Anal. Chem. 1987, 59, 1945-1951
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Curie-Point Pyrolysis Atmospheric Pressure Chemical Ionization Mass Spectrometry: Preliminary Performance Data for Three Biopolymers A. Peter Snyder* and Johannes H. Kremer' U.S. A r m y Chemical Research, Development and Engineering Center, Aberdeen Proving Ground, Maryland 21010-5423 Henk L. C. Meuzelaar, Willem Windig, and Koli Taghizadeh Biomaterials Profiling Center, University of Utah, Salt Lake City, Utah 84108
A Curie-point pyrolysis Inlet system was interfaced to an atmospherlc pressure chemical ionization (APCI) source on a quadrupole mass spectrometer. erolysis-APCI (Py-APCI) mass spectra of herring DNA, bovine serum aibumln, and bovine liver glycogen were compared and contrasted with low-voltage electron ionization mass spectra obtalned under similar pyrolysis condltlons. Factor analysis of a combined set of APCI and low-voltage electron Ionization (EI) mass spectra separated the spectra according to Ionization technique as well as biopolymer type. Discriminant analysis of mass spectral data categorized according to biopolymer type revealed patterns common to both ionization techniques. These common ionization-independent spectral patterns may eventually have Important applications for modeling and predicting Py-APCI spectra from Py-E1 spectra and vice versa.
In comparison to electron ionization (EI), atmospheric pressure chemical ionization (APCI) in the positive ion mode promotes soft ionization of a gaseous sample molecule, primarily by proton transfer from a hydrated proton reagent ion. Moreover, APCI lends itself to a "wall-less" ion source configuration and allows convenient accessibility for introduction of a sample to the ion source. Some of the sample introduction systems that have been used in combination with APCI are direct atmospheric and confined space (headspace) analyses of gases (1-4), direct insertion probe ( 5 ) ,thermogravimetry (6, 7), high-pressure liquid chromatography (81,supercritical fluid chromatography (9),and laser-assisted ionization (10). In view of the highly successful performance of APCI sources in aerosol detection applications, e.g. for detection of chemical , warfare agents a t sub parts per billion (ppb) levels ( l l ) there is considerable interest in extending the applicability of APCI-MS to nonvolatile high molecular weight components in aerosols, e.g. biological agents. In order to analyze nonvolatile aerosol components, pyrolysis techniques have proven to be fast, reproducible methods leading to sample volatilization. One of the most widely used heating systems in pyrolysis mass spectrometry (Py-MS) is the Curie-point pyrolysis technique, which involves rapid (102-104K/s) heating of a ferromagnetic sample device, e.g. filament, tube, or foil (12, 13),in a radio frequency field. Curie-point pyrolysis in combination with low-voltage EI-MS has been successfully applied to the characterization of biopolymers (14,15) as well as more complex biological systems such as bacteria (16-20), yeasts (21,22),range grasses (23),normal and leukemic white blood cells (24, 25), body fluids (261, and mycobacteria (27). Present address: Wehrwissenschaftliche Dienststelle der Bundeswehr fuer ABC-Schutz,Humboldtatrasze, D3042 Munster, FRG. 0003-2700/87/0359-1945$01.50/0
This paper reports the coupling of a Curie-point pyrolyzer to an APCI quadrupole MS system. The potential of PyAPCI-MS is examined in terms of reproducibility and information yield by using a test set of three biopolymers (herring DNA, bovine serum albumin, and bovine liver glycogen). The APCI data obtained are compared with PyEI-MS data on the same biopolymer suite but obtained in a different laboratory.
