Analytical methods blindness

The ultimate analysis of organosilicon compounds is an important subject to every worker in the field of siloxane polymers and their intermediates, for without dependable analytical methods the research chemist gropes blindly, at a loss concerning the composition of his products and unable to evaluate the effects of chemical attack. It is the purpose Of this chapter to trace very briefly the development of adequate analytical procedures for organosilicon compounds, with particular emphasis upon those methods which may be used for investigating the composition of silicone polymers. 

The limit of detection is the smallest amount of a substance detectable by an analytical method. This limit is strongly related to the noise of the measuring process. The definition given by Kaiser9495) in the field of spectroscopy96 should be understood in these terms. The most frequently used definition is stated by Eq. (37) where sdv(b) is the estimated standard deviation of a blind, i.e. the noise of the analytical procedure ... 

The blind application of statistical tests to retain or reject a suspect measurement in a small set of data is not likely to be much more fruitful than an arbitrary decision. The application of good judgment based on broad experience with an analytical method is usually a sounder approach. In the end, the only valid reason for rejecting a result from a small set of data is the sure knowledge that a mistake was made in the measurement process. Without this knowledge, a cautious approach to rejection of an outlier is wise. 

Were matrix interferences present Was the analytical instrument blind to interferences (e.g., GC/MS under selected ion mode) or was sample clean-up effective Is a different method of analysis going to be used from that published If so, will the solvent used for elution still be effective in the new instrumental method ... 

It is only after these requirements are defined, and a reliable analytical method is developed, that detection limits which are appropriate for the analysis can be calculated. Then, specific quality control measures that eliminate or quantify errors must be developed and implemented. The implementation of such a plan for the analysis of a set of samples prepared by an Independent laboratory and analyzed blindly by this laboratory follows. 

History and other sources. In the course of this investigation, which included more than 40 samples from areas around the world, we were able for the first time to analyze by Py-GC samples of Japanese amber from the Fuji area. In an interesting finding we established that one fossil resin sample from Montana was almost pure natural polystyrene." On the basis of data obtained, attributions of some doubtful samples were done in a blind experiment and a number of fake amber imitation materials were uncovered. As a main result of this work, we reached the conclusion that Py-GC can be a valuable addition to the analytical methods routinely used in the study of amber, giving, in many cases, much more convincing fingerprints than other methods, e.g., FTIR. 

Single-operator characteristics are determined by analyzing a sample whose concentration of analyte is known to the analyst. The second step in verifying a method is the blind analysis of standard samples where the analyte s concentration remains unknown to the analyst. The standard sample is analyzed several times, and the average concentration of the analyte is determined. This value should be within three, and preferably two standard deviations (as determined from the single-operator characteristics) of the analyte s known concentration. 

Agency. A second example of an external method of quality assessment is the voluntary participation of the laboratory in a collaborative test (Chapter 14) sponsored by a professional organization such as the Association of Official Analytical Chemists. Finally, individuals contracting with a laboratory can perform their own external quality assessment by submitting blind duplicate samples and blind standard samples to the laboratory for analysis. If the results for the quality assessment samples are unacceptable, then there is good reason to consider the results suspect for other samples provided by the laboratory. 

Two aspects are important for IQC (1) the analysis of control materials such as reference materials or spiked samples to monitor trueness and (2) replication of analysis to monitor precision. Of high value in IQC are also blank samples and blind samples. Both IQC aspects form a part of statistical control, a tool for monitoring the accuracy of an analytical system. In a control chart, such as a Shewhart control chart, measured values of repeated analyses of a reference material are plotted against the run number. Based on the data in a control chart, a method is defined either as an analytical system under control or as an analytical system out of control. This interpretation is possible by drawing horizontal lines on the chart x(mean value), x + s (SD) and x - s, x + 2s (upper warning limit) and x-2s (lower warning limit), and x + 3s (upper action or control limit) and x- 3s (lower action or control limit). An analytical system is under control if no more than 5% of the measured values exceed the warning limits [2,6, 85]. 

The evaluation study will determine the attributes (bias, precision, specificity, limits of detection) of the immunoassay. Bias testing (systematic error) will be conducted by measuring recoveries of the analyte added to matrices of interest. Replicate analysis will be performed on blind replicates or split levels (e.g., Youden pairs). A minimum number of replicates will be performed to provide statistically meaningful results. The number of replicates will be determined by the intended purpose of the immunoassay as well as the documented method performance of the comparative method. 

Sieving Methods Sieving is probably the most frequently used and abused method of analysis because the equipment, analytical procedure, and basic concepts are deceptively simple. In sieving, the particles are presented to equal-size apertures that constitute a series of go-no go gauges. Sieve analysis implies three major difficulties ( ) with woven-wire sieves, the weaving process produces three-dimensional apertures with considerable tolerances, particularly for fine-woven mesh (2) the mesh is easily damaged in use (3) the particles must be efficiently presented to the sieve apertures to prevent blinding. 

The other technique that could challenge this method is that based on fluorescent polymers which are extremely specific to the target analyte and so blind to other compounds [20]. This type of instrument is therefore attractive when wanting to detect a known explosive but will be blind to any variations. 

The laboratory should maintain a continuing quality control chart (Figure 3.6) for each method. A reference material of known analyte content is blindly and randomly run each day, or preferably with each batch of samples. If measured values fall outside prescribed standard deviation limits, then you should check for some systematic error such as reagent deterioration or instrument drift (needs recalibration). 

The second aspect of method validation is the experimental work. This typically involves initial experiments with analytical standards to confirm the reliability and repeatability of calibration of the system using only standards. The next step usually involves a series of analytical runs, conducted over several days or weeks, in which one or more analysts prepare calibration curves and analyze replicates of the typical analyte/matrix combinations and concentrations that are to be routinely analyzed using the method. The final phase of validation typically includes several runs in which fortified or incurred materials, again representing typical analyte/matrix combinations and concentrations, are provided blind to the analyst(s). The results are then summarized in a validation report, which again should receive appropriate peer review within the laboratory prior... 

Obvious questions are how many replicates should be included in the design and also how many analytical runs should be completed. Eurachem guidance suggests a minimum of 10 replicates for recovery (accuracy) and precision. A collaborative smdy design is recommended to include a minimum of five materials (matrices) at three concentrations, in blind duplicate, which means that each participating laboratory produces 10 results for each concentration. However, when one considers the recommendation that at least six different sources of matrix should be used in validation for methods for veterinary drug residues in foods and that the validation should include analyses conducted on multiple days, with inclusion of other variables, such as analyst, equipment, and reagents, it is obvious that 10 replicates will prove insufficient to provide the necessary data for a suitable assessment of... 

The underlying concept of the validation experiments is to provide a prediction of the performance to be expected over an extended period in routine use, and a minimal dataset will not satisfy this expectation. Therefore, a typical validation design will include the six different sources of matrix, usually at each of three concentrations bracketing the MRL, repeated as analyst spikes in three or four analytical runs, followed by one or two additional runs where the materials are provided as unknowns (blind) to the analyst. The design is usually repeated for each required matrix (e.g., each species-tissue combination) for the initial target species and may be also be required when the method is applied routinely to other species. However, when there are obvious commonalities (such as tissues from different ruminants), method extension may require only a reduced dataset, based on experience with the method. 

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