pattern

script. Registry No. (E,E)-CH3CH=CHCH=CH(CH2)gCHO,. 69775-58-2 ... 54-8; (Z,Z)-CH3(CH2)gCH=CHCH=CHCH202CCH3,96348-55-9; ...... polyester carpet. 1/3. ...
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1830

Anal. Chem. 1985, 57, 1630-1636

helpful discussions and for preliminary review of the manu(9) Arlga, T.; Arakl, E.; Murata, T. Chem. Phys. Llplds 1977, 19, 14-19. (IO) Abley. P.; McQulllln, F. J.; Mlnnlkln, D. E.; Kusamran, K.; Maskens, K.; script. Polgar, N. Chem. Common. 1970, 348-349. Registry No. (E,E)-CH3CH=CHCH=CH(CH2)&H0, (11) Tumlinson, J. H.; Heath, R. R.; Doollttle, R. E. Anal. Cbem. 1974, 46, 1309-1312. 69775-58-2; (E,Z)-CH&H=CHCH=CH(CH2)&HO,96362-35-5; (12) Vostrowsky, 0 . ;Mlchaells, K.; Bestmann, H. J. Jusfus Lleblgs Ann. (Z,Z)-CH3CH2CH=CHCH=CH(CH&CHO,71317-73-2;(2,Chem. 1981, 1721-1724.

Z)-CH3(CH2)2CH=CHCH=CH(CH2)&HO,96348-46-8; (2,Z)CHS(CH~)~CH=CHCH=CH(CH~)&HO, 96348-47-9;(2,Z)CH,(CH2)4CH=CHCH=CH(CH2)8CHO, 96348-48-0;(2,Z)CHs(CH2)6CH=CHCH=CHCHO, 96348-49-1; (E,EE)-CH,CH= CHCH=CH(CH~)BCH~OH, 33956-49-9; (E,Z)-CH,CH= CHCH=CH(CH2)&H20H, 80625-59-8;(E,Z)-CH&H2CH= CHCH=CH(CH2)6CH20H, 54364-60-2;(Z,Z)-CH3CH2CH= CHCH=CH(CH2),CH20H, 72553-59-4; (Z,Z)-CH3(CH2)2CH= CHCH=CH(CH2)&H20H, 765-18-4;(Z,Z)-CHS(CH2),CH= CHCH=CH(CH2)9CH20H, 96348-50-4; (Z,Z)-CH3(CH2)4CH= CHCH=CH(CHZ)&H20H, 96348-51-5; (Z,Z)-CH3(CH2)&H= CHCH=CHCHZOH, 96348-52-6;(Z,Z)-CH3(CH2),CH= CHCH=CHCHZCH20H, 79532-13-1;(E,E)-CH,CH=CHCH= CH(CH~)BCH~O~CCH~, 53880-51-6;(E,Z)-CH,CH=CHCH= CH(CH,)SCH202CCH3, 69775-62-8;(E,Z)-CHSCHzCH= C H C H = C H ( CH2)5 C HzOzCCH3, 54364-62-4; (2,Z) CH&H2CH=CHCH=CH(CH2)7CH202CCHs154664-97-0;(2,Z)-CH3(CH2)3CH=CHCHUH(CH~)&H20&CH3,96348-53-7; (Z,Z)-CH3(CH2)4CH=CHCH=CH(CH2)sCHzOzCCH3, 96348-

(13) Wolff, R. E.; Wolff, G.; McCloskey, J. A. Tefrahedron 1988, 22, 3093-3101. (14) Dommes, V.; Wlrtz-Peltz, F.; Kunau, W. H. J. Cbromafogr. Sci. 1978, 14, 360-366. (15) Suzuki, M.; Arlga, T.;Sekine, M.; Arakl, E.; Mlyatake, T. Anal. Chem. 1981, 53,985-988. (16) Kldwell, D. A.; Blemann, K. Anal. Chem. 1982, 54,2462-2465. (17) Buser, H. R.; Arn, H.; Guerln, P.; Rauscher, S. Anal. Chem. 1983, 55, 818-822. (18) Leonhardt. B. A. In "Semlochemlcals: Flavors and Pheromones"; Acree, T. E., Soderlund, D. M., Eds., Walter de Gruyter and Co.: Berlin, Federal Republlc of Germany, In press.