EXPERIMENTAL SECTION Sample Preparation. Herring DNA, bovine serum albumin (BSA), and bovine liver glycogen (GLY) were obtained from Sigma. (Py-APCZ-MS). Two milligrams of each biopolymer was dissolved in 4 mL of deionized, distilled water (pH 7.6); 4 WLof the solution (2 wg of biopolymer) was applied to the tip of a 770 "C Curie-point wire and dried by continuous, manual rotation above a hot air gun. The end of the wire was 1.3 cm from the quartz glass reaction tube opening. Water was used as solvent, because it dissolves the highly polar biopolymers, facilitating sample transfer to the wire, whereas organic solvents are likely to become partially occluded within the dried sample on the wire and to deplete source reagent protons when released in the pyrolyzate. (Py-EZ-MS). Suspensions of each of the three biopolymers, with 2 mg of biopolymer per mL spectrograde methanol were prepared; 5 ILLof each suspension (10 kg of biopolymer) was applied to a 770 "C Curie-point wire. The methanol was evaporated under continuous, mechanical rotation of the wire. Methanol was used as the sample carrier, because it was found to be optimal in a survey of different liquids (28). Pyrolysis Mass Spectrometry. (Py-APCZ-MS). The Curie-point pyrolysis probe was designed and constructed at the University of Utah, Biomaterials Profiling Center, for the ion source of the Sciex TAGA 6000 triple quadrupole mass spectrometer. Figure 1 presents a schematic of the probe positioned in the mass spectrometer ion source. The glass reaction tube was held inside the radio frequency (rf) coil by a nylon Swagelok/ polycarbonate shaft assembly (see insert, Figure 1).A small Teflon sleeve was placed over the Curie-point wire on the inside of the probe to prevent it from slipping out of the reaction tube. The sleeve also contacted a support rod inside the polycarbonate shaft to prevent the wire from slipping within the shaft. A gentle stream of nitrogen gas (40 mL/min) was directed into the shaft (a in Figure 1)and through the glass reaction tube to provide a nonoxidative atmosphere for the pyrolysis event. The polycarbonate shaft was inserted in the outer copper tube, which housed the rf coil. The copper tube remained in the ion source chamber when the shaft was removed. A Teflon block interface supplied the rf signal (e in Figure 1)directly to the coil, which was grounded to the copper tube. The interface also supplied a 1L/min nitrogen mantle gas flow (b in Figure 1) over the shaft/glass reaction tube assembly in order to provide a focusing effect for the pyrolyzate as it entered the ion source region. A Teflon cap with a center hole was placed on the front of the copper tube. The position of the complete probe with respect to the point discharge (needle) was optimized for satisfactory background reagent ion and sample @ 1987 American Chemical Society
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Flgure 1. Schematic representatlon of ths Fy-APC-MS instrumental configuration with the pyrolysis reactor detailed in the inset: (a), (b). and (c) represent the Pyrolysis reactor. mantle. and plenum n%ogen gas flow inlets. respectively; (d) static exhaust, (e) rf frequency connection to the coil, (f) interface plate.
ion distributions, A JHP-3S Curie-point pyrolyzer from Japan Analytical Industry Co., Ltd., Tokyo, Japan, delivered at 800 W 783.87 kHz to the Curie-point wire. The temperature rise time (TRT) was 1.5 s, and the total beating time (THT) was 10 8. Five replicate analyses were performed hy using one Curie-point wire for each biopolymer suspension. Each wire was pyrolyzes between each experiment in order to remove residual deposits. The TAGA 6000 triple quadrupole mass spectrometer was used to ohtain conventional mass spectra, with the first quadrupole scanned and the second and third quadrupoles operated in the rf-only mode. Nitrogen gas was introduced into a plenum (c in Figure 1)separating the atmospheric pressure ionization region from the 2.4 x 10" Torr analyzer region at a rate of 900 mL/min. The potential difference between the point (needle) and plane (interface) (fin Figure 1)was approximately 2400 V. The resultant electric field ionized the plenum nitrogen gas and initiated a cascade of reactions that resulted in a dynamic balance of protonated water cluster ions, [H(H2O).]