Evaluation of laser desorbed transition-metal ions as analytical

Anal. Chem. 1987,59, 1937-1944 (14) Katakuse, I.; DesMerlo, D. M. Int. J . Mass Spectrom. Ion Processes 1983, 54, 1-15. (15) Wong, S. S.; Rollgen, F. W. Nucl. Insfrum. Methods Phys. Res. 1988, 814, 436-447. (16) BrOfSkY, D. F.; Giessman, u.;Barofsky, E. h i . J . Mass SPeCtfOm. Ion Phys. 1983, 53, 319-322. (17) Wlttmaack. K. Surf. Sci. 1979, 89, 668-700. (18) SifiO, s.J.; @iffis, D. P.; Murray, R. W.; Linton, R. W. Anal. Chem. 1985, 57, 137-142. (19) Busch, K. L.; HSU, 8.H.; Xie, Y. x.; cooks, R. G. Anal, Chem, 1983, 55, 1157-1160.

1937

(20) Day, R. J.: Unger, S. E.; Cooks, R. G. Anal. Chem. 1980, 5 2 , 557A572A.

RECEIVED for review April 28, 1986. Resubmitted December 29, 1986. Accepted April 13, 987. Support of this project was granted by the National Institute of Environmental Health Sciences, Research Triangle Park, NC, which provided funding for both instrumentation and stipends.

Evaluation of Laser Desorbed Transition-Metal Ions as Analytical Chemical Ionization Reagents by Pattern Recognition R. A. Forbes, E. C. Tews, Y. Huang, and B. S. Freiser* Department of Chemistry, Purdue University, West Lafayette, Indiana 47907

S. P. Perone* Lawrence Livermore National Laboratory, Livermore, California 94550

Ten transttlownetal ions are evaluated as chemical ionization reagents by use of pattern recognition. The data for the reactions of Sc', Y', La', V', Nb', Ta', Cr', Fe', Co', and Cu' with 11 ketones, 8 aldehydes, and 8 ethers were collected wHh a Fourier transform mass spectrometer equlpped with a laser desorption lonlratlon source. Seiectivitles of the metal Ions for the three organlc classes are quantitated and compared to 70-eV electron Impact (EI) data. WMle a 100% recognttlon accuracy for the three classes is obtained for EI, Cu', and Sc' data, the fewest features are requlred for, and the best separatlon is produced with, the Sc' data. The selected features are also examined for their chemical Infonnatkn, providing clues to the characterktk reactlvttles and reaction mechanisms for the metal Ions with the three functional groaps.

Recently, the reactions of metal ions with organics in the gas phase have gained considerable interest (1-18). Transition-metal ions, generated by laser desorption, have been studied in our laboratory for a number of years (12-18). A major goal of this work has been to understand the fundamental processes involved in metal ion/molecule reactions by obtaining kinetic, thermodynamic, and mechanistic information. In addition, from initial studies performed in our laboratory on, for example, Cu+ with esters and ketones (12) and Fe+ with ethers (13), ketones (13),and hydrocarbons (14), as well as studies performed in other laboratories ( 5 , 9 , 1 1 ) , it is apparent that metal ions also hold promise as selective chemical ionization (CI) reagents. Pattern recognition has been found to be an extremely useful technique for solving a wide variety of chemical problems (19,ZO). In mass spectrometry, in particular, pattern recognition has been applied to the prediction of biological activity of antibiotics (21), localization of conjugated double bonds in aliphatics (22),evaluation of field-desorption and fast-atom-bombardment profiles (23), and the characterization of bacteria cultures by mass spectral analysis of growth media (24). Pattern recognition is ideal for analysis of GC/MS data and has been applied, for example, in the identification of dipeptides in amino acid sequencing (25) and the analysis of 0003-2700/87/0359-1937$01.50/0

