Quality control statistical tools

Quality assessment includes the statistical tools used to determine whether an analysis is in a state of statistical control and, if possible, to suggest why an analysis has drifted out of statistical control. Among the tools included in quality assessment are the analysis of duplicate samples, the analysis of blanks, the analysis of standards, and the analysis of spike recoveries. 

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]. 

On the one hand, statistical quality control is an important tool for quality assurance within analytical chemistry itself (monitoring of test methods), and on the other for quality control of processes and products by means of analytical methods. 

Grant, E.L., and Leavenworth, R.S. (1988), Statistical Quality Control, 6th ed., McGraw-Hill, New York, NY. Green, P.E. (1976), Mathematical Tools for Applied Multivariate Analysis Student Edition, Academic Press, New York, NY. 

Ledolter, J, and Burrill, C. W. (1998), Statistical Quality Control Strategies and Tools for Continual Improvement, Wiley, New York. 

Duarte, I., Barros, A., Belton, P. S., Righelato, R., Spraul, M., Humpfer, E., and Gil, A. M. (2002). High-resolution nuclear magnetic resonance spectroscopy and multivariate statistical analysis for the characterization of beer.. Agric. Food Chem. 50, 2475-2481. Duarte, I. F., Barros, A., Almeida, C., Spraul, M., and Gil, A. M. (2004). Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer. J. Agric. Food Chem. 52, 1031-1038. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

A statistical index of precision calculated as ([standard deviation x 100] mean). The CV is a measure of the variability in a group of measurements. Since the CV is unitless, it can be used to compare CVs from different experiments . It is also a quality control tool. For example, in the algal microplate toxicity test, algal cell density in control wells at the end of the test exposure period must have a CV not exceeding 20% to meet test acceptability criteria. Volume 1(1,2,3,10). 

Propagation of error, sequential analysis, and quality control are additional statistical techniques with which the chemical engineer in design should be acquainted. The intent of this section will only be to outline the value of these tools and leave the details to other references. 

The main problem during quality assurance and quality control of analytical results arises from insufficient information about the tools used during this process, and about how they are used. First and foremost should be described the statistical tools used, which lie at the heart of metrology. 

In many cases and circumstances of the daily quality control of analytical work RMs and CRMs are helpful tools. Very often RMs are sufficient, in particular for statistical control actions. Where a rough estimate of accuracy or even precision is sufficient, a simple RM or calibration material is also largely adequate. However, for the establishment of the accuracy in the procedure of method development and validation, for revalidation of modified methods or whenever the analyst needs to demonstrate accuracy, e.g. measurements for court cases, CRMs should be employed as they have the advantage of being certified. It will be up to the operator and the laboratory s quality management to determine when, where, and how RM or preferably CRMs shall be used. 

New. A new microscale titration experiment is included, provided by Professor John Richardson from Shippensburg State University, for the analysis of hard-water samples (Experiment 18). The tools an4 techniques used for that experiment could be used to design similar experiments for other titrations if desired. (If your in-stractor tries this with you, I may include your experiment in the next edition ) Two team experiments are added (Experiments 39 and 40) to illustrate the principles presented in Chapter 4 on statistical validation. One is on method validation and quality control, in which different members of teams perform different parts of the validation for a chosen experiment. The other is on proficiency testing, in which you calculate the z-values for all the student results of one or more class experiments and you compare your z-value to see how well you have performed. 

What we have presented here is only a small portion, and very simplified at that, of the extensive array of concepts and techniques that constitute statistical process control. It is not our aim to exhaust this subject, but only to discuss it a little as an application of the normal distribution. Deeper treatments can be found in any of many books entirely dedicated to quality or statistical process control. To learn more about these important tools you can consult, for example, Oakland and Followell (1990) or Montgomery (1997). 

The magnitude of these variations is dependent on the quality of the lathe machine. However, once the tool is sufficiently worn, there will be significant change in the dimensions and variation will no longer be confined within control zone. In that case, one can identify the cause and replace the tool. Shewhart is called as the father of statistical quality control by many. 

Pigments and pigment dispersions have been and continue to be evaluated for color using a wide variety of methods. Over the years Oil Ink Tests, Latex Paint Tests, Liquid Ink Tests and PVC, Rubber, and Polyethylene Two-Roll Mill Tests have been used as Quality Control methods. We did not have extensive reproducibility data for these methods, but we felt that all of them could be improved using statistical tools. 

Note Although the terms statistical process control (SPC) and statistical quality control (SQC) are often used interchangeably, there are various differences between these terms. SQC is a broader concept including descriptive statistical methods, acceptance sampling, and SPC as commonly adopted tools. Ishikawa (Ishikawa 1976) points out that statistical process control and statistical quality control use the same set of tools to control respectively the input of a process (independent variables) and the output of the process (dependent variables). Other SPC/ SQC advocates further elaborate this concept by differentiating these terms according to the type of data elaborated by the tools SPC is based on process signal data analysis, while SQC is based on product feature-related data. 

Statistical models are becoming common tools in the hands of practicing engineers to address quality control and reliability issues and to perform fidlure analyses. At this stage of your education, it is important to realize that, in order to use statistical models, you need first to completely understand the underlying concepts. The nest sections are devoted to Kime of these important concepts. 

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