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Multivariate quality control

UNEQ can be applied when only a few variables must be considered. It is based on the Mahalanobis distance from the centroid of the class. When this distance exceeds a critical distance, the object is an outlier and therefore not part of the class. Since for each class one uses its own covariance matrix, it is somewhat related to QDA (Section 33.2.3). The situation described here is very similar to that discussed for multivariate quality control in Chapter 20. In eq. (20.10) the original variables are used. This equation can therefore also be used for UNEQ. For convenience it is repeated here. [Pg.228]

Meglen RR (1990J Analytical problem solving, reference materials, and multivariate quality control A chemometrics approach. Fresenius J Anal Chem 338 363-367. [Pg.292]

Hotelling, H. (1947). Multivariate quality control. In Techniques of Statistical Analysis", (C. Eisenhart, M. W. Hastay, and W. A. Wallis, Eds), pp. 111-184. McGraw-Hill, New York. [Pg.112]

P Miller and RE Swanson. Contribution plots The missing link in multivariate quality control. In 37th Annual Fall Technical Conf., ASQC, Rochester, NY, 1993. [Pg.292]

Miller P, Swanson RE, Heckler C, Contribution plots a missing link in multivariate quality control, Applied Mathematics and Computer Science, 1998, 8, 775-792. [Pg.362]

PCA is the basis for a multivariate process monitoring and a multivariate quality control, which are much more effective than the usually applied univariate approaches. ... [Pg.230]

P Miller, RE Swanson, and CF Heckler. Contribution plots The missing hnk in multivariate quality control. Int. J. App. Math. Comp. Science, 8(4) 775-792,1998. [Pg.161]

Alt, F. B., N. D. Smith, and K. Jain, Multivariate Quality Control, in Handbook of Statistical Methods for Scientists and Engineers, 2d ed., H. M. Wadsworth (Ed.), McGraw-Hill, New York, 1998, Chapter 21. [Pg.426]

Hotelling H. Multivariate quality control. In Eisenhart C, Hastay M, Wallis WA, editors. Techniques of statistical analysis. New York MacGraw-HiU 1947. p. 111-84. [Pg.137]

A solvent free, fast and environmentally friendly near infrared-based methodology was developed for the determination and quality control of 11 pesticides in commercially available formulations. This methodology was based on the direct measurement of the diffuse reflectance spectra of solid samples inside glass vials and a multivariate calibration model to determine the active principle concentration in agrochemicals. The proposed PLS model was made using 11 known commercial and 22 doped samples (11 under and 11 over dosed) for calibration and 22 different formulations as the validation set. For Buprofezin, Chlorsulfuron, Cyromazine, Daminozide, Diuron and Iprodione determination, the information in the spectral range between 1618 and 2630 nm of the reflectance spectra was employed. On the other hand, for Bensulfuron, Fenoxycarb, Metalaxyl, Procymidone and Tricyclazole determination, the first order derivative spectra in the range between 1618 and 2630 nm was used. In both cases, a linear remove correction was applied. Mean accuracy errors between 0.5 and 3.1% were obtained for the validation set. [Pg.92]

A. J. Charlton, W. H. Farrington, P. Brereton 2002, (Application of 1H NMR and multivariate statistics for screening complex mixtures quality control and authenticity of instant coffee), J. Agric. Food Chem. 50, 3098-3103. [Pg.488]

All data obtained by these novel techniques require a very deep and multifaceted analysis, in order to check the principal and fundamentals variables and to reject the others. In this scenario, chemometrics provide scientists with useful tools to interpret the large amounts of data generated by these complex analytical assays and allows for quality control, classification procedures, modelling studies. Discrimination between different molecules available as novel drugs and molecules having no interesting biological activities is easy by means of multivariate analysis. [Pg.50]

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. [Pg.195]

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. [Pg.421]

