Multivariate comparison

Nygren M, Hansson M, Sj str+m M, et al. 1988. Development and validation of a method for determination of PCDDs and PCDFs in human blood plasma A multivariate comparison of blood and adipose tissue levels between Vietnam veterans and matched controls. Chemosphere 17 1663-1692. 

The topic of the previous sections of this chapter has been univariate population-based reference values and quantities derived from them. Such values do not, however, fit the common clinical situation in which observed values of several different laboratory tests are available for interpretation and decision making. For example, the average number of individual clinical chemistry tests requested on one specimen received in the author s laboratory is 9.7. There are two models for interpretation by comparison in this situation. We can compare each observed value with the corresponding reference values or inteiwal (i.e., we perform multiple, univariate comparisons) or we can consider the set of observed values as a single multivariate observation and interpret it as such by a multivariate comparison. In this section, the relative merits of these two approaches are discussed, and methods for the latter type of comparison are presented. 

Yunker MB, MacDonald RW, Fowler BR, Cretney WJ, Dallimore SR, McLaughlin FA (1991) Geochemistry and fluxes of hydrocarbons to the Beaufort Sea shelf A multivariate comparison of fluvial inputs and coastal erosion of peat using principal components analysis. Geochim Cosmochim Acta 55, 255-273. 

Normally, one does not have hue values of the elements of the slope mah ix M for comparison. It is always possible, however, to obtain y, the vector of predicted y values at each of the known Xi from any of the slope vectors m obtained by the multivariate procedure... 

D. Coomans, I. Broeckaert, M. Jonckheer and D.L. Massart, Comparison of multivariate discrimination techniques for clinical data—Application to the thyroid functional state. Meth. Inform. Med., 22 (1983) 93-101. 

There are four main types of data that frequently occur in sensory analysis pair-wise differences, attribute profiling, time-intensity recordings and preference data. We will discuss in what situations such data arise and how they can be analyzed. Especially the analysis of profiling data and the comparison of such data with chemical information calls for a multivariate approach. Here, we can apply some of the techniques treated before, particularly those of Chapters 35 and 36. 

Use of multivariate approaches based on classification modelling based on cluster analysis, factor analysis and the SIMCA technique [98,99], and the Kohonen artificial neural network [100]. All these methods, though rarely implemented, lead to very good results not achievable with classical strategies (comparisons, amino acid ratios, flow charts) and, moreover it is possible to know the confidence level of the classification carried out. 

However, these statements are generalizations, and it is not necessarily true to say that all biotransformations will be greener than the chemical alternative. Therefore, it is important to analyse each comparison objectively on a case-by-case basis using a multivariate process to take into account the complexity of the analysis. Designing greener processes involves, for example ... 

Figure 17.3 gives some comparisons of the performance of the multivariable DMC stmctuie with the diagonal stmcture. Three linear transfer-function models are presented, varying from the 2 x 2 Wood and Berry column to the 4 x 4 sidestream column/stripper complex configuration. The DMC tuning constants used for these three examples are NP = 40 and NC = 15. See Chap. 8, Sec. 8.9. 

Shapiro Wilks W-test for normal data Shapiro Wilks W-test for exponential data Maximum studentlzed residual Median of deviations from sample median Andrew s rho for robust regression Classical methods of multiple comparisons Multivariate methods... 

Summarizing, sequence comparison allows us to compute the quantitative distance between any two linguistic assertions. In this research, the assertions are those derived from the procedures described above. Once distances (similarity data) are computed that satisfy the metric axioms, the matrix of inter-assertion distances can be analyzed using a variety of multivariate statistical techniques. 

R. Heikka, P. Minkkinen, and V-M Taavitsainen, Comparison of variable selection and regression methods in multivariate calibration of a process analyzer. Process Control and Quality, 6, 47-54 (1994). 

E.V. Thomas and D.M. Haaland, Comparison of multivariate calibration methods for quantitative spectral analysis. Anal. Chem., 62, 1091-1099 (1990). 

The use of weekly paclitaxel (45 mg/m2) and carboplatin (100 mg/m2) has been reported (135,136). This combination as used to treat 62 patients with stage III and IV squamous cell carcinoma of the head and neck concurrently with radical radiotherapy lead to a clinical complete response rate of 75 % both at the primary and in the neck with a median survival of 33 mo (135). The authors report a retrospective comparison to similar patients treated with concurrent carboplatin alone or concurrent carboplatin and bleomycin and show on multivariate analysis that complete response and treatment with paclitaxel were predictive for survival (136). This result while encouraging is retrospective in nature and is subject to potential bias. 

PLS was advantageous when studying the relationship of the toxicity of thiify triazines on Daphnia magna (25), and in a comparison between Hansch analysis and PLS analysis, using the same data set, it was shown that tiie multivariate approach of PLS provided more useful models than the Hansch type approach (26). 

Conventional and multivariate methods were used to establish the best pyrolysis and atomisation temperatures and the chemical modifier (centred full factorial designs). A comparison is presented... 

M. L. Griffiths, D. Svozil, P. J. Worsfold, S. Denham and E. H. Evans, Comparison of traditional and multivariate calibration techniques applied to complex matrices using inductively coupled plasma atomic emission spectroscopy, J. Anal. At. Spectrom., 15, 2000, 967-972. 

Samara C, Kouimtzis TH, Katsoulos GA (1994) Characterisation of airborne particulate matter in Thessaloniki, Greece D Part III comparison of two multivariate modelling approaches for... 

Raman spectroscopy has its main strength in the combination of a fairly high chemical selectivity and a true remote sensing capability. In comparison, NIR has been used extensively in the manufacturing industry due to its ruggedness and simplicity with respect to interfacing of probes to process vessels. However, due to fairly poor spectral selectivity it has to be paired with multivariate data evaluation and is thus sometimes considered as a black box technique. Mid-IR, on the other hand, offers a high selectivity and is also well established... 

Heikka, R., Minkkinen, P. and Taavitsainen, V.-M., Comparison of Variable Selection and Regression Methods in Multivariate Calibration of a Process Analyzer Process Contr. Qual. 1994, 6, 47-54. 

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