+, n = 14. The reagent-sample ion clusters entered the ion lens assembly from 3-mm and 0.05-mm orifices in the interface plate and first ion lens (Ll) plate, respectively. In order to dissociate the protonated hydrates, which occur under atmosphere pressure conditions in high molecular weight elustern, the voltages on L1 and L2 were set to 70 and 58 V, respectively, which were similar to those of Dawson et al. (29, 30). These conditions produced the characteristic low molecular weight reagent ions H(H,O).+ in relative abundance n = 1 (6%), n = 2 1100%). The remaining ,~~~ n = 3 (66%). ,~~ ~ and ,n = 4 .(7%). , , ~ ~ ~com- ~ puter-c&r&xi parameters were as follows: ion lenses wGe ( ~ 3 ) +50 V, (L4) +40 V, (L5) -250 V, (L6) +25 V, (L10) +50 V; quadrupole 1,2, and 3 rod offsets were +45, -30, and +45 V, respectively; the resolution setting on quadrupole 1 was 130. Details of their functional roles can be found elsewhere (31). Mass spectra were obtained from m / z 56 to 190 with a dwell time of 2.5 ms/amu. With computer overhead, approximately one mass scan was performed every 0.9 8. Mass spectra were obtained in a sequential fashion for a period of 2 min, and the reagent ion background spectra were taken for the fmt 0.5 min with pyrolysis commencing at that time. Most of the total ion current was recorded within the subsequent 0.5 min. Samples were flushed with nitrogen for approximately 2 min prior to the pyrolysis event. (Py-EZ-MS). The basic Curie-point Py-MS approach with low-voltage (14 eV) electron ionization and a schematic of the pyrolysis unit have been described elsewhere (14,25).Both APCI and E1 sources were at ambient temperature. Data Analysis. For the APCI data, the 14 background mass spectra from 0.1 to 0.3 min were integrated, averaged, and subtracted from the integrated and averaged 15 mass spectra (0.4-0.6 min) of the total ion pyrogram for each biological component. Mass intensities were recorded manually and entered into a data file for the SIGMA statistical computer package (32). Spectra were ~
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Average pyrolysis mass spectra of DNA: (a) APCI; (b) EI.
normalized to 100% total intensity. For the calculation of the total ion intensity, peaks with a high variance were temporarily excluded (33).All masses were used in the factor and discriminant analyses (34). The EI-MS data were normalized as described above. Because of the negligible background signal contributions in low-voltage E1 pyrolysis mass spectra (14), no hackground subtractions were performed. Standard factor and discriminant analyses were applied, using all the mass variables.
RESULTS AND DISCUSSION The averaged Py-APCI-MS pattern of DNA in Figure 2a exhibits prominent mass peaks at m / z 60,80,81,98,99,116, 149, 161, 163, and 179. This spectrum shows an obvious similarity with the Py-EI-MS pattern in Figure 2b. Previous studies (35,36)have made clear that the Py-EI-MS pattern of DNA is primarily derived from the deoxyribose moiety of DNA, with m / z 116 representing the intact deoxyribose moiety -H,O signal whereas the lower mass peaks appear to correspond to a range of further degraded (e.g. furanoic) deoxyribose fragments, as confirmed by Py-FI-MS/MS studies (37). The absence of peaks representative of the nucleic acid base or phosphate moieties is not surprising in view of the current understanding of DNA pyrolysis mechanisms, which are thought tu involve elimination of deoxyribose moieties with simultaneous formation of base-phosphate conjugates that condense on the walls of the reaction tube. Heating of the reaction tube above 500 "C, however, leads to the dissociation of the basephosphate complexes resulting in the production of large amounts of free bases and nonvolatile (po1y)pbosphates ~ (38). With the exception of guanine, which has a tendency to decompose into relatively nonspecific fragments (39),the bases or their protonated forms are readily detected by various mass spectrometric techniques, including