complex mixtures of essential oils in fragrances (26). In our laboratory, pattern recognition has been found to be a simple and effective means for evaluating our emerging metal ion CI database. The work presented in this paper demonstrates the use of pattern recognition as an organized, systematic approach to evaluate and compare the analytical utility of several different laser desorbed metal ions as CI reagents. In addition, feature selection provides a convenient method for extracting the mass peaks characteristic of the reactivities of the metal ions with different organic functionalities. Chemical ionization, introduced in 1966 by Munson and Field (27),allows the exploitation of ion-molecule chemistry to enhance the selectivity of mass spectrometric measurements in addition to what may be possible instrumentally. In contrast to conventional electron impact (EI) ionization, chemical ionization offers a wide variety of reagents of differing reactivities, through which, ionization is accomplished in the chemical reaction of the ion reagent with the neutral sample (28). By far the most common chemical ionization analyses involve proton or charge transfer reagent ions (28). Extending the list of useful reagent ions, however, is currently a matter of intense investigation. In particular, laser desorption of metal ions with Fourier transform mass spectrometry (FTMS) provides a selection of reagents which can be readily generated. From a periodic table of reactivities, metals can, in theory, be selected to provide as much universality or selectivity as desired. In addition metal ions should offer a choice of soft ionization, providing molecular weight information, and hard ionization, yielding fragment ions characteristic of structural features of the sample. Thus, the chemistry of the reaction may be exploited to obtain the desired information from the analysis. As an example of soft ionization, Figure 1 shows the 70-eV E1 and Cr+ CI spectra for two mixtures of three oxygencontaining components. While the E1 spectra are complex and contain relatively low abundances of the molecular ions, Cr+ produces three pseudomolecular ion peaks (parent plus chromium), which are the dominant product peaks for both mixtures. Since Cr+ condenses with many oxygenated compounds, it may prove useful for the selective detection of trace concentrations of oxygenated organics. Depending on the 0 1987 American Chemical Society

ANALYTICAL CHEMISTRY, VOL. 59, NO. 15, AUGUST 1, 1987

1938

Table I. 27 Compounds of Three Organic Classes for with 10 Metal Ions

b) c f

il,,

+

M/z 52

,

50

Cr -B

+

M/z 154

L I Cr-C I

,,,

,

./,, ,

I

I

;;yj;;6

, I,(

M/Z 138

150

130

110

90

70

170

Mass (AMU)

C)

1I

I

,

20

,

. 40

.

E+

,

.

.

.

.

ao loo Mass ( AMU)

.

60

d)

.

.

140

120

C rf D M / Z 152

Cr+-F M / Z 182

I

I

1

,

, Ib.

I I

1 1

Study

ketone

aldehyde

ether

propanone butanone 2-pentanone 3-pentanone 2-hexanone 3-hexanone 3-heptanone 3-methyl-2-butanone 3,3-dimethyl-2-butanone 2,4-dimethyl-3-pentanone 4,4-dimethyl-3-pentanone

propanal butanal pentanal hexanal methylpropanal dimethylpropanal 2-ethylbutanal 2-methylpentanal

ethyl ether ethyl propyl ether propyl ether butyl ether methyl tert-butyl ether isopropyl ether ethyl tert-butyl ether sec-butyl ether

