The accuracy of the analysis is approximately &lo% of the amount reported, as determined from eight standards run on eight different sampling tubes’over a 16-hour period. Furthermore three-day storage at ambient temperature had no deleterious effects on the recovery (see Table
I). The signal obtained from the 21-491 spectrometer is illustrated in Figure 3. Since the spectrometer is repeatedly sweeping m l e 79 to 81, the output is a series of mass peaks forming a n envelope as shown in Figure 3a. Closer examination of each sweep shows t h a t the nominal mass peaks a t mle 79 and 81 are composed of several peaks themselves (Figure 3b). These are due to different formula ions having the same nominal mass. For example mle 79 is composed of three peaks. The low mass peak is Br+ (78.9183), the middle is the desired C~H4035C1+(78.9951) and the high mass is C513CH6+ (79.0503) and &H7+ (79.0548). The resolution required to separate these pairs of ions is 1027, 1431, and 17600. With a spectrometer resolution of 1250 (10% valley), three separate peaks are observed for these ions. With this spectrometer resolution, even greater specificity for bisCME is obtained since only ions with the proper formula are observed. The analysis with either spectrometer is quite rapid. Since only mle 79 and 81 are recorded, only those substances which have those ions in their mass spectrum will
yield a signal. Thus a sample may be analyzed even though the column has not eluted all t h e components from previous samples. In actual practice, a sample may be run every 15 minutes allowing around-the-clock monitoring of air with samples taken once an hour. An analytical method for bisCME has recently been reported (6) using a similar concentration method and direct analysis by high resolution mass spectrometry. This depends solely on a n identification based on the mass (or formula) of mle 79. A serious interference may be encountered if other substances are present which yield the ion CzH40Cl+. This is a very likely occurrence since chloromethyl-methyl ether has this ion in its mass spectrum (see Table 11) and would be expected to be present in large excess. A physical separation is required to avoid the interference from this and similar substances. The analytical system consisting of a sampling tube containing a retentive substrate for collection of air samples and storage for subsequent GC-MS analysis for bisCME a t the part-per-billion level has proven to meet all the requirements for a specific, sensitive, reliable analysis with a fast turn-around time. Received for review November 2 , 1972. Accepted July 12, 1973.
Mass Chromatographic Analysis of Volatiles Alan C. Lanser, J. 0 . Ernst, W. F. Kwolek, and H. J. Dutton Northern Regional Research Laboratory, Peoria, ///. 6 1604
A gas chromatographic method for determining molecular weights called mass chromatography has particular interest for the lipid chemist. The equipment differs from the familiar dual compensating gas chromatograph in using different carrier gases in each column and in employing the “forgotten ideal” gas density balances as detectors in independent mode. Molecular weights are calculated from detector responses for the same component eluted from identical columns with different carrier gases. An analysis of errors, precision, and accuracy of the method is given. A typical example of application in lipid chemistry illustrates the complementary roles of mass chromatography and mass spectroscopy in compound identification.
Mass chromatography is old in principle ( I , 2 ) but new in application. Renewed interest in this procedure is due largely to the availability of instrumentation which facilitates its conduct (3, 4 ) . Mass chromatography differs from familiar dual compensating column gas chromatography ( 1 ) A. Liberti, L. Conti, and V. Crescenzi, Atti Accad. Naz. i i n c e i Rend.. 20, 623 (1956). ( 2 ) A. Liberti, L. Conti. and V. Crescenzi, Nature (London). 178, 1067 (1956). (3) D. G . Paul and G. E. Umbreit, Res./Deve/op..21, 18 (1970). ( 4 ) C. E. Bennett, L. W. DiCave, Jr., D. G . Paul, J. A . Wegener, and L. J. Levase, Amer. Lab., May 1971.
