True response, quantitative

Due to the relative uniformity of ion formation by the RF spark (although its timing is erratic), the most widely used method of quantitation in SSMS is to assume equal sensitivity for all elements and to compare the signal for an individual element with that of the total number of ions recorded on the beam monitor. By empirically calibratii the number of ions necessary to produce a certain blackness on the plate detector, one can estimate the concentration. The signal detected must be corrected for isotopic abundance and the known mass response of the ion-sensitive plate. By this procedure to accuracies within a factor of 3 of the true value can be obtained without standards. 

Through statistical means and correlation transformation of the spectroscopic data, quantitative chemical sensing is possible. This is true, however, only in cases which permit good matrix (background) correction and optical measurement condition correction which allow linear analytical response in a reasonable range. 

Whichever method of presentation is chosen, it is critical that the numerical estimates not be separated from the various assumptions upon which they are based and their uncertainties. The dose-response characterization should contain a discussion and interpretation of the numerical estimates so that the risk manager gains significant insight into the extent to which the quantitative estimate reflects the true magnitude of potential human responses. The risk manager needs to understand that the true human health risk cannot be known with the degree of accuracy reflected in the numerical estimates. 

Various techniques, such as graphic illustrations (e.g., isobolograms), mixture toxicity indices (e.g., an additivity index), formulas, or fully parameterized models, exist for predicting an expected combined effect based on concentration addition or response addition (for review, see Bodeker et al. 1990). The quantitative relationship between the expected combined effect calculated according to concentration addition or response addition depends (in addition to other factors) primarily on the steepness of the concentration response relationship of the individual components (Drescher and Bodeker 1995). Concentration addition predicts a higher combined effect as compared to response addition when the mixture components have steep concentration response relationships, whereas the opposite is true for flat concentration response relationships of the mixture components. 

Quantitative methods of sensitivity analysis and metrics for measuring sensitivity are widely available. The most commonly used sensitivity analysis methods are often relatively simple techniques that evaluate the local linearized sensitivity of model response at a particular point in the input domain. This type of approach is typically used if the model inputs are treated as point estimates, often representing the best guess as to the true but unknown value of each... 

Some aspects of degree of concern currently can be considered in a quantitative evaluation. For example, EPA considers human and animal data in the process of calculating the RfD, and these data are used as the critical effect when they indicate that developmental effects are the most sensitive endpoints. When a complete database is not available, a database UF is recommended to account for inadequate or missing data. The dose-response nature of the data is considered to an extent in the RfD process, especially when the BMD approach is used to model data and to estimate a low level of response however, there is no approach for including concerns about the slope of the dose-response curve. Because concerns about the slope of the dose-response curve are related to some extent to human exposure estimates, this issue must be considered in risk characterization. (If the MOE is small and the slope of the dose-response curve is very steep, there could be residual uncertainties that must be dealt with to account for the concern that even a small increase in exposure could result in a marked increase in response.) On the other hand, a very shallow slope could be a concern even with a large MOE, because definition of the true biological threshold will be more difficult and an additional factor might be needed to ensure that the RfD is below that threshold. 

Another problem concerns the suppression of ion formation from an analyte caused by another analyte or by the presence of another constituent (e.g., buffer) in the solution. Since the MS response significantly depends on solvent and sample composition, ion signal intensities of a given analyte do not necessarily correlate with its concentration in the sample. For the quantitative analysis of an analyte of interest by ESI-MS (the same is true for MALDI-MS), the use of an adequate internal standard is therefore mandatory. 

As a result of the imperfections inherent in all electromechanical and electrical devices, true linearity is a hypothetical concept, and practical detectors can only approach this ideal response. Consequently, it is essential for the analyst to have some measure of detector linearity that can be given in numerical terms. Such a specification would allow quantitative comparison between detectors and indicate how close the response of the detector was to true linearity. Fowlis and Scott [1] proposed a simple method for measuring detector linearity. They assumed that for an approximately linear detector, the response can be described by the power function... 

---