Data Quality Control and Interpretation

Lipidomics Comprehensive Mass Spectrometry of Lipids, First Edition. Xianlin Han. 2016 John Wiley Sons, Inc. Published 2016 by John Wiley Sons, Inc. 

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Such an expert system can also be adapted for the evaluation of data in the published literature. However, this point raises a number of practical questions. A better exploitation of chromatographic data in this field would require an important effort to be made by analysts to constitute standards for quality control and interpretation... 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

E1N SIGHT, and others. All of these programs are specifically directed toward the multivariate analysis of analytical chemical data both in assessing data quality (quality control and quality assurance) and in interpreting data to provide insight into the complex system under investigation. 

When the analytical laboratory is not responsible for sampling, the quality management system often does not even take these weak links in the analytical process into account. Furthermore, if sample preparation (extraction, cleanup, etc.) has not been carried out carefully, even the most advanced, quality-controlled analytical instruments and sophisticated computer techniques cannot prevent the results of the analysis from being called into question. Finally, unless the interpretation and evaluation of results are underpinned by solid statistical data, the significance of these results is unclear, which in turn greatly undermines their merit. We therefore believe that quality control and quality assurance should involve all the steps of chemical analysis as an integral process, of which the validation of the analytical methods is merely one step, albeit an important one. In laboratory practice, quality criteria should address the rationality of the sampling plan, validation of methods, instruments and laboratory procedures, the reliability of identifications, the accuracy and precision of measured concentrations, and the comparability of laboratory results with relevant information produced earlier or elsewhere. 

The correct interpretation of the large amount of experimental data obtained by these techniques is a problem not to be taken lightly, since many important decisions relative to the use of a particular material may be based on these interpretations. It is obvious that these techniques can be used both for quality control and as an approach to the basic science of pseudostable materials. 

Another presumptive application of pattern recognition techniques is the interpretation of quality control data- Two such problems were described by Kowalski C147]- Quality control measurements were performed to characterize production items of an explosive in one case and of beryllium parts in the other. Pattern recognition methods may be useful for the evaluation of specification limits. If no classification method is capable of distinguishing between good and poor items, then it may be supposed that the data (quality control measurements) are insufficient-Feature selection and combination of features may indicate those measurements which provide most information on the separation into good and poor items C1483. 

Plastics are viscoelastic. Their behavior is partly elastic and partly that of a very viscous fluid. Properties of strength and rigidity vary with amount of stress, the rate of loading, and the temperature at which the stress is applied. Viscoelastic behavior requires performance tests to measure time dependence. The viscoelasticity of plastics also severely limits the usefulness of many short-time tests such as impact, tensile, and flexural strengths and modulus. Unfortunately, such test data are very widespread because they are easier and cheaper to obtain than time- and temperature-dependent information. These data can cause much confusion and disappointment when used for plastics. Short-time data are useful for quality control and specification purposes, and if properly interpreted, can shed some light on plastic performance. However, they cannot be used in design and are more often than not misleading because they do not account for the viscoelastic behavior of plastics. 

Modern gas chromatography is most often performed with high efficiency capillary (open tubular) columns coated with a cross-linked polymeric stationary phase. We provide chromatographic data on the two most common cross-linked phases (Ref. 1). These data are useful for the interpretation of chromatograms, and for quality control and assurance. The retention indices presented in these tables were measured at 120 °C isothermaUy. Retention indices are temperature dependent the temperature dependence of the Kovats indices have been studied for many compounds (Ref. 2). For more extensive information on other cross-linked phases, other silicone phases, mesogenic phases, and solid sorbents, the reader is advised to consult Ref. 1. 

The first of these points will be commented upon subsequently in connection with an interpretation of the statistical variability of fracture events. To answer this and the second question the highly developed tools of statistical analysis (e.g., 1—2) are available their application forms a backbone of industrial quality control and material design. Understandably, the complex input (mechanical, thermal, and environmental attack) acting upon a complex system (e. g., a highly structured polymer) has a complex, non-deterministic output. The determination and evaluation of only a small number of different data will necessarily reveal only a partial aspect of the fracture process. For this reason the questions as to cause and kinetics of fracture development can rarely be answered unambiguously. Any mathematical interrelations established between variables (e.g., time and stress) are valid for extrapolation only if the basis does not change. 

Machine learning provides the easiest approach to data mining, and also provides solutions in many fields of chemistry quality control in analytical chemistry [31], interpretation of mass spectra [32], as well prediction of pharmaceutical properties [33, 34] or drug design [35]. 

H.E. Grotjan and B.A. Keel, Data Interpretation and Quality Control, in Immunoassay, ed. 

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

The analysis of quality control samples is an important activity for laboratories and to make the most of the data, control charts should be used. This chapter has discussed a number of common types of control chart and described how they are set up and interpreted. 

Chemical analysis finds important applications in the quality control of industrial processes. In an ideal situation a continuous analysis of the process stream is made and some aspects of this are discussed in Chapter 12. However, such continuous analysis is by no means always possible, and it is common to find a process being monitored by the analysis of separate samples taken at regular intervals. The analytical data thus obtained need to be capable of quick and simple interpretation, so that rapid warning is available if a process is going out of control and effective corrective action can be taken. 

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