2344
in two important aspects: different carrier gases (cgl and C ~ Z )are used in each column; each column effluent is monitored by a gas density balance, the ”forgotten” absolute detector ( 0 , 6 ) , in independent inoncompensating) mode. The response of each detector to an unknown is a function of its concentration and the difference between the molecular weights of the unknown and the carrier gas. Two equations can be written involving the amount and molecular weight of the unknown (MWx) corresponding to the observed detector responses for each column (R,,, and RCg2).These two equations in two unknowns can then be solved algebraically ( 3 , 3 ) :
To determine the instrument constant K , known molecular weights (MW,,d) and measured detector responses (R1 and Rz) are substituted in Equation 1, which has been rearranged to give:
Mass chromatography or molecular weight chromatography is of particular interest to lipid chemists because ( 5 ) A. J. P. Martin and A. T. James, Biochem. J . . 63,138 (1956). (6) C. E. Bennett, L. W. DiCave, J r . , and D. G . Paul, Abstract of paper, “Gas Density-The Forgotten Ideal Detector,” Pittsburgh Conference in Analyticai Chemistry and Applied Spectroscopy, Cleveland, Ohio, March 1972.
ANALYTICAL CHEMISTRY, VOL. 4 5 , NO. 14, DECEMBER 1973
many fatty acids, their derivatives, and split products fall into its favorable (up to 350) range for study. Further, it is not necessary for unknowns to be isolated in high purity before molecular weight analysis by this procedure. Molecular weights of components in mixtures which may be separated by gas chromatography can be determined; conversely, if one cannot resolve an unknown in the mixture by gas chromatography, its molecular weight cannot be determined. Mass chromatography and mass spectrometry are in many aspects complementary. One of the frequent problems encountered by the mass spectroscopist is the absence of' a parent ion which reveals the molecular weight of the unknown, e.g., normal aldehydes larger than 5 carbon atoms lose a mass 28 fragment and alcohols dehydrate. Independent information on the unknown provided by mass chromatography can assist the mass spectroscopist with the interpretation of his incomplete but admittedly more precise mass spectral data. The assessment of the precision and accuracy of the mass chromatograph over a wide range of molecular weights is one objective of the present study; the presentation of' a typical application of this new tool in lipid research is a second.
I
___---
I_-__ I I
I
- -*-
I
I-
I
PROCEDURES The evaluation of the mass chromatograph was conducted under the tollowing experimental design. Mixtures of ( a i normal hydrocarbons i H ) C7.9 and even chain length C10.24. t h ) normal . ( c ) fatty acid methyl esters ( E ) even aldehydes chi C C - ~ Zand chain length C6-22 were successively introduced into the mass chromatograph on 3 successive days according to t h e folloning randomized pattern. Day
Sequence A E H H.4 E
E HAE .4H HEAA H E Mass chromatography was performed with the >IC-2 mass Chromatograph (Chromalytics Corporation) using SFe and Cor, as carrier gase:: and 8-ft X LB-in. columns packed with O\--lOl (10%) on Chromosorh W HP. I'emperature programming was from -40 to +350 "C at a rate of 10" per minute. Injection of the mixtures ( e a . 4 pll was through the molecular weight inlet port and the trapping-valving system. Typical mass chromatograms are recorded in Figure 1 for esters, hydrocarbons. and aldehydes. Detector responses were determined in three different ways. One. measurement of peak heights IP.H.1 a.ith a ruler: two, recording of the mass chromatogram on digital tape and subsequent electronic integration (E.1.J t Inibtronic 43 KAI recorder and a CRS 12 40 pial-hack): and three. direct on-line d a t a acquisition ( 0 . L . )to an IBSl 1800 computer for area determination. The instrument constant K was examined by t\vo estimation methods. Molecular weights calculated with these constants were then compared to the theoretical molecular weights. T h e first method 01' calculating K consisted simply of averaging the K value> lor each component of the mixture and using this average K ( K i to calculate the individual component molecular aeights. The ceconci method for calculating K employs a n iterative computer program to minimize deviations of computed molecular weights froin the theoretical molecular weights ( K O ) .These methods Lvere evaluared by comparing the standard deviations computed from the differences hetween calculated d u e s and theoretical molecular weights.
R E S U L T S AND DISCUSSION
Figure I . Mass chromatograms of (top) methyl esters, (middle) hydrocarbons, and (bottom) aldehydes. Channel A , SFe carrier gas, left; channel 6 ,CO2 carrier gas, right
2 R2 5.b9O 14.62 15.78 lb.28 1bei9
'.
