Data inter-laboratory study

NIST has also used results obtained from inter-laboratory studies as an additional set of results in the two or more methods approach (mode 2 in Table 3.13). For example for the recent value assignment for PCBs and pesticides in SRM 1944, the mean of results from 19 laboratories participating in an inter-laboratory comparison exercise was used as an additional set of data in the determination of the certified values. Similar inter-laboratory study results were also included in the value assignment of PAHs, PCBs, and pesticides for two recently issued mussel tissue materials, SRM 2977 and SRM 2978. 

The NADA method approval process consists of three phases (1) method development by the sponsor and generation of information to establish that the method satisfies acceptability criteria (2) FDA review of the sponsor s data to determine suitability of the method and (3) the method trial , an inter-laboratory study, which determine whether the method meets performance criteria when used in multiple laboratories. The inter-laboratory method trial procedure provides an indication of a method s ability to be used as a practicable and reliable regulatory tool. Sponsors are urged to develop methods that are mgged and exceed rather than meet the minimal standards of acceptability. Those methods that appear marginally acceptable after review often do not pass the inter-laboratory method trial. 

Because of the small number of laboratories involved, validation of UK methods by inter-laboratory study has become impractical in most cases. Even where it is practical, it is usually impossible to validate all pesticide-matrix combinations. Moreover, single-laboratory validation data will have to be generated. Therefore, the CSL guidelines are one of the first that strictly focus on requirements of single-laboratory validation. Some examples of minimum requirements are given in Table 8. Additionally, these guidelines emphasize some other important aspects of validation and contain some new ideas. 

In summary, official German analytical methods for pesticide residues are always validated in several laboratories. These inter-laboratory studies avoid the acceptance of methods which cannot readily be reproduced in further laboratories and they do improve the ruggedness of analytical procedures applied. The recently introduced calibration with standards in matrix improves the trueness of the reported recovery data. Other aspects of validation (sample processing, analyte stability, extraction efficiency) are not considered. 

Following the guidelines of ASTM D 6300, repeatability (r) and reproducibility (R) were determined from the data shown in Table 6. The results are given in Table 7 along with the mean of each sample and the standard deviations of r and R. The repeatability results show significant improvement over that of the ASTM D 7039-04. The reproducibility results are similar to that of inter-laboratory study (ILS) in 2002 [3]. Further interlaboratory round robin studies will be needed to confirm the reproducibility. 

Measurement Uncertainty Based on Inter-laboratory Study Data... 

Table 25.4 Comparison of labelled folic acid data with those obtained by stable isotope dilution assay (SIDA) and by HPLC-UV in an inter-laboratory study (internal standard). SIDA results generally are in good accordance with the labels. Differences for corn flakes, noodles and bread to HPLC-UV were attributable to interfering compounds.

Each individual method collection comprises a large number of methods, which often have different validation statuses. For instance, the most important Swedish multi-residue method (based on ethyl acetate extraction, GPC and GC) is validated for many pesticides by four laboratories, but other methods are presented with singlelaboratory validation data. Some methods in the Dutch and German manuals were tested in inter-laboratory method validation studies, but others by an independent laboratory or in a single laboratory only. 

The largest commercially available datasets are the Physical Properties (PHYSPROP) and AQUASOL databases ca. 6000 compounds in each database). The AQUASOL database has been published as a book. Furthermore, two relatively large sets of aqueous solubility data models were used in many other studies.Data from the AQUASOL database had an interlaboratory variation of about a = 0.49 log-units (as estimated for A=1031 molecules).Moreover, large inter-laboratory errors mask the influence of temperature, and differences as large as AT = 30 °C do not increase this error. In-house models developed at pharmaceutical companies could be based on similar or even larger numbers of measurements. For example, about 5000 molecules were used to develop a model at Bayer Healthcare AG. " ... 

These assays, which have been standardized and validated at InterceU AG s Ghni-cal Immunology Laboratory, enable reliable measurements of epitope-specific T cell responses induced by vaccination. All assays were performed in compliance with Good Laboratory Practice (GLP)/Good Clinical Practice (GCP) requirements. Standardization of the blood cell isolation procedure at the different investigational sites led to a high rate of evaluable assays. However, due to the lack of inter-laboratory standardization of T cell assays, comparison of the results of this study with published data from similar trials is difficult. Cryopre-served blood cells were used, which may have resulted in a possible underestimation of T cell responses compared with assays that utilize fresh blood. 

Kent and Smith (1987) reported the results of an inter-laboratoiy study on measmement of colom standards. They came to the obvious and important conclusion that in order to transfer and compare colour data from one laboratory to another, which is certainly necessary when colour is being used as a buying criterion, the measuring system and the colour standard have to be carefully defined. Most instrument manufacturers will provide details of best practice with respect to how the instrument and its standards should be maintained. 

It is possible to take up the problem from the other end and to declare the reproducibility of the procedure, i.e. the inter-laboratory standard deviation jr, as the measurement uncertainty ( top-down procedure). If the same analytical procedure is performed in numerous laboratories at the occasion of a collaborative study it can be assumed that all possible influences add to the inter-laboratory standard deviation which finally is calculated from all data. If no interlaboratory test was performed, it is only the own intra-laboratory standard deviation which is known, a value that may be too small and thus too optimistic, especially when it is necessary to compare the own results with the ones from other laboratories (suppliers, customers, authorities, competitors). 

Communication in the 1970 s was very different than it is today. There were no faxes, no emails, no satellite or cellular telephones, and international flights and inter-continental phone conversations were quite expensive. Thus, it so happened that at the same time that the Indiana University researchers were collecting their data a similar study was being conducted in England, without the researchers involved in one knowing about the other. The British study was conducted by a team of the U.K. s Transport Road Research Laboratory, and it involved a... 

Contact angle measurement is commonly used to characterize a surface and to study various wetting and de-wetting phenomena While the measurement is simple, the interpretation is not. This point has been noted by many surface investigators in the past, e.g.. Pease in 1945 [25], Morra et al. [26] in 1990, Kwok and Neumann [27] in 1999 and more recently by Marmur [28] as well as Strobel and Lyons [29]. Prior to data interpretation, one has to make sure that the measurement apparatus and procedures are impeccable. Over the years, many have voiced concerns over surface preparation and conditioning, measuranent procedure and technique, and data analysis [26,30-35]. It is therefore imperative for the community to have a set of common measurement protocol or guideline, so that inter laboratory data can be compared. Discrepancy in conclusion can be rationalized without concerns of experimental setups or procedures. 

The accuracy of exposure assessment is determined by systematic and random errors in the assessment. For quantitative exposure assessments, important sources of error include measurement errors (i.e. from laboratory and field monitoring techniques), as well as variations in exposure over time and space. For qualitative exposure proxies (e.g. self-reported past exposures, occupational histories or expert evaluations), the most important sources of error are recall bias (systematic differences in exposure recall between cases and controls) and random error, expressed in terms of intra- and inter-rater agreement. Although systematic errors can result in serious misinterpretations of the data, especially due to scaling problems, random errors have received more attention in epidemiology because this type of error is pervasive, and its effect is usually to diminish estimates of association between exposure and disease. The magnitude of random errors can be considerable in epidemiological field studies. 

---