high- (40, 4 0 , and low-voltage (36)EI-MS, FI-MS (36,39),and FD-MS (38). Since the present experiments did not involve the use of heated reaction tubes, no obvious signals are observed in Figure 2 for the bases. Finally, the peaks at m / z 149,161,163, and 179 in Figure 2a, corresponding to the peak series at m / z 148,160,162, and 178 in Figure 2b, merit some attention. From previous studies (35)it is known that relatively large amounts of (po1y)furanoic compounds are formed during DNA pyrolysis, possibly through condensation reactions involving the highly abundant C&,O+ ion at m / z 81. The above mentioned peak series in the m / z 145-180 mass range appears to represent these (po1y)furanoic compounds (35). On comparison of the spectra in Figure 2, the expected protonation effects in APCI are most clearly observed in the mass range above m / z 145. Why protonation effects are not as obvious for some of the peaks in the lower mass range is not clear. Various mechanisms can he postulated to account
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for this observation including charge transfer (e.g. m / z 80, 116), certain compounds may be more amenable to protonation than E1 (e.g. m / z 59, 60, 80) (vide infra) or the APCI signals could represent different fragments from their E1 counterparts (e.g. m / z 81). The formation of the prominent peak a t m / z 116, which showed unusually large intensity fluctuations between duplicate APCI spectra, is incompletely understood at present. The APCI and E1 mass spectra of Curie-point pyrolysis products from glycogen in Figures 3, parts a and b, respectively, also reveal a high degree of overall correspondence as well as some marked differences. The pyrolysis mechanisms of (po1y)hexoses such as glycogen are relatively well understood (42,431 and the pyrolysis mass spectra of glycogen have been studied with several different Py-MS techniques, including high- (28,44), and low-voltage (44) Py-EI-MS, Py-FI-MS (4547),Py-FD-MS (461,flash desorption MS (481,and Py-PI (photoionization)-MS (amylose) (49). Under rapid vacuum pyrolysis conditions, the main pyrolysis mechanism appears to involve depolymerization into levoglucosan subunits followed by more or less severe cracking into smaller pyrolysis products. When soft ionization methods such as FI and FD are used, the molecular ion of levoglucosan (mlz 162) is one of the most prominent mass peaks. Under low- and highvoltage E1 and P I conditions, however, the molecular ion of levoglucosan is completely fragmented and cannot be observed directly. An exception to this “rule” occurs when the pyrolysis products are introduced into the MS system as a rapidly expanding molecular beam and ionized by EI, as described by Milne et al. (50). In this case the extreme adiabatic cooling of the molecules appears to result in the formation of more stable molecular ions of levoglucosan under E1 conditions. The largest hexose moieties preserved under E1 conditions are usually seen at m / z 144 (levoglucosan - HzO) and at m / z 126 (levoglucosan - 2 H20;levoglucosenone) (51). Protonated forms of these signals appear in Figure 3a at m/z 145 and 127, respectively. The low abundance of m/z 163 may be surprising at first since the APCI technique might be expected to preserve the protonated molecular ion of levoglucosan. However, as mentioned before, the reaction tube was not preheated, and thus most of the levoglucosan formed may have been lost by condensation on the walls of the tube. Nevertheless, the APCI mode tends to produce relatively greater amounts of the higher molecular weight features (i.e. potentially more mass spectral information) than the E1 mode. Nearly all signals in Figure 3a represent protonated molecular ions, which correspond to many of the established M + features in the E1 spectrum of Figure 3b. Individual feature differences between the spectra appear to involve the signals at m / z 63,75,77,79,81,91, and 117 (in Figure 3a), which may well represent relatively labile molecular ions, which frag-
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Flgure 4. Average pyrolysls mass spectra of BSA: (a) APCI, (b) EI.
