytical quality specifications, decisions were made regarding definitions of limits of detection and quantitation, levels of apparent residues at which confirmation was required, and how such confirmation would be achieved. All of these decisions were based on fulfilling the objectives of the study while operating within unavoidable time and resource constraints. 

Method validation is defined in the international standard, ISO/IEC 17025 as, the confirmation by examination and provision of objective evidence that the particular requirements for a specific intended use are fulfilled. This means that a validated method, if used correctly, will produce results that will be suitable for the person making decisions based on them. This requires a detailed understanding of why the results are required and the quality of the result needed, i.e. its uncertainty. This is what determines the values that have to be achieved for the performance parameters. Method validation is a planned set of experiments to determine these values. The method performance parameters that are typically studied during method validation are selectivity, precision, bias, linearity working range, limit of detection, limit of quantitation, calibration and ruggedness. The validation process is illustrated in Figure 4.2. 

As a manager you must appreciate that the assumptions made during a QRA are as important as any quantitative result. And the decisions you make will be crucially tied to your appreciation of the limitations of such studies. 

Since one of the main aims of green chemistry is to reduce the use and/or production of toxic chemicals, it is important for practitioners to be able to make informed decisions about the inherent toxicity of a compound. Where sufficient ecotoxicological data have been generated and risk assessments performed, this can allow for the selection of less toxic options, such as in the case of some surfactants and solvents [94, 95]. When toxicological data are limited, for example, in the development of new pharmaceuticals (see Section 15.4.3) or other consumer products, there are several ways in which information available from other chemicals may be helpful to estimate effect measures for a compound where data are lacking. Of these, the most likely to be used are the structure-activity relationships (SARs, or QSARs when they are quantitative). These relationships are also used to predict chemical properties and behavior (see Chapter 16). There often are similarities in toxicity between chemicals that have related structures and/or functional subunits. Such relationships can be seen in the progressive change in toxicity and are described in QSARs. When several chemicals with similar structures have been tested, the measured effects can be mathematically related to chemical structure [96-98] and QSAR models used to predict the toxicity of substances with similar structure. Any new chemicals that have similar structures can then be assumed to elicit similar responses. 

Although the lower limit of quantitation is established during assay validation and prior to microdosing, assay sensitivity remains an uncertainty until the actual analysis of the microdose samples as well. There is always the danger that plasma exposures from the microdose are lower than predicted and as a result plasma concentrations from some or all of the time points cannot be detected by the LC-MS/MS method. Reduction of this risk is achieved by collaborative communication between the bioanalytical chemist and the project team. Conservative estimates on bioavailability and clearance can be used to establish the necessary limit of detection needed to determine plasma concentrations for all time points. Updates on the progress of the assay development allow the team to decide if the achievable limit of detection will enable the determination of plasma concentrations from enough time points to make a go-no go decision. Of course, sensitivity is not an issue with AMS, which practically ensures that plasma concentrations will be determined, possibly for several days, enabling the observation of complex PK and clearance from deep compartments. 

In addition to successful linking of target antigen and DNA marker, as discussed in the previous chapter, the subsequent amplification of the DNA is the second key factor for efficient IPCR. Similar to many protocols developed for quantitative PCR [2], the DNA amplification product has to be converted into a detectable signal. Typically, a simple yes/no decision on the presence of the DNA marker is not sufficient, and a quantitative readout dependent on the antigen concentration is needed. Therefore, in many IPCR applications the cycle number in PCR-amplification is limited to the exponential phase of the amplification for example, 30 or fewer cycles [10, 24-26, 29, 31, 33, 37]. Alternatively, successful applications of 40 cycles were also reported [34-36, 38, 39, 41], underlining the relative flexibility of PCR conditions for the amplification step. The need for an optimized cycle number is only important for end point determinations such as gel electrophoresis (Section 2.2.1) or PCR-ELISA (Section 2.2.2). Recently, the... 

Two important components of quantitative analysis of environmental samples are the determination of method detection limits and instrument calibration. Understanding how they contribute to data quality will enable us to make decisions related to data validity during the assessment phase of the data collection process. 

---