(19) Tomer, K. B.; Crow, F. W.; Gross, M. L. J. Am. Chem. SOC. 1983, 105,5487-5488. (20) Leonhardt, B. A.; DeVllblss, E. D.; Klun, J. A. Org. Mass Specfrom. 1983, 18,9-11. (21) Brauner, A.; Budzlkiewicz, H.; Boland, W. Org. Mass Specfrom. 1982, 17, 161-164. (22) Vostrowskv. 0.: Michaelis. K.: Bestmann. H. J. Jusfus Lleblas Ann. Chem. 1962, 1001-1005. I

54-8; (Z,Z)-CH&CH&CHUHCH=CHCH,02CCH202CCH3,9634&55-9;

(Z,Z)-CH3(CH2)&H=CHCHUHCH2CH202CCH3,60754-63-4; (Z,Z)-CH3CH2CH=CHCH=CH(CH2),CHzOCHO, 82623-59-4; (Z,Z)Z)-CHS(CH~)~CH=CHCH=C"~)~CH~)&H~OCHO, 96348-56-0; (Z,Z)-CH3(CH2)eCH=CHCH=CHCH2OCHO,96348-57-1; (2,Z)-CH3(CH2)7CH=CHCH=CHCH&H2OCHO, 96348-58-2; (Z,Z)-CH3(CH2)4CH=CHCH=CH(CH2)SCHS, 96394-00-2. LITERATURE CITED (1) Budzlklewlcz, H.; Busker, E. Tetrahedron 1980, 36, 255-266. (2) Greathead, R. J.; Jennlngs, K. R. Organic Mass Specfrom. 1980, 15. 431-434. (3) Chal, R.; Harrlson, A. G. Anal. Chem. 1981, 53,34-37. (4) FerrerCorrela, A. J. V. K.; Jennlngs, K. R.; Sen Sharma, D. K. A&. Mass Specfrom. 1978, 7A, 287-294. (5) Hunt, D. F.; Harvey, T. M. Anal. Chem. 1975, 47, 2136-2141. (6) Burnier, R. C.; Byrd, G. 0.; Freiser, B. S. Anal. Chem. 1980, 52, 1641- 1650. (7) Peake, D. A.; Gross, M. L.; Ridge, D. P. J. Am. Cbem. SOC. 1984, 106, 4307-4316. (8) Peake, D. A.; Gross, M. L., unpubllshed results.

Doollttle, R. E.; Roelofs, W. L.; Solomon, J. D.; Card6, R. T.;Beroza, M. J. Chem. fcol. 1978, 2 , 399-410. Henrick, C. A. Tetrahedron 1978, 33, 1-45. Coffelt, J. A.; Vlck, K. W.; Sonnet, P. E.; Doollttle, R. E. J. Chem. fCOl. 1979, 5 ,955-966. Hall, D. R.; Beevor, P. S.; Lester, R.; Nesbitt, B. F. Experienfia 1980, 36, 152-154. Davis, H. G.; McDonough, L. M.; Burdltt, A. K., Jr.; Bierl-Leonhardt, B. A. J. Chem. Ecol. 1984, 10, 53-61. Heath, R. R.; Tumllnson, J. H. I n "Insect Pheromones"; Hummel, H. H., Eds.; Sprlnger-Verlag: New York, 1984. Corey, E. J.; Suggs, J. W. Tefrahedron Left. 1975, 2647-2650. Zwelfel, G.; Backlund, S. J. J. Organomef. Chem. 1978, 156,

159-170. Chodklewlcz, W. Y.; Cadlot, P. C . R . Hebd. Seances Acad. Sci. 1955, 247, 1055-1057. Zweifel, G.; Polston, N. L. J. Am. Chem. SOC. 1970, 92, 4068-4071. Henrlck, C. A.; Wllly, W. E.;Baum, J. W.; Baer, T. A.; Garcia, B. A,; Mastre, T. A.; Chang, G. M. J. Org. Chem. 1975, 40, 1-7. Elnhorn, J.; Vlrellzler, H.; Gemal, A. L.; Tabet, J. C. Tefrahedron Left. 1985, 1445-1448.

