Quality Control, Data Analysis

Weizhen Wang is Associate Professor of Statistics in the Department of Mathematics and Statistics of Wright State University. His interests are in bioequivalence, multiple comparisons, categorical data analysis, quality control, and nonparamet-... 

Analysis of Retrospective Production Data Using Quality Control Charts... 

Statistics C CHATFIELD Data collection and analysis quality control reliability... 

Humidity J COMYN Relative humidity, laboratory control of humidity Statistics C CHAIEIELD Data collection and analysis quality control reliability... 

The raw data analysis and genotype calling is the first step and handled by the instrumentation software to yield genotype calls for each SNP and subject. The PG SNP Screen software links the sample identifiers back to the clinical database and performs extensive quality control analysis. Quality control includes comparison of genotypes from replicates of the same sample and from control samples to known genotypes. Verification of Hardy Weinberg equilibrium and analysis of quality scores supplied by the instrumentation software assists in establishing quality thresholds on SNPs and DNA samples. 

Like XPS, the application of AES has been very widespread, particularly in the earlier years of its existence more recently, the technique has been applied increasingly to those problem areas that need the high spatial resolution that AES can provide and XPS, currently, cannot. Because data acquisition in AES is faster than in XPS, it is also employed widely in routine quality control by surface analysis of random samples from production lines of for example, integrated circuits. In the semiconductor industry, in particular, SIMS is a competing method. Note that AES and XPS on the one hand and SIMS/SNMS on the other, both in depth-profiling mode, are complementary, the former gaining signal from the sputter-modified surface and the latter from the flux of sputtered particles. 

The determination and analysis of sensory properties plays an important role in the development of new consumer products. Particularly in the food industry sensory analysis has become an indispensable tool in research, development, marketing and quality control. The discipline of sensory analysis covers a wide spectrum of subjects physiology of sensory perception, psychology of human behaviour, flavour chemistry, physics of emulsion break-up and flavour release, testing methodology, consumer research, statistical data analysis. Not all of these aspects are of direct interest for the chemometrician. In this chapter we will cover a few topics in the analysis of sensory data. General introductory books are e.g. Refs. [1-3]. 

P. Lea, T. Naes and M. R0dbotton, Analysis of Variance for Sensory Data. Wiley, London, 1997 D. H. Lyon, M. A. Francombe, T. A. Hasdell and K. Lawson, Guidelines for Sensory Analysis in Product Development and Quality Control. Chapman and Hall, London, 1990. 

Ihnat M (1998a) Reference materials for data quality control. In Kalra YP, ed. Handbook of Reference Methods for Plant Analysis, pp 209-220. CRC Press, Boca Raton. 

An operation or series of operations that contributes to the validation of screening results. Such operations include validation of liquid handling devices and plate readers, experiment controls, such as determination of the Z factor and use of assay controls, and postexperiment controls, such as data analysis validation and database administration. Results of a screen are validated only after a set of quality controls have been performed. 

Of the various methods of data presentation, the one with which starting analysts may be least familiar is trend analysis and statistical quality control. In an industrial environment, analysis is often centered around the production of batches of material. The properties of those batches may change over time due to random effects or to subtle changes in the production process. In either case, the quality of the product may change. Analysis is used to track the change in the properties of batches over time. Industrial analytical methods, therefore, need to be extremely rugged. Millions of dollars may depend on the analyst s judgment as to batch equivalence. 

In the field of in-process analysis, analytical NMR applications also constitute a growth area - and also in relation to additives. This stems from the fact that the method makes it possible to use chemical analytical data in polymer quality control. Robust tools for hostile chemical plant environments are now available. The field of process analytical chemistry has been pushed to the forefront of the partnership between industry and academia. 

Laboratory analysis provides data that will be used as the basis for decision-making. The data require that the analysis of samples in laboratories meets specific quality assurance and quality control (QA/QC) requirements. 

Data analysis should focus on the development or refinement of the conceptual site model by analyzing data on source characteristics, the nature and extent of contamination, the contaminants transport pathways and fate, and the effects on human health and the environment. All field activities, sample management and tracking, and document control and inventory should be well managed and documented to ensure their quality, validity, and consistency. 

Regulatory status In 1998, Lopez Canyon Sanitary Landfill received conditional approval for an ET cover, which required a minimum of 2 years of field performance data to validate the model used for the design. An analysis was conducted and provided the basis for final regulatory approval of the ET cover. The cover was fully approved in October 2002 by the California Regional Water Quality Control Board—Los Angeles Region. 

The semiconductor industry makes heavy use of manufacturing data to provide ongoing quality control information. RS/1 is used as the central software tool to tie together a number of different data bases and software systems to make the data available for graphing and analysis. 

The analysis of each EO distribution of a multiple alkyl AE should facilitate better characterization protocols. One-dimensional NPLC may provide sufficient resolution for less complicated AE (i.e., Novel II 1412-70 and Brij 35), but 2DLC offers the selectivity to display the EO distribution of each end group independently, which is neither easy nor unambiguous to extract from one-dimensional data. 2DLC is a powerful technique not only to separate materials, but also to aid in identification, characterization, analytical trouble shooting, synthesis optimization, and quality control. 

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