Peak using principal component analysi

To compare the scent profiles of individuals we selected 11 compounds and calculated their relative peak areas. Principal Component Analysis (PCA) was used to compare the individual peak areas of the four individuals. PCA is a multivariate statistical method which reduces the dimensions of a single group of data by producing a smaller number of abstract variables (Jolliffe, 1986). For this analysis we used multiple samples of each individual and calculated all factors on the basis of a correlation matrix. The resulting first and second factor accounted for a total of 99.17 % of the variance in proportional peak area. 

Using principal component analysis, Dreassi et al. (92) investigated interactions between skin and water or lipids. Displacements of the peaks were observed in the... 

The results show that DE-MS alone provides evidence of the presence of the most abundant components in samples. On account of the relatively greater difficulty in the interpretation of DE-MS mass spectra, the use of multivariate analysis by principal component analysis (PCA) of DE-MS mass spectral data was used to rapidly differentiate triterpene resinous materials and to compare reference samples with archaeological ones. This method classifies the spectra and indicates the level of similarity of the samples. The output is a two- or three-dimensional scatter plot in which the geometric distances among the various points, representing the samples, reflect the differences in the distribution of ion peaks in the mass spectra, which in turn point to differences in chemical composition of... 

A sample may be characterized by the determination of a number of different analytes. For example, a hydrocarbon mixture can be analysed by use of a series of UV absorption peaks. Alternatively, in a sediment sample a range of trace metals may be determined. Collectively, these data represent patterns characteristic of the samples, and similar samples will have similar patterns. Results may be compared by vectorial presentation of the variables, when the variables for similar samples will form clusters. Hence the term cluster analysis. Where only two variables are studied, clusters are readily recognized in a two-dimensional graphical presentation. For more complex systems with more variables, i.e. //, the clusters will be in -dimensional space. Principal component analysis (PCA) explores the interdependence of pairs of variables in order to reduce the number to certain principal components. A practical example could be drawn from the sediment analysis mentioned above. Trace metals are often attached to sediment particles by sorption on to the hydrous oxides of Al, Fe and Mn that are present. The Al content could be a principal component to which the other metal contents are related. Factor analysis is a more sophisticated form of principal component analysis. 

As in many such problems, some form of pretreatment of the data is warranted. In all applications discussed here, the analytical data either have been untreated or have been normalized to relative concentration of each peak in the sample. Quality Assurance. Principal components analysis can be used to detect large sample differences that may be due to instrument error, noise, etc. This is illustrated by using samples 17-20 in Appendix I (Figure 6). These samples are replicate assays of a 1 1 1 1 mixture of the standard Aroclors. Fitting these data for the four samples to a 2-component model and plotting the two first principal components (Theta 1 and Theta 2 [scores] in... 

Then using these 91 peaks only, the original data set was reexamined by principal components analysis. Eigenvalues greater than one were plotted to determine how many factors should be retained. After variraax rotation, the factor scores were plotted and interpreted. 

Raman spectroscopy can also directly benefit TE analysis by non-invasively monitoring the growth and development of ECM by different cells on a multitude of scaffold materials exposed to various stimuli (e.g. growth factors, mechanical forces and/or oxygen pressures). Indeed the non-invasive nature of Raman spectroscopy enables the determination of the rate of ECM formation and the biochemical constituents of the ECM formed. Univariate (peak area, peak ratios, etc.) and multivariate analytical techniques (e.g. principal component analysis (PCA)) can be used to determine if there are any significant differences between the ECM formed on various scaffolds and/or cultured with different environmental parameters, and what these biochemical differences are. Least square (LS) modelling, for example, could allow the quantification of the relative components of the ECM formed (Fig. 18.3) [4, 38],... 

Principal component analysis (PCA) is commonly used to identify those analytes that are most different from the control samples and provides for a visual characterization of the data set. Following data reduction, PCA is used to find linear combinations (eigenvectors) of the original resolved peaks most different from controls, and these vectors are used to create visually characterize data sets. The PCA Eigenvectors have several desirable properties, including (a) the combinations are not correlated and (b) they can be rank-ordered (from most to least). 

Figure 16.2 Principal component analysis biplot using the total cuticular mixtures of Smeringerus mesaensis, summer adult males (10 HaM) and females (10 HaF) with peaks projection (pi to 83) involved in sex-differrention.

Principal component analysis was used to examine the trends present in the training-set data. Figure 9.11 shows a plot of the two largest principal components of the 85 GC peaks obtained from the 221 gas chromatograms. Each chromatogram is represented as a point in the principal component map. The JP-4, JP-7, and JPTS... 

Statistical analyses. Three-way analyses of variance treating judges as a random effect were performed on each descriptive term using SAS Institute Inc. IMP 3.1 (Cary, North Carolina). Principal component analysis of the correlation matrix of the mean intensity ratings was performed with Varimax rotation. Over 200 GC peaks... 

Several factors are important in quantitative work. First, an internal reference band is needed for calculating relative intensity (either area or peak) of the band being used in quantitation. Although an external standard can be used, the internal band-ratio calculation is more reliable. However, if a chemometrics approach (e.g., principal components analysis [PCA], principal components regression [PCR], or PLS) is used, a standard is not required. 

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