visual displays of the data, by the number of features required, and by examination of the individual features chosen. EXPERIMENTAL SECTION Instrumental Methods. A Nicolet prototype Fourier transform mass spectrometer (FTMS 1OOO) was used to generate the CI mass spectral data. Details of the ICR and FTMS experiment have been described elsewhere (30-3). Metal ions were generated by focusing the frequency doubled beam (532 nm) of a Quanta-Ray Nd:YAG pulsed, infrared laser onto targets of the pure metal. The different laser targets are attached to the front screen of the FTMS cell and may easily be selected by positioning the laser beam. The prototype instrument is equipped with a 15-in. Varian electromagnet which was maintained at 0.9 T for these experiments. Sample pressures were approximately 2 X Torr, as measured by a Bayard-Alpert ionization gauge, while the trapping times ranged from 100 to 500 ms. The chemicals used for the collection of the data in these experiments were obtained commercially in high purity and were used as supplied except for multiple freeze-pump-thaw cycles to remove noncondensable gases. Data. The CI spectra used in the analysis consist of the primary product ions generated by the initial reaction of the metal ion reactant with the organic neutral sample. Spectra were taken at several reaction times, and the shortest time yielding an acceptable signal-to-noise ratio was considered optimal for the primary product determination. Isotope contributions and peaks corresponding to further reactions of the primary products were eliminated. Primary product ioqs can, in most cases, be easily distinguished from secondary product ions in several ways. First, they can be distinguished temporally. As the reaction time is increased, primary ions appear first and then secondary ions begin to appear a t the expense of primary ion abundance. Second, FTMS ejection methods (35) permit the isolation of a particular ion and the subsequent monitoring of its reaction chemistry (17). Thus, a direct link between a primary ion and the secondary ions it produces can be readily established. Each spectrum was normalized to the percentage of the base product peak. The spectra were then reduced to sets of nominal mass and intensity and treated as follows in order to distinguish the product ions of different metals: first, the mass of the metal reactant ion was subtracted from the nominal product ion masses (since the charge typically stays with the metal-ligand fragment); second, a multiple of 1000 was added to the result, depending on the entry number of the metal ion into a database management file. For example, the product ion corresponding to ScOH+ is identified as feature 6017, as 6017 = 62 - 45 + (6 X lOOO), since Sc', nominal mass 45, is the sixth metal ion in the file. Along with the primary product data, 500-ms spectra were used without modification (except for addition of a multiple of 1000) as a second set of data to test the viability of using fixed reaction time and pressure as a standard set of conditions. Fe+ and Y+ data were taken from an earlier study (29), and unfortunately, no standard 500-ms spectra were available. Low-resolution,70-eV, E1 data were also collected along with the CI data for comparison. Under the conditions used for EI, some self-chemical ionization was also observed as eviaenced by the presence of [M + I]+ peaks


1030

Table 11. Best Recognition Accuracies Obtained for 10 Metal Ions and E1 (a) Primary Product Data SEIa

fsc+*

fY+

fv+

SLa+

fNbt

fTat

fc,+

fFe*

scut

fco+

ketone aldehyde ether

100 100 100

100

100

100

100

91

100

13

88

100

75

88

82 88

100

63 88

91 88

91

100

100

100

100

100

100

100

100

100

total features P/F

100

100

100

10 2.1

85 5 5.4

8 3.4

14

I 3.9

96 6 4.5

93 5 5.4

93 6 4.5

89 5 5.4

96 8 3.4

100 100

89 6 4.5

12 2.3

(b) 500-ms Data fsc+

ketone

aldehyde ether

Os,

SLa+

100 100 100

total 100 features 4 6.8 P/F features selected by SSTRA.

82 63

100 100

100

88

81

96

12

5

2.3 *f,

fv+

5.4

fNbt

fTa+

fcr+

fco+

SCU+

91

100

91

88

100 100

82 88 88

100

100 100

96

85

96

I

4

11

3.9

6.8

96 5 5.4

100 96 8 3.4

88

100

2.5

features selected by FANNDE.

more intense than those predicted from the 13C natural abundances. Pattern Recognition Software. All of the pattern recognition programs have been written in our laboratory for the IBM 9000 lab computer using IBM version CS 9002 FORTRAN 77 and employ the it-nearest neighbor (KNN) classifier. The first nearest neighbor (i.e., k = 1)was used exclusively,due to the relatively small size of the data sets, and Euclidean distance measure was employed. Recognition accuracies were generated by using the leave-one-outalgorithm (LOO) and used as performance feedback in the feature selection algorithms (36). Two different heuristic feature selection routines were employed to reduce the number of features required for recognition of the organic classes: successive subtraction of features using total recognition accuracy as the selection criteria (SSTRA)and forward addition of features using the nearest neighbor distance.error as the selection criteria (FANNDE) (37). A weighting of the features, relative to one another, has been used to improve the performance of the KNN classifier. A complete description of this weighting scheme and the feature selection algorithms has been given (37). Since the results obtained with these algorithms depend upon the order in which features are examined and upon other control parameters, several different trials were performed with each routine, varying the control parameters, and the maximum recognition accuracy which was obtained with either routine is reported as a measure of the separability of the classes using a particular metal ion. As a visual display of this best result, a nonlinear mapping routine (NLM) provides a two-dimensional plot of the data set which reflects the N-dimensional (Nbest features) intercompound distances (38). In any supervised learning approach, such as used here, the danger exists of finding an artificial separation of the classes rather than a separation based upon true chemical differences. In consideringthe significance of the separation, probably the most important factor to consider is the number of features required (39). As a general rule of thumb, for any supervised learning scheme, the smaller the ratio of patterns to selected features (P/F), the more possibility of a happenstance class separation. Another important factor to be considered in selecting best features based upon classifier performance is the size of the initial feature pool. The larger the number of initial features (relative to the number of patterns), the more possibility exists of finding features which may produce circumstantial class separations. In this work, the significance of the recognition accuracies are checked in several ways. First, the P / F ratio is used as a relative measure of the validity of the separation. The larger this value, the more likely a chemically meaningful class separation has been found. Second, visual displays of the data are made, using the selected features, to graphically examine the separations in the data. Finally, the features are examined for chemical information in an attempt to find the reasons why the features were chosen.

