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Principal component method

A. Meister, Estimation of component spectra by the principal components method. Anal. Chim. Acta, 161 (1984) 149-161. [Pg.304]

Our band shape methods have made use of the principal component method of factor analysis (Pancoska etal., 1979 Malinowski, 1991) to characterize the protein spectra in terms of a relatively small number of coefficients (loadings) (Pancoska et al., 1994 1995 Baumruk et al., 1996). This approach is similar, in its initial stages, to various methods (Selcon, Variselect, etc.) that have been used for determining protein secondary structure from ECD data (Hennessey and Johnson, 1981 Provencher and Glockner, 1981 Johnson, 1988 Pancoska and Keiderling, 1991 Sreerama and Woody, 1993, 1994 Venyaminov and Yang, 1996). At this point, one can say these traditional quantitative methods have had little impact upon structural studies of denatured proteins. [Pg.167]

Among the multivariate statistical techniques that have been used as source-receptor models, factor analysis is the most widely employed. The basic objective of factor analysis is to allow the variation within a set of data to determine the number of independent causalities, i.e. sources of particles. It also permits the combination of the measured variables into new axes for the system that can be related to specific particle sources. The principles of factor analysis are reviewed and the principal components method is illustrated by the reanalysis of aerosol composition results from Charleston, West Virginia. An alternative approach to factor analysis. Target Transformation Factor Analysis, is introduced and its application to a subset of particle composition data from the Regional Air Pollution Study (RAPS) of St. Louis, Missouri is presented. [Pg.21]

Rachdawong P, Christensen E (1997) Determination of PCB sources by a principal component method with nonnegative constraints. Environ Sci Technol 31 2686-2691... [Pg.96]

D. Multivariate Statistical Analysis—Principal Components Method. .. 99... [Pg.87]

Another common method in factor analysis is principal component analysis. Principal component method can be considered a development of the previous procedure and commonly proceeds in a sequence of steps [74] as follows ... [Pg.181]

Streich, W.J., Dove, S. and Franke, R. (1980). On the Rotational Selection of Test Series. 1. Principal Component Method Combined with Multidimensional Mapping. JMed.Chem.,23,1452-1456. [Pg.650]

When the principal components method was derived, matrix differentiation was used to determine the principal component vector p which minimized the sum of squared deviation. For this the symmetric mean centred variance-covariance matrix (X X) (X - X) was involved. [Pg.519]

The SIMCA method is based on the construction of principal components (PCs) which effectively fit a box (or hyper-box in N dimensions) around each class of samples in a data set. This is an interesting application of PCA, an unsupervised learning method, to different classes of data resulting in a supervised learning technique. The relationship between SIMCA and PLS, another supervized principal components method, can be seen clearly by reference to Section 7.3. [Pg.145]

Yang Kai, Yang Guangbin, 1997. Performance degradation analysis using principal component method. Annual Reliability and Maintainability Symposium, 136-141. [Pg.841]

The principal component method may be explained quite simply In terms of Its application to observations of particle length (X]), width (X2> and thickness (X3). [Pg.340]

Some methods that paitly cope with the above mentioned problem have been proposed in the literature. The subject has been treated in areas like Cheraometrics, Econometrics etc, giving rise for example to the methods Partial Least Squares, PLS, Ridge Regression, RR, and Principal Component Regression, PCR [2]. In this work we have chosen to illustrate the multivariable approach using PCR as our regression tool, mainly because it has a relatively easy interpretation. The basic idea of PCR is described below. [Pg.888]

The essential slow modes of a protein during a simulation accounting for most of its conformational variability can often be described by only a few principal components. Comparison of PGA with NMA for a 200 ps simulation of bovine pancreatic trypsic inhibitor showed that the variation in the first principal components was twice as high as expected from normal mode analy-si.s ([Hayward et al. 1994]). The so-called essential dynamics analysis method ([Amadei et al. 1993]) is a related method and will not be discussed here. [Pg.73]

Grubmiiller described a method to induce conformational transitions in proteins and derived rate constants for these ([Grubmiiller 1994]). The method employs subsequent modifications of the original potential function based on a principal component analysis of a short MD simulation. It is discussed in more detail in the chapter of Eichinger et al. in this volume. [Pg.74]

We have to apply projection techniques which allow us to plot the hyperspaces onto two- or three-dimensional space. Principal Component Analysis (PCA) is a method that is fit for performing this task it is described in Section 9.4.4. PCA operates with latent variables, which are linear combinations of the original variables. [Pg.213]

To gain insight into chemometric methods such as correlation analysis, Multiple Linear Regression Analysis, Principal Component Analysis, Principal Component Regression, and Partial Least Squares regression/Projection to Latent Structures... [Pg.439]

The previously mentioned data set with a total of 115 compounds has already been studied by other statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis, and the Partial Least Squares (PLS) method [39]. Thus, the choice and selection of descriptors has already been accomplished. [Pg.508]

The dimensionality of a data set is the number of variables that are used to describe eac object. For example, a conformation of a cyclohexane ring might be described in terms c the six torsion angles in the ring. However, it is often found that there are significai correlations between these variables. Under such circumstances, a cluster analysis is ofte facilitated by reducing the dimensionality of a data set to eliminate these correlation Principal components analysis (PCA) is a commonly used method for reducing the dimensior ality of a data set. [Pg.513]