1 R1 -2.253 3.37: 9.470 13.65 16-08 10.78 23.33 20.40
9.
22.32
lb.35
1. 7 . 7.
L.
'.
6,
-.
16.76 15.9*
4 MYCAL
131050 159.08 180.72 217.53 263.29 2'1.15 296.5b 320.72 353.00
5 ERR -1.4 -2.6 -3.2 -a88 -.73 2.2 5.5 . 7 ~
b
7
K
5
a54719 1*b296 .*713b ,27122 e40901 e49001 ,6938h
sb9725 .evi~e
~
i.i.q~S
e7.955 !h.
I-,
In the computer output of Figure 2 for a typical example of P.H. data for methyl esters, it is apparent that the average K is 0.491 (column 6, line 10) and that the standard deviation is 2.5 mass units for this method. This is larger than the deviations for the error minimization technique of 2.3 ( K = 0.492: columns 6 and 7 . line 15). Column 4 gives the calculated molecular weights for the
lb.75
3 'AwSfD
130.19 1¶8.2* 106.31 216.35 2a2.41 270.L5 290.51 32b.57 352.62
1P.
19, ?" . 21.
z. ??. ?L.
-2.250 3.370 9.670 13.65 1 6 -.0 8. . ~ 10.70 20.33 20.48 22.32
6.690 lb.62 15.78 lb.28 16.19 .~ ib.75 16.76 15.96 lb.35 ~
130.19 158.24 186.30 216.35 242.41 .. ~
~
170.46
29a.51 326.57 354.62
131.5s 159.13 188.92 217.95 143.95 272.15 297184 322.35
-1.3 -.a9 -2.h
356.0b
-1.6
-3.6
-1.5 -1.7 e67
4.2
;45;9s e45299
....
2.3083 2.3003
,54719 .46296 .47136 -47022 *20901 sL9OOl .493Uh -49725
.+~ias
Figure 2. Computer output for calculation of average K (f?) and optimized K ( K O ) and resultant molecular weights for methyl esters of Figure 1
ANALYTICAL CHEMISTRY, VOL. 45, NO. 14, DECEMBER 1973
2345
Table I. Summary of K Estimation Number of components
Peak height Electronic integrator On-line Hydrocarbons Aldehydes
Esters
160 160 160 62 47 51
Optimized K KO
0.499 0.517 0.523
0.500 0.537 0.498
Soa 6.26 15.57 26.21 7.31 6.28 4.08
This is in accordance with t h e experience of Swingle who advocates the use of perfluoro-n-hexyl iodide as a desirable internal standard (8). Another method of determining molecular weights and their confidence limits is facilitated by rewriting Equation 2:
(3) or
a Standard deviation
methyl esters, in t h e upper section using K and in the lower section using KO. Column 5 gives the deviation between theoretical (column 3) and calculated (column 4) molecular weights (lines 1-9 and 16-24). The remainder of column 5 shows t h e change in t h e sum-squared error during the minimization process. Column 7 is the standard deviation for molecular weights calculated from response values. A summary of results from optimizing K is given in Table I for all 160 components considered together (upper section) and by compound class (lower section). An obvious conclusion concerns the method of area analysis. Although various authors have shown the near equivalence or even superiority of electronic methods for baseline correction and area integration over P.H. measurements, triangulation, planimeters, etc., the results of Table I indicate that the simple manual drawing of baselines and measurement of P.H. with a ruler shows the lowest standard deviation (SO). A search for t h e assignable causes of the error in the automated methods has been made. Deviations for the O.L. computer procedure appear most explicable. The logic of base-line corrections as taken from the widely used SCAN subroutine ( 7 ) assumes a concave upward or ascending base line. Since t h e base line of the mass chromatograph for Channel A (SFe) as seen in Figure 1 is obviously convex upward, this program includes the area below the base line along with the peaks. A major reprogramming task for this procedure of integration is indicated before reliable results can be obtained. The error for the E.I. system is smaller: however, its source is not as clear. Attended operation of the processor showed that the base-line value was not being reset between peaks as hoped. The base-line display feature of this processor indicated improper corrections were being made for the ascending and descending base lines. Further study of the sources of error on replication and on parameter adjustment is required. The superiority of the P.H. measurement over the automated methods comes from the tendency of the latter to include areas from overlapping peaks, whereas with P.H. measurement the influence of overlapping peaks is a minimum. The conclusion drawn from Figure 2 for the methyl ester run t h a t the minimization technique for determining K is superior to arithmetic averaging is inherent in the nature of Equation 1. The errors of molecular weight determination are lowest a t values closest to the molecular weight of the carrier gases (8); conversely, however, errors in the calculation of K are highest in these parts. By the error minimization process, greater weight has been given to the K's from the higher molecular weight compounds in a region where the K's are determined most accurately. (7) Gas Chromatograph Monitoring Program 1600-23.5.001. Contributed Program Library IBM. ( 8 ) R. S. Swingle, Ind. Res., 1 4 ( 2 ) , 40 (1972).
