Multivariate analysis techniques

Self-modehng multivariate analysis technique see SIMPLISMA... 

Analytical Data Analysis. The development and commercialization of the gas chromatograph in the mid 1950 s had a dramatic effect on flavor research because the technique made it possible to obtain objective measurements of the numerous compounds which made up the flavor of the product under investigation. Data analysis was reasonably simple and straightforward, as the number of resolved peaks was small. However, as chromatographic techniques were refined and high resolution capillary columns and microprocessor controlled GC s were introduced, the use of computers and multivariate analysis techniques have become essential for data analysis and reduction. 

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... 

A. M. Grotti, Improving the analytical performances of inductively coupled plasma optical emission spectrometry by multivariate analysis techniques, Ann. Chim. (Rome), 94, 2004, 1-15. 

Principal component analysis (PCA), factor analysis (FA) and cluster analysis (CA) are some of the most widely used multivariate analysis techniques applied to... 

The application of chemometric or multivariate analysis techniques to nuclear magnetic resonance (NMR) spectroscopic data is reviewed. Descriptions of the different processing and data manipulation procedures being utilized to produce reproducible input data sets for chemometric analysis are discussed. A brief review of... 

Multivariate analysis techniques were applied to peak areas obtained by CE to evaluate the ripening time of the cheese. Data were autoscaled prior to model calculations. This normalization involved the subtraction of the mean and then the division of each value of a given variable by the standard deviation of all the values for this variable over the entire sample collection period (48). After normalization, all variables had the same weight because they had a mean of zero and unitary variance. 

The basic principle of experimental design is to vary all factors concomitantly according to a randomised and balanced design, and to evaluate the results by multivariate analysis techniques, such as multiple linear regression or partial least squares. It is essential to check by diagnostic methods that the applied statistical model appropriately describes the experimental data. Unacceptably poor fit indicates experimental errors or that another model should be applied. If a more complicated model is needed, it is often necessary to add further experimental runs to correctly resolve such a model. 

According to Ennis (1988), the application of the various multivariate analysis techniques (factor, cluster, discriminant analysis, multidimensional scaling) to classification in sensory analysis has been very valuable but is of little help for understanding the modes of perception. Mathematical models are proposed for predicting human sensory responses and the author concludes that they need development before they are able to improve the understanding of the complex perceptions associated with foods and beverages . 

Cheng, M. D., Lioy, R J., and Opperman, A. J. (1988) Resolving PMi0 data collected in New Jersey by various multivariate analysis techniques, in PMl0 Implementation of Standards, C. V. Mathai and D. H. Stonefield, eds., Air Pollution Control Association, Pittsburgh, PA, pp. 472-483. 

M. Tatsuoka, Multivariate Analysis Techniques for Educational and Psychological Research, Wiley, New York (1971). 

The analysis used to test the Hi and H2 hypotheses and the profitability comparison is composed of five steps. Each step uses a specific multivariate analysis technique... 

From a conceptual point of view, it must be noted that thanks to the generalized use of multivariate analysis techniques, researchers in sensory science have started to pay more attention to the relative positioning of the objects rather than to product scores on separate attributes. As a result, rather than measuring the stimulus by conventional physical means as a psychophysicist might do, one might create a multidimensional space, and then use the coordinates of that space as a surrogate set of physical measures made on the same stimuli in the test set (Moskowitz, 2003). 

Multivariate analysis techniques such as principal component analysis (PCA) and multivariate curve resolution (MCR) providing useful tools for gaining important information from large data sets, have been employed to interpret TOF-SIMS spectra of macromolecules [39 9]. PCA is especially useful to characterize the... 

Gilmore, I.S., Wagner, M.S. (2009) Special edition on multivariate analysis techniques. Surf. Interface Anal, 41, issues 2 and 8,76-142. 

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