Y. Ren, W. Li, Y. Guo, R. Ren, L. Zhang, D. Jin and C. Hui, Study on quality control of metronidazole powder pharmaceuticals using near infrared reflectance first-derivative spectroscopy and multivariate statistical classification technique, Jisuanji Yu Ymgyong Huaxue, 14, 105-109 (1997). [Pg.488]

Analytical quality control (QC) efforts usually are at level I or II. Statistical evaluation of multivariate laboratory data is often complicated because the number of dependent variables is greater than the number of samples. In evaluating quality control, the analyst seeks to establish that replicate analyses made on reference material of known composition do not contain excessive systematic or random errors of measurement. In addition, when such problems are detected, it is helpful if remedial measures can be Inferred from the QC data. [Pg.2]

Data have been collected since 1970 on the prevalence and levels of various chemicals in human adipose (fat) tissue. These data are stored on a mainframe computer and have undergone routine quality assurance/quality control checks using univariate statistical methods. Upon completion of the development of a new analysis file, multivariate statistical techniques are applied to the data. The purpose of this analysis is to determine the utility of pattern recognition techniques in assessing the quality of the data and its ability to assist in their interpretation. [Pg.83]

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. [Pg.294]

The two research investigations reported here - the sensory quality control specification model and the application of sensory and analytical data for defining differences in tobacco aroma - both demonstrate the usefulness of multivariate analysis techniques for analyzing analytical and sensory data as well as correlating these data. Although these tasks do not compare in complexity to that of the prediction of sensory response to analytical data collected on cigarette smoke, our research to date has revealed no element which indicates that this is an impossible task. In fact, the results of these and similar... [Pg.128]

In recent years the term qualimetrics has been coined to refer to the use of chemometrics for the purposes of quality control (Massart et al. 1997). ft relates particularly to the use of multivariate analysis of process control measurements. Other texts on quality assurance in chemical laboratories include the latest edition of Garfield s book published by AOAC International (Garfield et al. 2000), material published through the Valid Analytical Measurement program by the LGC (Prichard 1995), and books from the Royal Society of Chemistry (Parkany 1993,1995 Sargent and MacKay 1995). Wenclawiak et al. (2004) have edited a series of Microsoft PowerPoint presentations on aspects of quality assurance. [Pg.9]

Majcen et al. [93] studied linear and nonlinear multivariate analysis in the quality control of industrial titanium dioxide white pigment using XRF spectrometry. [Pg.275]

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. [Pg.160]

Lachenmeier, D. W., Frank, W., Humpfer, E., Schafer, H., Keller, S., Mortter, M., and Spraul, M. (2005). Quality control of beer using high resolution nuclear magnetic spectroscopy and multivariate analysis. Eur. Food Res. Technol. 220, 215-221. [Pg.161]

Due to their persistent silica skeletons and their diversity, diatom remains provide a good record of past and present environmental conditions. Cameron (2004) recently showed that they could be used to compare samples that had been in contact with water and for the investigation of time of death in drowning. Through the recent advances in analytical quality control and use of multivariate statistics, their use in forensics is likely to develop further. In a similar way, phytoliths (the plant opal silica structure that accumulates in some plants) have been used to differentiate soils with otherwise similar mineralogy (Marumo and Yanai 1986). [Pg.295]

Martens and Martens produced a recent text which gives quite a detailed discussion on how multivariate metiiods can be used in quality control [14], but covers several aspects of modern chemometrics, and so should be classed as a general text on chemometrics. [Pg.10]


See other pages where Multivariate quality control is mentioned: [Pg.2006]    [Pg.2006]    [Pg.42]    [Pg.266]    [Pg.567]    [Pg.10]    [Pg.485]    [Pg.529]    [Pg.41]    [Pg.92]    [Pg.331]    [Pg.154]    [Pg.39]    [Pg.462]    [Pg.512]    [Pg.305]    [Pg.331]    [Pg.39]    [Pg.3383]    [Pg.592]   
See also in sourсe #XX -- [ Pg.232 ]




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