2346
In this form, the method of least squares can be used to estimate 1/K. Assuming b = 1, the proper estimate will depend on the assumed form of variation in R1/R2 as dependent on U. However, we find it useful to work in t h e logarithmic form log R ] / R 2 = -log K
+ b log U
(5)
Again the method of least squares can be used to estimate the parameters -log K and b. It is necessary to fit the model for two regions of MW-ie., MW,,, < MW, < MW,,, and MW, > MW,,,-when MW,,, > MW,,,. Theoretically b equals 1; however, analysis of these d a t a suggested t h a t often the least squares estimate of b differed significantly from 1. Figures 3, 4, and 5 for P.H. data on methyl esters, hydrocarbons, and aldehydes are examples of the log-log plots with the theoretical line ( b = 1) drawn in. Visual inspection of all plots showed increased dispersion going from methyl esters t o hydrocarbons to aldehydes. Also dispersion about the theoretical line tended to be constant within each of these chemical classes. Estimation of MW, involves two stages. First, the theoretical line is computed and graphed on log-log paper. Then, given RI/R2, the estimate of U, can be obtained with appropriate confidence limits. Next MW, is obtained as
Methods are available for estimating the precision with which U,, and, as a result, the precision for MW,, is determined (9). I t is apparent in Table I1 that the calculated K based on P.H. d a t a varies significantly depending on class of compound. The relative standard deviation in a n estimated K was 9.9% for aldehydes, 2.1% for hydrocarbons, and 0.9% for methyl esters. These values are the same for the ratios R1/R2 and are derived from analyses of logarithms of K or R1/R2. Thus, for example, a plotted point R1/R2 for a hydrocarbon or the estimated K has a relative standard deviation of 2.1?70 and 95% confidence limits of about 4.2%-i.e., 100(1.0212 - 1). This observed variation in K associated with class of compound may arise from several sources, including variability and sensitivity of the gas density balance, differences in thermal stability and reactivity of compounds, and possible compound-substrate interactions. Since only one stationary phase (nonpolar) was used in this study, it is possible t h a t variation in K could be attributed t o a d sorption of t h e sample due to varying polarity. However, column adsorption is usually signaled by skewed or tailing (9) C. A. Bennett and N. L. Franklin, "Statistical Analysis in Chemistry and the Chemical Industry," John Wiley & Sons, lnc., New York, N.Y., 1954, p 228.
ANALYTICAL C H E M I S T R Y , VOL. 45, NO. 14, DECEMBER 1973
O.l+
,*‘ *
aw
+
*/+
0.01
-0.1
*
*/* t
/’
-0.21
I’
E
-~ - 0 . 3 +
/‘
I‘ -0.4. *
/’
-0.5+
+ Methyl Esters
/’
x
/‘
-0.6+
Hydrocarbon
I
+
1’
-0.71 15$’157 -0.8 -1.1
+ Aldehyde
+
M.W.
