Evaluation Factors Analysis

As defined in detail in Chapter 3, the committee used five basic evaluation factors to assess the status of Tier 1 technologies. These factors were commented on earlier in this chapter in the respective evaluation factors analysis sections for each of the three Tier 1 international munitions processing technologies. 

State-of-the-art for data evaluation of complex depth profile is the use of factor analysis. The acquired data can be compiled in a two-dimensional data matrix in a manner that the n intensity values N(E) or, in the derivative mode dN( )/d , respectively, of a spectrum recorded in the ith of a total of m sputter cycles are written in the ith column of the data matrix D. For the purpose of factor analysis, it now becomes necessary that the (n X m)-dimensional data matrix D can be expressed as a product of two matrices, i. e. the (n x k)-dimensional spectrum matrix R and the (k x m)-dimensional concentration matrix C, in which R in k columns contains the spectra of k components, and C in k rows contains the concentrations of the respective m sputter cycles, i. e. ... 

Since the introduction of factor analysis for evaluation of depth profile data by Gaarenstroom [2.12], many papers have been published [e.g. 2.13-2.20]. With the help of factor analysis, three results can directly be obtained ... 

Human Factors Engineering/Ergonomics approach (control of error by design, audit, and feedback of operational experience) Occupational/process safety Manual/control operations Routine operation Task analysis Job design Workplace design Interface design Physical environment evaluation Workload analysis Infrequent... 

Performance-influencing factors analysis is an important part of the human reliability aspects of risk assessment. It can be applied in two areas. The first of these is the qualitative prediction of possible errors that could have a major impact on plant or personnel safety. The second is the evaluation of the operational conditions under which tasks are performed. These conditions will have a major impact in determining the probability that a particular error will be committed, and hence need to be systematically assessed as part of the quantification process. This application of PIFs will be described in Chapters 4 and 5. 

Because of peak overlappings in the first- and second-derivative spectra, conventional spectrophotometry cannot be applied satisfactorily for quantitative analysis, and the interpretation cannot be resolved by the zero-crossing technique. A chemometric approach improves precision and predictability, e.g., by the application of classical least sqnares (CLS), principal component regression (PCR), partial least squares (PLS), and iterative target transformation factor analysis (ITTFA), appropriate interpretations were found from the direct and first- and second-derivative absorption spectra. When five colorant combinations of sixteen mixtures of colorants from commercial food products were evaluated, the results were compared by the application of different chemometric approaches. The ITTFA analysis offered better precision than CLS, PCR, and PLS, and calibrations based on first-derivative data provided some advantages for all four methods. ... 

In their fundamental paper on curve resolution of two-component systems, Lawton and Sylvestre [7] studied a data matrix of spectra recorded during the elution of two constituents. One can decide either to estimate the pure spectra (and derive from them the concentration profiles) or the pure elution profiles (and derive from them the spectra) by factor analysis. Curve resolution, as developed by Lawton and Sylvestre, is based on the evaluation of the scores in the PC-space. Because the scores of the spectra in the PC-space defined by the wavelengths have a clearer structure (e.g. a line or a curve) than the scores of the elution profiles in the PC-space defined by the elution times, curve resolution usually estimates pure spectra. Thereafter, the pure elution profiles are estimated from the estimated pure spectra. Because no information on the specific order of the spectra is used, curve resolution is also applicable when the sequence of the spectra is not in a specific order. 

B.G.M. Vandeginste, F. Leyten, M. Gerritsen, J.W. Noor, G. Kateman and J. Frank, Evaluation of curve resolution and iterative target transformation factor analysis in quantitative analysis by liquid chromatography. J. Chemom., 1 (1987) 57-71. 

SEC-FUR with principal factor analysis has been used for the evaluation of 120 FUR spectra of 120 SEC fractions of thermally and radiolytically aged multicomponent systems consisting of Estane 5703, a nitroplas-ticiser (bis-2,2-dinitropropylacetal/bis-2,2-dinitropropyl-formal 1 1) and Irganox 1010 [708],... 

K. Faber and B.R. Kowalski, Critical evaluation of two F-tests for selecting the number of factors in abshact factor analysis. Anal Chim. Acta, 337, 57-71 (1997). 

As noted above, Raman images of skin are usually derived from hundreds to thousands of spectra. Examination of each individual spectrum is evidently impractical. Multivariate approaches are required to condense the information into a small set of components with a minimum loss of spectral information. We have found initial evaluation by principal component analysis (PCA) followed by factor analysis to be useful for this purpose. 

Faber, K. and Kowalski, B.R., Critical Evaluation of Two f-tests for Selecting the Number of Factors in Abstract Factor Analysis Anal. Chim. Acta 1997, 337, 57-71. 

Frequently, however, the lack of specificity in an analytical technique can be compensated for with sophisticated data processing, as described in the chemometrics chapter of this text (Chapter 8). Quinn and associates provide a demonstration of this approach, using fiber-optic UV-vis spectroscopy in combination with chemometrics to provide realtime determination of reactant and product concentrations.23 Automatic window factor analysis was used to evaluate the spectra. This technique was able to detect evidence of a reactive intermediate that was not discernable by off-line HPLC, and control charting of residuals was shown to be diagnostic of process upsets. Similarly, fiber-optic NIR was demonstrated by some of the same authors to predict reaction endpoint with suitable precision using a single PLS factor.24... 

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

The FAQLQ-PF and -TF were developed and validated in four stages (1) item generation using focus groups with both children and parents, expert opinion, and literature review (2) item reduction, using clinical impact and factor analysis (3) internal and test-retest reliability and construct validity were evaluated and (4) cross-cultural and content validity was examined by administering the questionnaire in a US sample (FAQLQ-PF, only). 

A comprehensive review on the application of HG to the AAS analysis of wines and beverages was authored by Baluja-Santos and Gonzalez-Portal [67]. The paper aimed to describe the fundamentals of HG, evaluate factors causing interferences and their possible elimination, decide which was the spectrometric method best fit to be used in combination with HG systems, and compare the obtained results with those of other techniques for the analysis of wine and beverages. Attention was focused on key elements such as As, Sb, Bi, Pb, Sn, Se, and Te. 

In addition to Ro and MF, inert matters of coal such as ash and sulfur, which do not soften and melt, are important properties of coal to be evaluated. The relations between FOB prices of coals from various parts of the world and the above-mentioned factors were analyzed by multiple regression analysis.(3) This permits economic evaluation of coals as raw materials for coke-making. Petroleum residual oils and petroleum coke can similarly be evaluated as raw materials. The most difficult problem here is how to evaluate factors corresponding to Ro and MF in coals.(6) This report presents primarily an estimation of such factors for evaluation. 

Comparison and ranking of sites according to chemical composition or toxicity is done by multivariate nonparametric or parametric statistical methods however, only descriptive methods, such as multidimensional scaling (MDS), principal component analysis (PCA), and factor analysis (FA), show similarities and distances between different sites. Toxicity can be evaluated by testing the environmental sample (as an undefined complex mixture) against a reference sample and analyzing by inference statistics, for example, t-test or analysis of variance (ANOVA). 

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