mented in the low-voltage E1 process. Averaged APCI and E1 mass spectra of BSA pyrolyzates are shown in Figure 4, parts a and b, respectively. Pyrolysis mechanisms for complex proteins are much less well understood than those for nucleic acids and (po1y)hexoses. Some aromatic and sulfur-containing amino acids are known to produce rather characteristic pyrolysis products, e.g. by elimination of side chains (52,53). Typical examples can be seen in Figure 4b at m / z 92 (e.g. toluene) and 104 (e.g. styrene), both thought to originate from phenylalanine, as well as m / z 94 (e.g., phenol) and 108 (e.g., cresol) apparently derived from tyrosine, or at m/z 117 (e.g., indole) and 131 (e.g., methylindole), assumed to represent primarily tryptophan. Only a few amino acids appear capable of breaking loose from the protein backbone, e.g., phenylalanine (producing a small phenylacetonitrile signal contributing to m/z 117) and proline (producing pyrroline at m / z 69 and various substituted homologues) (14,54). In view of the absence of obvious monomer signals for most amino acids, the dominance of side-chain, low molecular weight fragments, and the high propensity for char formation, the protein backbone has been generally assumed to sustain few bond scissions during pyrolysis, thus severely restricting the usefulness of pyrolytic techniques for protein characterization purposes (14, 54). Recently, however, diketopiperazines, a class of pyrolytic products long regarded as secondary pyrolysis products formed by condensation of two amino acid moieties, have been shown to represent dimeric chain fragments instead (55-57). The precise isomeric structures of these pyrolysis products, known to provide characteristic clusters of small mass peaks around m / z 124, 138,152,166,etc. in Py-E1 spectra (Figure 4b), are found to be highly characteristic for the specific amino acid pair from which they were derived. Direct comparison of the APCI and E1 patterns in Figure 4, parts a and b, respectively, shows a much lower degree of correspondence than observed for DNA and glycogen. The main cause appears to be due to inherent differences in ionization efficiencies between the techniques. For instance, toluene (mlz 92 in Figure 4b) is known to have a low proton affinity in APCI. Conversely, E1 tends to enhance the relative abundance of aromatic signals (e.g. toluene, phenol, cresol, etc.) vs. aliphatic components (14,58). This may explain the large peaks at m / z 60, 70, and 84 in the APCI spectrum (Figure 4a), which may well represent protonated molecular ions of aliphatic nitrogen-containing compounds such as amides (e.g. at m / z 60 and 74) and nitriles (e.g. at m / z 70 and 84), both known to be prominent pyrolysis products of proteins (14, 21). The qualitative comparisons of the APCI and E1 spectra in Figures 2-4 lead to the following generalized conclusions: (1) for either DNA, GLY, or BSA, the overall pyrolysis products appear to be very similar in spite of the differences
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in instrumental conditions (high-vacuum pyrolysis in E1 vs. atmospheric pressure pyrolysis in nitrogen in APCI); (2) the 770 "C pyrolysis temperature appeared to produce the same general distribution of mass spectral features without loss of information for each biopolymer when compared with their mass spectra a t the 510 and 610 O C Py-EI-MS temperature conditions ( 1 4 , 28, 35, 40, 44, 52,53,58);(3) differences between APCI and E1 spectra seem to be due to protonation effects and reduced fragment ion formation in APCI, as well as characteristic differences in relative ionization efficiencies between the two techniques. Reproducibility. In view of the promising qualitative results obtained with the Py-APCI-MS technique, it is interesting to examine some quantitative aspects, e.g. with regard to short-term reproducibility ("repeatability") of the spectra. As presented in previous Py-EI-MS studies, quantitative measures of reproducibility can be obtained by means of univariate statistical techniques (59,60) as well as by more sophisticated multivariate statistical methods (15,59,61).A relatively simple but informative univariate statistical approach is to calculate the relative standard deviation of each peak for a set of replicate analyses and to plot the results in order of increasing standard deviation values. Figure 5 shows such a plot for the Py-APCI and Py-E1 spectra. Figure 5 reveals a marked discrepancy between the relatively poor reproducibility of the DNA replicate spectra and the more reproducible BSA and glycogen spectra, as obtained by the Py-APCI technique. The poor reproducibility of the peaks in the DNA replicate spectra is not just due to the relatively small number of intense peaks in the Py-APCI spectrum of DNA, since even relatively intense peaks, e.g., a t m / z 116, show marked intensity fluctuations. As shown in Figure 5, the replicate APCI spectra of BSA and glycogen contain approximately 25 and 34 peaks (see Figure 5), respectively, with relative standard deviations below 20%. In contrast, the BSA and glycogen spectra obtained by E1 contain approximately 80 and 47 peaks (see Figure 5), respectively, with relative standard deviations of less than 20%. Moreover, the DNA spectra are well behaved in the E1 case, with some 63 peaks below the 20% relative standard deviation level. To some extent, the greater number of reproducible mass peaks in the Py-E1 spectra may be explained by the higher number of abundant mass peaks produced by the E1 technique, due to the inherently greater ion fragmentation. Perhaps more important, however, is the fact that marked inherent differences exist between the two mass spectrometry techniques as well as between the degree of optimization. Examples of the former include atmospheric
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Figure 6. Plot of the representation space described by F I and F I I obtained by factor analysis of the pyrolysis mass spectra of DNA, bovine serum albumin, and glycogen: (a) APCI; (b) EI.
