Principal Components Analysis resins

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... 

Parallel analyses by IR, thin-layer chromatography (TLC), and gas chromatography-mass spectrometry (GC-MS) of organic remains adhering to shards of ancient amphoras excavated in the harbor of Carthage (Tunisia) identified these remains as pine pitches. Capillary GC of methylated acid fractions showed abietic acid, dehydroabietic acid, and 7-ketodehydroabietic acid as the principal components. Two-dimensional TLC of untreated ether extracts revealed abietic acid in 12 of 31 samples and dehydroabietic acid in 26 of 31 samples. IR spectra of solid, raw samples indicated the presence of isopropyl groups, characteristic of the abietane skeleton, in 80% of the samples. Rapid and convenient analysis by TLC and IR was, in most cases, sufficient to identify pine resin products even after extensive pyrolytic and oxidative degradation. 

The system has thus been reduced to a univariate problem and conventional analysis may be employed for subsequent quantification if required. In a closely related study of the curing of a cyanate ester rather than an epoxy resin (Cooper, 1999) a similar profile to Figure 3.46 was obtained after PCA (Figure 3.47), and a comparison with the univariate analysis of single peak intensities that are the major peaks in the first principal component after mean centring was made. 

Each oil-dispersant combination shows a unique threshold or onset of dispersion [589]. A statistic analysis showed that the principal factors involved are the oil composition, dispersant formulation, sea surface turbulence, and dispersant quantity [588]. The composition of the oil is very important. The effectiveness of the dispersant formulation correlates strongly with the amount of the saturate components in the oil. The other components of the oil (i.e., asphaltenes, resins, or polar substances and aromatic fractions) show a negative correlation with the dispersant effectiveness. The viscosity of the oil is determined by the composition of the oil. Therefore viscosity and composition are responsible for the effectiveness of a dispersant. The dispersant composition is significant and interacts with the oil composition. Sea turbulence strongly affects dispersant effectiveness. The effectiveness rises with increasing turbulence to a maximal value. The effectiveness for commercial dispersants is a Gaussian distribution around a certain salinity value. 

One of the main problems associated with analysis of Cannabis resin is the difficulty of purifying the individual components. The principal active... 

---