-0.75 t
1
161 165 171 180 193 214 249 321 5!9 -0.9 -0.7 -0.5 -0.3 -0.1 x = Log u
Figure 3. Log-log plot for methyl esters with theoretical ( b line
-o% =
1)
155’:157
+ M.W. 161 165 171 180 193 214 249 321 5!9 -0.9 -0.7 -0.5 -0.3 -0.1
x
= Log
u
Figure 4. Log-log plot for aldehydes and hydrocarbons with theoretical ( b = 1) line. MW > 144
Table II. K a n d KOfor Compound Classes Based on Peak Height Data K O (optimized K ) K (average K ) Day
A
H
E
A
H
E
1
0.693 0.659 0.667 0.716 0.700 0.739
0.521 0.524 0.528 0.523 0.528 0.518
0.492 0.515
0.525 0.512 0.532 0.528 0.570 0.575
0.497 0.501 0.506 0.494 0.502 0.501
0.492 0.503 0.502 0.492 0.499 0.500
2 3
0.500 0.491 0.499 0.505
peaks. In this study, all peak shapes were uniform and sharp. Because of the deficiencies in both E.I. and O.L. methods of data acquisition pointed out above and the unusually high standard deviation observed for the aldehyde data, the entire series of runs was repeated. In the repeat run$, programming for the O.L. computer method was improved by addition of a “bunching count” which reduced the signal noise and thus improved the derivative calculations. Attended operation of the E.I. processor had shown improper resetting of the base line when the playback unit was operated a t the fast (71/2X) speed as used previously. Reducing this speed by a factor of 4 and optimizing the parameters greatly improved the base-line corrections and the deviation of the results of the E.I. method. This second series of runs gave results similar t o those reported in Tables I and 11. Standard deviations for the O.L. and E.I. measurements were reduced. The relative standard deviation for aldehydes was slightly lower (7.170); but for hydrocarbons and methyl esters, So increased to 3.8% and 2.97’0, respectively. The results of the second series of runs, therefore, substantiate the data of the first series. Analysis of variance of K indicated significant variation associated not only with compound class, but also with day-to-day runs for both series. Variation in K for each class of compound within each day was significant in the second series only. Because of this observation of variability in K , the judicious inclusion of internal standards in each run is recommended. Although day-to-day variability in K within each compound class might be anticipated due to slight differences
-1.01
+ x
1
Aldehyde Hydrocarbon i
-1.2k
+
M.W.
141 139 135 129 122 112 101 89 8 - 1 . 4 ~ : : - J 0.5 -1.5 -1.1 -0.7 -0.3 0.1 ~
~
’
;
~
x
= Log
~
u
Figure 5. Log-log plot for aldehydes and hydrocarbons with theoretical ( b = 1) line. MW < 144
Figure 6. Mass chromatogram for fraction containing unknown with added hydrocarbon standards
ANALYTICAL CHEMISTRY, VOL. 45, NO. 14, DECEMBER 1973
2347
and 75 (10, 11) suggested the following structure with the proposed bond rupture (a):
0
II
CH,- 0-C-(cH,),-cH,+
a
;
,OCH,
7,
A sample of methyl 9,9-dimethoxynonanoate was exam-
20.