vs. vacuum conditions of the pyrolysis and ionization events in APCI and EI, respectively,different liquid media for sample application (see Experimental Section), and manual vs. automated sample application on the Curie-point wire. Examples of incompletely optimized APCI parameters include the repeated use of the same wire for the same sample as well as the need for further balancing of the nitrogen gas flows through the pyrolysis probe and plenum. Combinations of the above parameters could explain the relatively poorer short-term reproducibility (mlz relative standard deviation) for a greater number of mass spectral features in APCI than E1 as observed in Figure 5. A more comprehensive analysis of the different sources of variance influencing the qualitative and quantitative properties of pyrolysis mass spectra can be performed by means of multivariate statistical analysis techniques such as factor analysis and discriminant analysis. These methods provide a better overview of the wealth of mass spectral features by reducing the data to a manageable, graphical format. Common, as well as unique aspects of the test biopolymer spectra under different ionization modes and source operating conditions can be observed that would go unnoticed in a visual comparison of mass spectra (Figures 2-4). The first two factors plotted in Figure 6a account for 61.2% of the total variance in the data set. It is interesting to note that if all spectra were perfectly reproducible, the resulting three data points (each representing all superimposed replicate spectra) should lie in a plane. This mlz dimensional space would then of course be fully described by only two factors. Consequently, the variance not explained by the two-dimensional factor space in Figure 6a (100.0 - 61.2 = 38. 8%) must be largely due to "noise" introduced by the irreprodu-
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for both ionization techniques. cibility of the spectra (assuming that the centroids of the three groups lie directly in FI/FII space). Thus, Figure 6a shows the between-group as well as within-group variance after a major amount of within-group variance has been removed. Obviously, all three groups of biopolymer spectra are well separated in FI/FII space, with the DNA and glycogen spectra showing the tightest clustering behavior and the BSA spectra more spread out in FI/FII space. A very similar pattern is seen in the FI/FII score plot of the E1 spectra in Figure 6b. In this case, however, 75.4% of the total variance is contained in the first two factors and thus only 24.6% of the total variance is left unexplained. This is due to the lower average within-group variance (compare Figure 5b with Figure 5a). Figure 6a shows that from a multivariate point of view (i.e. when considering the complete mass spectra as opposed to the individual m / z features), Py-APCI-MS provides a similar degree of compound differentiation as Py-EI-MS (Figure 6b). The fact that the BSA clusters in FI/FII space tend to show a higher within-group variance in both ionization methods can be explained by the relatively large number of low abundant mass peaks in the BSA spectra. This leads to a higher probability that within-group intensity fluctuations will correlate more or less strongly with the between-group intensity differences defining the FI/FII space. Preliminary investigation of the long-term reproducibility of APCI has shown that over a period of 2 months, the same feature distribution and similar m / z intensity ratios have been observed for GLY mass spectra in the APCI mode. The long-term reproducibility for the GLY and BSA biopolymers in the E1 mode has been presented elsewhere (15,281. Uniqueness and Communality. In order to obtain a better measure of the relationship between the Py-APCI and Py-E1 data sets, factor analysis of the combined data set was performed; the results revealed that 62.9% of the total variance was explained by the first three factors. The FI/FII score plot in Figure 7 (50% of total variance) shows that the differences between the biopolymers (largely represented by factor I; 27.3% of the total variance) and the differences between the two Py-MS techniques (largely represented by factor 11; 22.7% of the total variance) provide roughly equal sources of variance in FI/FII space. FI differentiates the three biopolymers regardless of ionization method while FII groups all spectra according to ionization method irrespective of biopolymer type. In other words, in this space the unique parts and the common parts of the two sets of spectra are of similar magnitude. A more detailed examination of Figure 7 reveals that the DNA spectra obtained by the two methods