RECEIVED for review December 20,1984.Accepted March 19, 1985. Mention of a commerical or proprietary product does not constitute an endorsement by the USDA.

Smoke Aerosol Analysis by Pyrolysis-Mass Spectrometry/Pattern Recognition for Assessment of Fuels Involved in Flaming Combustion Kent J. Voorhees* and Rushung Tsao Department of Chemistry and Geochemistry, Colorado School of Mines, Golden, Colorado 80401

Pyrolysis-mass spectrometry with pattern recognltlon has been used to analyze smoke aerosols to determine contrlbuting fuels involved in fires. Aerosols generated in a flaming mode in a laboratory calorimeter from mixtures formulated from 10 polymeric substances were collected on glass filters. The nonvolatile materials associated with the aerosols were pyrolyzed to provide mass spectral flngerprints of the mixtures. Factor analysis with graphical rotation generated factor spectra whlch were compared to reference pyroiysls spectra of aerosols from the pure polymers. An overall IdentHlcatlon rate of 78% was achleved.

The legal actions associated with catastrophies like the Las

Vegas, NV, MGM Hotel fire have demonstrated the need for an analytical technique for the assessment of the fuels involved in smoke aerosol formation at a particular site. Smoke aerosols are usually composed of a highly condensed carbon material, an amorphous polymeric substance, and various volatile compounds adsorbed on the other materials. The amorphous polymeric substance produced from nonoxidative conditions has been shown to have similar structure to that of the original polymeric source (1,2).Although some structural modification and molecular weight degradation had occurred to this material, enough of the original polymer structure was present to allow for the identification of the original polymer source. Pyrolysis-mass spectrometry has been extensively used for fingerprinting a number of materials (3-5). Recently, we reported on the application of Py-MS with pattern recognition 0 1985 American Chemlcal Society

ANALYTICAL CHEMISTRY, VOL. 57, NO. 8, JULY 1985

Table I. Materials Used in Flaming Combustion Study ignition temp, material

O C

Douglas fir cotton polyurethane GM-24 polyurethane GM-29 poly(viny1chloride) (PVC) nylon carpet polyester carpet ABS polystyrene GM-51 wool Plexiglas black rubber

465 525 405 550 600 555 550 575 490 650 (unknown) (unknown)

for the identification of aerosols from nonflaming combustion (6). In our application, nonflaming conditions were initially chosen to simplify the complexity of the oxidative process by eliminating the high-temperature flame reactions. Overall, by using visual identification, factor analysis, and a regression technique, an identification success rate of 85% was reported. The aerosols from flaming combustion are subjected to the high-temperature flame. Since most fires are fuel rich, it was speculated that a portion of the high molecular weight fragments generated by the pyrolysis step would survive the flame with only minor modification (I, 2). This paper describes the application of Py-MS with multivariate statistics for the differentiation and identification of aerosol materials produced in a flaming combustion mode. EXPERIMENTAL SECTION Sample Collection. A totalof ten polymeric and two unknown materials were selected for this study. The unknowns were only studied in mixtures. Table I summarizes the materials. A cone furnace designed by the National Bureau of Standards Fire Research Laboratory was used to produce the aerosol materials. The details about equipment and apparatus design were reported elsewhere (7). Most samples (50-60 g) were ignited with a propane torch and allowed to burn freely. Some materials like wool, PVC pipe, and the two carpet materials were difficult to keep burning. A cone heater located above the combustion plate was used with these materials. Depending on the material being studied, the total combustion time was 2-5 min. Eleven mixtures containing three to five ground materials (particle size was less than 5 mm) were randomly formulated for the identification study. No information was generated to evaluate particle size and mixing technique on the composition of the mixture aerosols. Smoke particulates generated in the apparatus were removed and collected through two separate pumping systems using Millipore filter holders containing Gelman 47-mm glass fiber filters. The filters were previously extracted with hexane and methanol to remove organic contaminants. One pump with a constant flow rate of 5 L/min was used to collect a sample over the entire combustion period. The other pumping system with a flow rate of 8 L/min was used for the collection of several consecutive filters. These filters were used to show the changes in the chemical constituents during the combustion. After a 24-h vacuum treatment at 50 "C, the nonvolatile materials were prepared for pyrolysis by sonicating the filters with 3 mL of methanol. The volume of the suspensions was reduced by vacuum to 0.5 mL. Samples were stored at refrigerator temperatures prior to analysis. Pyrolysis-Mass Spectrometry. Curie-point wires composed of Fe, CO,and Ni (510 "C)were used throughout the study. Approximately 60 W g of material was coated onto the Curie-point wires (8).This sample size is larger than that normally used for Py-MS. Much of the sample weight was made up of glass filter. The sample on the wire was pyrolyzed with a Fisher Curie-point pyrolyzer (1.5 kW, 1.1 MHz power supply) connected with an Extranuclear SpectrEL computerized quadrupole mass spectrometer system (6). Low-energy electron ionization (15 eV) was used to minimize fragmentation. A total of 35 scans with a scan range of 10-240 amu and a scan rate of 1250 amu/s were used