1
10. li l
’
i
- h - 0
’
20
40
60
80
100
120 140 160 180 200 220 240 Mass
I
/ l
260
Figure 7. Mass spectrum of unknown from gas chromatographic-mass spectrometric system
in instrument settings, gas flows, experimental technique, etc., the variation in K in the same day within each class was not expected. However, those attempting to ensure reproducibility in temperature programmed gas chromatography have gone to elaborate hardware and uniform procedures to reduce run-to-run variation. While the randomized pattern of sample injection was desired for the error analysis work of this paper, it is believed that better precision may be obtained by successive replication of one sample on the same day. Mass chromatography can play a primary role in the determination of unknown molecular weights in a laboratory not equipped with a mass spectrometer. However, as illustrated in the following example for the identification of an unknown, mass chromatography can also play a complementary role to mass spectrometry. Mass chromatography is most frequently applied to systems in which one or more of the components cannot be identified by gas chromatography. A distillate was submitted which contained an impurity. It was learned that the unknown could be resolved by gas chromatography from the two known components. In the mass chromatogram of Figure 6 for a fraction concentrating the unknown (peak 3), peaks 1 and 2 were known to be methylazelaaldehydate (MAZ) and dimethylazelate (DMA). Because the compound class of the unknown was also unknown, peaks 1 and 2 and added hydrocarbons were used as internal standards to calculate K. The molecular weight was calculated to be 231.2 f 4.9. Mass spectra of two of the three peaks, as determined by our gas chromatographymass spectrometer system, gave fragmentation patterns readily identifiable as the known DMA and MAZ. The mass spectrum for the unknown given in Figure 7 was not present in our library of private and published mass spectra. The mass spectroscopist is unable to say from examining the mass spectrum above whether the highest observed fragment, mass 201, was the parent peak, P - 1, or the highest molecular weight fragment from a spectrum lacking a parent peak: However, consideration of the mass chromatographically determined molecular weight of about 232 and the highest mass spectral fragment of 201 suggested to our mass spectroscopist the possible loss of mass 31 corresponding to a methoxyl fragment. The lack of any other fragment caused by the loss of a larger alkoxyl group, the intensity of the 201 ion peak, and the presence of characteristic acetal fragment peaks of mass 47
2348
ined by the organic chemist originating the sample with his gas chromatograph, by the mass spectroscopist, and by the mass chromatographer. Confirmation of identity was obtained by all three. The complementary role of mass chromatography to mass spectrometry in the identification of an unknown as illustrated above would appear to be a general relationship of the two techniques. Whereas mass chromatography lacks the precision, the accuracy, and the informational multipeaks, it can frequently suggest t o the mass spectroscopist where to look for the molecular ion should one occur or which member of a homologous series one is dealing with. In their mass spectrometric analysis of aliphatic aldehydes, Gilpin and McLafferty (12) state “For the normal aldehydes with four or more carbon atoms, the ion resulting from the loss of 28 mass units is larger than the molecular ion . . . this loss presents a difficulty in unknown mixtures for the loss of 28 falls upon the same mass as the molecular ion of the aldehyde with two less carbon atoms.” Mass chromatographic data of relatively low precision would be helpful therefore in distinguishing normal aliphatic aldehydes. Alcohols constitute another class of volatiles which are important to the lipid chemist and present the difficulty to the mass spectroscopist of dehydrating (13) so as not to give the molecular ion P but rather P - 18. The recently studied field ion source does provide molecular ion information with alcohols ( 1 4 , however; when generally available, the field ion source or the recently studied chemical ionization (15) may eliminate the complementary role of mass chromatography. It would appear, however, that the absolute detector characteristics of the gas density balance (which eliminates the need for calibration factors) and the amenability of its output for computer acquisition of data and return of analyses will continue to recommend the mass chromatograph to the lipid chemist.
ACKNOWLEDGMENT Gas chromatographic-mass spectrometric analyses were performed by E. Selke and statistical calculations were made by Miss I. Stein. Received for review January 15, 1973. Accepted June 21, 1973. Presented a t the AOCS Meeting, Los Angeles, Calif., April 23-26, 1972. The Northern Regional Research Laboratory, Agricultural Research Service, U.S.Department of Agriculture. The mention of firm names or trade products does not imply t h a t they are endorsed or recommended by the Department of Agriculture over other firms or similar products not mentioned. (10) W. H. McFadden, J. Wasserman, J. Gorse. R. E. Lundin, and R. Teranishi, Anal. Chem., 36, 1031 (1964). ( 1 1) R. A . Freidel and A. G. Sharkey, Jr., Ana/. Chem., 28, 940 (1956). (12) J. A . Gilpin and F. W. McLafferty, Anal. Chem., 29,990 (1957). (13) R. A . Friedel, J. L. Shultz, and A . G. Sharkey, Anal. Chem., 28, 926 (1956). (14) W. K. Rohwedder, Lipjds, 6 , 906 (1971). (15) M. S. 6. Munson and F. H. Field, J. Amer. Chem. SOC., 88, 2621 (1966).
ANALYTICAL CHEMISTRY, VOL. 45, NO. 14, DECEMBER 1973