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$ Figure 8. (a) Score plot of the two-dimensional subspace described by the first two discriminant functions of both ionization methods for the three biopolymers, (b) smoothed variance diagram depicting the mass variables with a correlation coefficient 10.6. show the highest degree of similarity whereas the glycogen spectra appear to be most dissimilar. This finding is in good agreement with the visual impression obtained from Figures 2-4. Inclusion of FIII in this analysis (not shown) did not change these basic conclusions. The “overlap” between the two sets of spectra can be determined more precisely by discriminant analysis when assigning all APCI and E1 spectra of a given biopolymer to a single category, thereby systematically ignoring the differences between the two types of spectra. The resulting three-category discriminant analysis problem yields two discriminant functions, the scores of which are represented in Figure 8. It should be noted that the two-dimensional discriminant analysis solution in Figure 8 reveals the common parts of the between-group variances in both data sets. If canonical correlation techniques were used to examine the overlap between the two data sets, the resulting solution would have revealed the common parts of the between-group as well as the within-group variances. Although the latter information might be of interest for studying the contribution of the pyrolysis procedure to the within-group variance (e.g. by eliminating influence of the ionization methods), the present study focused primarily on the influence of the ionization methods on the spectra. The discriminant score plot in Figure 8 shows convincingly that the spectra obtained by both ionization methods do have a great deal in common. This is especially true for DNA (see also Figure 2), whereas the glycogen spectra show the strongest differences due to the protonation shifts in nearly all the APCI peaks. The DI/DII space in Figure 8 accounts for 36.0% of
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ACKNOWLEDGMENT The authors thank R. A. Yost for helpful discussions and critical analysis of the manuscript. The expert help of Wally Maswadeh in constructing the special Curie-point pyrolysis probe, of Patrick Jones in formatting the data files, and of Melinda Van in preparing the manuscript is gratefully acknowledged. Registry No. Glycogen, 9005-79-2.
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Figure 9, Discriminant spectra corresponding to the three biopolymer axes of the variance diagram (Figure 8b): (a) DNA: (b) glycogen: (c) BSA.
the combined variance in the data sets. In combination with the factor analysis results (Figure 7 ) ,it can be estimated that the two data sets have over half of their respective betweengroup variances in common. The chemical nature of these common differences between the average biopolymer spectra in both data sets can be explored further by means of the VARDIA (variance diagram) technique (62),which enables localization and extraction of oblique component axes from lower dimensional factor spaces even if the total number of components is larger than the dimensionality of the space. The variance diagram of the DI/DII space reveals, as expected, three major component axes (see Figure 8b). These component axes, which point into the general directions of the clusters of biopolymer spectra in Figure 8a, can be extraded numerically in the form of “discriminant spectra” (63). The three discriminant spectra shown in Figure 9 reveal the common characteristics of the APCI and E1 spectra for each biopolymer pyrolyzate. Since each of the three numerically extracted spectra are quite recognizable when compared to the original spectra in Figures 2-4, this type of numerically extracted “common pattern” might be used for weighting selected mass peaks in spectral libraries in order to make the library searchable for spectra obtained by different ionization methods. In principle, the high degree of overlap observed between the Py-APCI and Py-E1 spectra may make it possible to use established Py-MS libraries of complex organic materials obtained by E1 techniques (14) for identifying and interpreting pyrolysis mass spectra obtained by APCI methods or other more or less related ionization techniques. Despite the technique-oriented differences and incompletely optimized APCI conditions, the factor and discriminant analyses in Figures 6-9 afford a comprehensive comparison of the major differences and similarities in the spectral patterns that are not obvious from a visual comparison of the average spectra in Figures 2-4. A more universal solution to the dependence problems of mass spectral libraries on particular sets of experimental conditions might require the development of algorithmic (and/or heuristic) transform functions calculated on the basis of a limited number of experiments performed on selected subsets of standard reference materials. The results reported here may well generate cau-
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RECEIVED for review October 21, 1986. Accepted April 15, 1987. The research reported in this publication was supported in part by ARO Contract DAAG29-81-D-0100.
