Figure 34. Factor spectrum of the main factor, derived from a series of pyrolysis mass spectra of poliomyelitis-virus concentrates, obtained by purification on DEAE-Sephadex columns. The positive part of the spectrum (a) represents the DEAE-dextran pattern (compare with Atlas); the negative part reflects mainly pyrolysis fragments of the virus material (note the sulphur-containing fragments at m/z 34, H2S+"; 48, CH3SH "; 64, S02+-; 76, CS2+'). The calculation was based on a series of eight virus batches together with some standard samples prepared from two of the batches by addition of DEAE-dextran at concentrations of 20, 40, and 80 ppm, respectively. Semi-quantitative results are given in Figure (b). A linear relationship appears to exist between the polymer concentration and the factor score of the main factor (quadruplicate determinations are given). All tested virus batches (designated by numbers 132, 137, ..., 319; Figure (c)) have DEAE-dextran concentrations not exceeding the detection limit (20 ppm).
developed by McLafferty et al. (ref. 142). So far, the use of these latter techniques has not been reported in Py-MS.
With respect to the factor rotation methods it should be noted that owing to the complex character of pyrolysis mass spectra mathematical criteria usually cannot be found. Therefore, rotation techniques such as Varimax (ref. 138) and the method of Knorr and Futrell (139) may not perform satisfactorily. Moreover, target transformation and matching techniques (which use library criteria) are less well suited for the analysis of complex pyrolysis mass spectra, as the component sub-patterns can be influenced by the total sample matrix (see Section 4.1). For these reasons, Windig et al. (ref. 140) adapted the "graphical rotation" method (ref. 143), an empirical factor rotation technique, for interpreting pyrolysis mass spectra. The example in Figure 35 represents the analysis of a small set of pyrolysis mass spectra of yeast species in order to obtain a more quantitative description of the main groups of biochemical components in these microorganisms. The spectra of such yeasts (Figure 35a) are mainly composed of series of fragment peaks indicative of neutral hexose and pentose-type carbohydrates, N-acetylamino sugars and proteins, which are building blocks present in many different homo- and hetero-biopolymers of the microorganisms. The first, unrotated factor calculated is given in Figure 35b and the positive part describes the differences in the overall protein sub-patterns very well. The negative part of this factor represents a mixed pattern of fragment peaks attributable to a number of other component groups. On rotation in the plane through the two factor axes this pattern changes, until at 60° a set of peaks is observed (Figure 35c) which optimally represents a fragment pattern of pentose-type carbohydrates (compare with Atlas). At the same rotation angle the positive part of factor 2 (Figure 35d) optimally represents the highly correlated (not separable) hexose and N-acetylhexosamine sub-patterns (see Atlas), whereas the negative part of this second factor (not shown) now represents the protein pattern of the first, unrotated factor. The factor scores of these optimized "component factors" can be used as a semi-quantitative measure of the chemical components as shown in Figure 35e.
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