Analytical data, evaluation

The focus of this chapter has been the evaluation of analytical data, including the use of statistics. In this final section we consider how statistics may be used to characterize a method s ability to detect trace amounts of an analyte. 

A given enzyme may be assayed by its action on soluble substrates under chemical and physical conditions different from those encountered in a real-life wash. Such experiments indicate the enzyme s performance with respect to pH and temperature variations, or in conjunction with other soluble substances, etc. The analytical data thus obtained are not necessarily representative of the wash performance of the enzyme, and real wash trials are necessary to evaluate wash performance of detergent enzymes. 

After intakes have been estimated, they arc organized by population, as appropriate. Then, tlie sources of uncertainty (e.g., variability in analytical data, modeling results, parameter assumptions) and their effect on tlie exposure estimates are evaluated and sunuiumzed. Tliis information on uncertainty is important to site decision-makers who must evaluate tlie results of the e.xposure... 

Statistical and algebraic methods, too, can be classed as either rugged or not they are rugged when algorithms are chosen that on repetition of the experiment do not get derailed by the random analytical error inherent in every measurement,i° 433 is, when similar coefficients are found for the mathematical model, and equivalent conclusions are drawn. Obviously, the choice of the fitted model plays a pivotal role. If a model is to be fitted by means of an iterative algorithm, the initial guess for the coefficients should not be too critical. In a simple calculation a combination of numbers and truncation errors might lead to a division by zero and crash the computer. If the data evaluation scheme is such that errors of this type could occur, the validation plan must make provisions to test this aspect. 

Reliable evaluation of the potential for human exposure to endosulfan depends in part on the reliability of supporting analytical data from environmental samples and biological specimens. In reviewing data on endosulfan levels monitored or estimated in the environment, it should also be noted that the amount of chemical identified analytically is not necessarily equivalent to the amount that is bioavailable. 

An alternative for evaluating accuracy is spiking known amounts of standards to a food, as reported in several papers,although percent recoveries of spikes do not truly address the influence of the food matrix complexity on the extraction efficiency. Data evaluation procedures were developed as a manual system to assess the quality of analytical data for carotenoids in foods. ... 

All the analytical data are from the same laboratory consequently, interlaboratory analytical variation is not a factor. The intralaboratory variation for that laboratory was 9.1 percent (i.e., the relative standard deviation based on repetitive analyses of performance evaluation samples). 

Many drillings have been made for the purpose of the exploration of Kuroko deposits in the Green tuff region. A large number of petrographical and analytical data are available. Thus, it is possible to (1) assess the proportion of volcanic rocks that have suffered hydrothermal alteration, and (2) attempt a quantitative evaluation of the influence of hydrothermal alterations at back-arc basins on middle Miocene seawater chemistry. 

Much of the analytical data on the nutrient content of foods is generated using official methods of analysis (e.g. AO AC International). An evaluation of AO AC Methods of Analysis for Nutritional Labelling is available (Sullivan and Carpenter 1993). While these methods have often been studied for a variety of food matrices, applicability over the entire range of food matrices has not been formally studied in most cases. In addition, RMs are not available over the entire range of food matrices (Wolf... 

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