High-Mass Ion Fragmentation as a Function of Time and Mass Plamen Demirev,*' James K. Olthoff, Catherine Fenselau, and Robert J. Cotter
Department of Pharmacology and Molecular Sciences, The Johns Hopkins University, Baltimore, Maryland 21205
The effects of molecular weight and Ion ltfetlmes on the extent of fragmentation observed in mass spectra of compounds In the mass range 500-5800 have been studled. Fragmentatkn of large Ions over long perlods of tlme can be observed as coherent peaks In tlme-of-flight spectra when delayed Ion extraction technlques are employed. The bnplicatlons for the design of future hlgh-mass Instruments are discussed.
Several recent developments in mass spectrometry have expanded considerably the range of (bio)organic compounds accessible for study. The introduction of new techniques for ionization of involatile and thermally labile compounds, e.g. plasma desorption (PD) (I), fast (keV) atom (ion) bombardment (FAB) (2), and laser desorption (LD) (3),have allowed generation of gas-phase ions with masses as high as 23000 daltons. In turn, this progress in desorption has stimulated development of mass analyzer and detector systems capable of handling ions above 3000 daltons ( 4 , 5 ) . Initial studies by mass spectroscopists in the mass range above 3000 daltons have indicated that some of the concepts evolved historically for the mass range below lo00 have to be reexamined for the higher mass range. It has been shown both theoretically and experimentally (6, 7)that isotopic contributions to the envelopes of molecular ions in the range 3000 to 23 000 significantly reduce the ion Present address: I n s t i t u t e of Organic Chemistry, B u l g a r i a n Academy o f Sciences, 1113 Sofia, Bulgaria. 0003-2700/87/0359-1951$01.50/0
currents carried by the familiar monoisotopic ions, broaden the envelope widths at half height beyond 10 atomic mass units, and encourage exploitation of average mass measurements even on instruments for which unit mass resolution is possible. Secondly, while it has been suggested that the protonated species in desorption spectra are formed by ion/molecule reactions similar to those gas-phase processes of chemical ionization (8),this mechanistic explanation cannot necessarily be extrapolated to very large ions. Bovine insulin samples produce a distribution of singly and multiply charged molecular ions, which reflect their relative abundances in solutions of varying pH (9),suggesting that the desorption of preformed ions may become dominant at high mass and that the pK, and isoelectric point are more important than ionization potential and/or gas-phase basicities for determining ionization efficiencies for large molecules. High-mass spectra also appear to differ from low-mass spectra in the nature and extent of fragmentation. In particular, while small peptides such as leu-enkephalin (MW 556) yield complete N- and C-terminal sequence ions, the spectra of the insulins and proinsulins yield mainly molecular ions, nonspecific losses of small neutral groups contributing to the extended envelopes on the low-mass side of molecular ion envelopes (10, I l ) , and a continuous background of incoherent fragment ions. The intensity of this background, which is observed in both sector magnet and time-of-flight (TOF) analyzers, decreases exponentially with increasing mass and drops abruptly (though not to zero) at the high-mass end of the molecular ion envelope. This incoherent fragmentation, 0 1987 American Chemical Society