Multiple linear regression is strictly a parametric supervised learning technique. A parametric technique is one which assumes that the variables conform to some distribution (often the Gaussian distribution) the properties of the distribution are assumed in the underlying statistical method. A non-parametric technique does not rely upon the assumption of any particular distribution. A supervised learning method is one which uses information about the dependent variable to derive the model. An unsupervised learning method does not. Thus cluster analysis, principal components analysis and factor analysis are all examples of unsupervised learning techniques. [Pg.719]

The field points must then be fitted to predict the activity. There are generally far more field points than known compound activities to be fitted. The least-squares algorithms used in QSAR studies do not function for such an underdetermined system. A partial least squares (PLS) algorithm is used for this type of fitting. This method starts with matrices of field data and activity data. These matrices are then used to derive two new matrices containing a description of the system and the residual noise in the data. Earlier studies used a similar technique, called principal component analysis (PCA). PLS is generally considered to be superior. [Pg.248]

Chemical Properties. Elemental analysis, impurity content, and stoichiometry are determined by chemical or iastmmental analysis. The use of iastmmental analytical methods (qv) is increasing because these ate usually faster, can be automated, and can be used to determine very small concentrations of elements (see Trace AND RESIDUE ANALYSIS). Atomic absorption spectroscopy and x-ray fluorescence methods are the most useful iastmmental techniques ia determining chemical compositions of inorganic pigments. Chemical analysis of principal components is carried out to determine pigment stoichiometry. Analysis of trace elements is important. The presence of undesirable elements, such as heavy metals, even in small amounts, can make the pigment unusable for environmental reasons. [Pg.4]

There are no natural sources of pyridine compounds that are either a single pyridine isomer or just one compound. For instance, coal tar contains a mixture of bases, mosdy aLkylpyridines, in low concentrations. Few commercial synthetic methods produce a single pyridine compound, either most produce a mixture of aLkylpyridines, usually with some pyridine (1). Those that produce mono- or disubstituted pyridines as principal components also usually make a mixture of isomeric compounds along with the desired material. [Pg.332]

The sodium hydroxide is titrated with HCl. In a thermometric titration (92), the sibcate solution is treated first with hydrochloric acid to measure Na20 and then with hydrofluoric acid to determine precipitated Si02. Lower sibca concentrations are measured with the sibcomolybdate colorimetric method or instmmental techniques. X-ray fluorescence, atomic absorption and plasma emission spectroscopies, ion-selective electrodes, and ion chromatography are utilized to detect principal components as weU as trace cationic and anionic impurities. Eourier transform infrared, ft-nmr, laser Raman, and x-ray... [Pg.11]

In the case of low temperature tar, the aqueous Hquor that accompanies the cmde tar contains between 1 and 1.5% by weight of soluble tar acids, eg, phenol, cresols, and dihydroxybenzenes. Both for the sake of economics and effluent purification, it is necessary to recover these, usually by the Lurgi Phenosolvan process based on the selective extraction of the tar acids with butyl or isobutyl acetate. The recovered phenols are separated by fractional distillation into monohydroxybenzenes, mainly phenol and cresols, and dihydroxybenzenes, mainly (9-dihydroxybenzene (catechol), methyl (9-dihydtoxybenzene, (methyl catechol), and y -dihydroxybenzene (resorcinol). The monohydric phenol fraction is added to the cmde tar acids extracted from the tar for further refining, whereas the dihydric phenol fraction is incorporated in wood-preservation creosote or sold to adhesive manufacturers. Naphthalene Oils. Naphthalene is the principal component of coke-oven tats and the only component that can be concentrated to a reasonably high content on primary distillation. Naphthalene oils from coke-oven tars distilled in a modem pipe stiU generally contain 60—65% of naphthalene. They are further upgraded by a number of methods. [Pg.340]

A convenient method for assessing the extent of surface oxidation is the measurement of volatile content. This standard method measures the weight loss of the evolved gases on heating up to 950°C in an inert atmosphere. The composition of these gases consists of three principal components hydrogen, carbon monoxide, and carbon dioxide. The volatile content of normal furnace blacks is under 1.5%, and the volatile content of oxidized special grades is 2.0 to 9.5%. [Pg.543]

Other chemometrics methods to improve caUbration have been advanced. The method of partial least squares has been usehil in multicomponent cahbration (48—51). In this approach the concentrations are related to latent variables in the block of observed instmment responses. Thus PLS regression can solve the colinearity problem and provide all of the advantages discussed earlier. Principal components analysis coupled with multiple regression, often called Principal Component Regression (PCR), is another cahbration approach that has been compared and contrasted to PLS (52—54). Cahbration problems can also be approached using the Kalman filter as discussed (43). [Pg.429]


See other pages where Principal component method is mentioned: [Pg.14]    [Pg.368]    [Pg.174]    [Pg.113]    [Pg.330]    [Pg.330]    [Pg.343]    [Pg.14]    [Pg.368]    [Pg.174]    [Pg.113]    [Pg.330]    [Pg.330]    [Pg.343]    [Pg.357]    [Pg.11]    [Pg.36]    [Pg.702]    [Pg.722]    [Pg.168]    [Pg.419]    [Pg.421]    [Pg.425]    [Pg.426]    [Pg.426]   
See also in sourсe #XX -- [ Pg.174 ]




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