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for data acquisition. All samples were analyzed in triplicate and data were stored on computer disk. Data Analysis. The normalization procedure discussed in a previous paper (9) was used in this study. Original raw mass spectral data were normalized to the total ion counts, excluding background peaks from water and air (i.e., m/z 18,28, and 32). Then, inner and outer variances were calculated for each peak in the data set. Peaks with either a large inner variance or outer variance were deleted. Normalization was repeated after the deletion step and the ratio of outer to inner variance was recalculated. The deletion process continued until all peaks were below a specified threshold. Each spectrum with n peaks can be represented as a point in a n-dimensional space using a Cartesian coordinate system. The reproducibility of triplicate spectra from the same filter sample was determined by measuring Euclidean distances between each spectrum. Any spectrum outside a preselected threshold was excluded from further data analysis. After the peak and spectrum deletion process, the data were renormalized as a percent of the total ion current and were autoscaled (10) to create a new data set with a mean of zero and a standard deviation of one. Principal factor analysis (PFA) was the primary mathematical method used for data refinement (11). In the data set, many measurements of peaks are interrelated. PFA calculates mutually orthogonal eigenvectors through consecutive steps to extract maximum residual variance in each step. The number of eigenvectors in this analysis equals the total number of spectra in the data set. However, experimental error in the peak measurements inevitably leads to more eigenvectors than are useful; hence, the higher-dimensional factors (the final 10-15% of total variance) which usually contain this error were not utilized. These refined data not only reduced much of dimensionality but also minimized the experimental error contained in the original data set. A point representing each spectrum was calculated in multidimensional factor space. The distance between each point was used as a measure of similarity. The procedure of nonlinear mapping (12,13) was used to display the data in two-dimensional space. This procedure attempts to retain relative position and spacing of data points in the reduction of the displayed space. For quantitative assessment of differentiation between samples, another mathematical parameter, the similarity value, was used. This value, based on Euclidean distance between each pair of data points is expressed as

where D,, represents the distance between two points and D(max) is the maximum distance observed between any two points. In addition to the data reduction by PFA transformation, the factor scores calculated from the corresponding eigenvectors can be used to reduce sample display from multidimensional space to two dimensions (Karhunen-Loeve eigenvector projection) (14). In general, the differentiation between samples is usually shown in the first several factors which also contain the largest amounts of the total variance. In flaming combustion of mixtures, the chemical constituents of the nonvolatile compounds varied as a function of time because of the difference of ignition time and combustion rate for each material. The Py-MS spectra also changed from consecutively collected filter samples. By carefully examining each K-L plot and the associated factor loadings of the mixture data, a graphical rotation procedure (15, 16) was applied to several pairs of factors such that factor spectra were generated. These spectra were subsequently compared to reference spectra for identification of the materials involved in the obtained from combustion. The ARTHUR computer program (In, Infometrix, Inc., Seattle, WA, was used for the multivariate data analysis on a DEC 1091 mainframe computer.

RESULTS AND DISCUSSION The pyrolysis mass spectra of aerosols produced during flaming combustion from the ten known materials are shown in Figure 1. The characteristic peaks for the spectra in Figure 1are listed in Table 11. As was emphasized in the nonflaming combustion study (6),peaks in the lower mass range (