Sample analysis data quality, measurement

Sample analysis data quality. Precision of sample analysis is almost always measured by determining the RSD at two or more concentrations without using a calibration curve. Such data do not include the effects of the calibration process on precision. Bluch better information is given by the relative confidence bandwidth (RGB) defined as ... 

Each of the countries operates a quality analysis system for post-marketing control of drug quality, albeit with vast differences in capacity. Data on the outcome measure for drug quality— the number of dmg samples that failed quality tests compared with the total number of samples collected— are available in all the countries, except the Netherlands. Failure rates are high in some countries, e.g. Tunisia and Uganda. In Australia, high failure rates are found for herbal and other complementary products, compared with prescription dmgs. Empirical data on sanctions applied in such instances are not available. 

Multiway and particularly three-way analysis of data has become an important subject in chemometrics. This is the result of the development of hyphenated detection methods (such as in combined chromatography-spectrometry) and yields three-way data structures the ways of which are defined by samples, retention times and wavelengths. In multivariate process analysis, three-way data are obtained from various batches, quality measures and times of observation [55]. In image analysis, the three modes are formed by the horizontal and vertical coordinates of the pixels within a frame and the successive frames that have been recorded. In this rapidly developing field one already finds an extensive body of literature and only a brief outline can be given here. For a more comprehensive reading and a discussion of practical applications we refer to the reviews by Geladi [56], Smilde [57] and Henrion [58]. 

Over the years, many instruments have been developed for and used in the scientific laboratory. Today, the computer is used as a major tool in the scientific laboratory for the capture, manipulation, transfer, and storage of data. Consequently, the concern for data quality has shifted from the instruments that are used in the generation of the data to these electronic systems, often neglecting the fact that the data are only as accurate as the instrument measurements. For instance, many electronic systems can be used in chromatography analysis, from the electronic log book where the test substance inventory is kept, throughout data capture in the instrument, to the digitized electronic signal that is the raw data on the computer network. For crop residue samples, the... 

Laboratories using these methods for regulatory purposes are required to operate a formal quality control program. The minimum requirements of the program consist of an initial demonstration of laboratory capability and an ongoing analysis of spiked samples to evaluate and document data quality. The laboratory must maintain records to document the quality of data that is generated. Ongoing data quality checks are compared with established performance criteria to determine whether or not the results of analyses meet the demonstrated performance characteristics of the method. When results of spike sample analyses indicate atypical method performance, a quality control check standard must be analyzed to confirm that the measurements were performed in an in-control mode of operation. 

To investigate the data quality of PFC measurements, a worldwide interlaboratory study was conducted in 2005 involving 38 laboratories from 13 countries [93]. Each laboratory analysed 13 PFCs in three environmental samples and two human samples. Results indicated approximately 65% agreement for PFOS and PFOA in human blood and plasma samples, but agreement for other PFS As and PFCAs was much lower and most laboratories underestimated the PFC concentrations in fish extracts due to matrix effects. The study concluded that additional work is needed to improve the analytical techniques employed for the analysis of PFCs. 

Several recommendations arose from the interlaboratory smdy to minimize analytical challenges and to ensure data quality. As discussed above, it is recommended that mass labelled PFCs be employed as internal standards [93, 97]. It should be noted, however, that some electrospray ionization suppression may still occur if these internal standards are used at high concentrations [97]. Matrix effects can also be minimized by employing matrix-matched calibration standards in lieu of solvent-based calibration standards [97]. Unfortunately, matrix-matched standards can be impractical when an appropriate clean matrix cannot be found [94]. Other quality assurance and quality control measures, such as spike and recovery analyses of an analyte added to the sample matrix, repetitive analysis of samples to determine precision and comparison of internal standard quantitation to quantitation via standard additions, are also useful in determining data quality [94]. 

The frequency of the control sample analysis depends on the nature of the analysis. Successful analysis of the control samples assures that the system is performing as expected under the SOP. Validation of HPLC equipment assures that valid measurements are obtained. The quality of the analytical data can be maintained by keeping, in a safe place, records of the actual instrument conditions at the time the measurements were made. Backups should also be maintained. 

Three-way two-block data can be encountered, e.g., in modeling and multivariate statistical process control of batch processes. The first block contains the measured process variables at certain points in time of different batch runs. The second block might contain the quality measurements of the end products of the batches. Creating a relationship between these blocks through regression analysis or similar, can shed light on the connection of the variation in quality and the variation in process measurements. This can be used to build control charts [Boque Smilde 1999, Kourti et al. 1995], Another application is in multivariate calibration where, for example, fluorescence emission/excitation data of samples are used to predict a property of those samples [Bro 1999],... 

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