RESULTS AND DISCUSSION Best results of feature selection searches with the primary product data of the ten metal ions and E1 with the compounds listed in Table I are summarized in Table IIa. The number of features selected in each search and the ratio of the number of patterns to this number of features (P/F) are also given. Table IIb shows the best results for eight of the metal ions (data were unavailable for Y+ and Fe+) using the standard, 500-ms data. As is evident from Table 11, the three classes can be distinguished with high accuracy using most of the metal ions. The poorest recognition accuracy is noted for the La+ data (74%), where the aldehydes are indistinguishable from the ketones (only 13% accuracy), since nearly the only product observed for La+ with the two classes is Lao+. Y+ reacts similarly to La’, forming only the oxide with the smaller ketones and aldehydes. However, other products are observed for some of the larger compounds of the two classes and can, thus, be distinguished. A 100% recognition of the data set is obtained by using E1 data, the primary product Sc+ and Cu+ data, and the 500-ms Sc+ data. The chemical basis for these separations is discussed below. E1 Data. Ten E1 features are selected out of 78 observed, resulting in a P/F of 2.7, which can be considered relatively low. Figure 2 shows the nonlinear mapping (NLM) for the E1 data set using the corresponding best features. In the NLMs, the relative two-dimensional separations of the compounds reflect their relative N-dimensional separations. Thus, while a great deal of information is lost in mapping the data set from, in this case, 10 to 2 dimensions, the figure qualitatively reflects the distribution of the compounds in 10-dimensional space (38). From the NLM, it is appears that multicentered clustering occurs. Ketones (“1”) form two distinct clusters, and aldehydes (“2”) seem to overlap the ketones and the ethers (“3”). While it might appear from the mapping that several compounds would be misclassified, the NLM does not take the feature weightings into account, and there is always a finite amount of error involved in mapping from higher dimensions (38). A listing of the selected E1 features and their intensities is given in Table 111. Although the P/F ratio is low, it appears that a chemically valid separation of the three classes does indeed exist. For example, mlz 43 appears important to ketones and is the base peak for 7 of the 11ketones. m / z 43 is the base peak for two aldehydes also (which cluster near

1040


Table 111. Intensities of the Best E1 Features compound

29

39

43

55

57

70

71

73

87

88

0 10 0

0 0 0 0 0 0 0 0 0

0 0 6 0 2

0 2 0 0 0 0 0 0 0 0 0

0 0

0 0 0 0 0 0 0 0 0 0 0

Class 1

propanone butanone 2-pentanone 3-pentanone 2-hexanone 3-hexanone 3-heptanone 3-methyl-2-butanone 3,3-dimethyl-Z-butanone

0

3

8

0

0

5 3

39 2 2 27 2 8

2,4-dimethyl-3-pentanone 4,4-dimethyl-2-pentanone

0 0

8 12 11

9 2

1

2

100 100 100 100 6 15 100 38 100

0 0 0 0 0 0 0 0 4 1

100

2

0

100 9 65 100 0 100

2

4

41

0

100 0 0 0

42 2

3" 2" 0 0 0 0 0 0 0

Class 2 9

propanal butanal pentanal hexanal 2-methylpropanal 2,2-dimethylpropanal 2-ethylbutanal 2-methylpentanal

0 0

0 11

0 0

48

0

0

0

26

25

12

15 3 63

3 12

17 38

0 0

0

2

2

18

11

4 57 16

51 15 23

0

2 0

23 2

19 15

0

31 27 13 27 7

27 19 19

11

23

14

100 100

4

Class 3 100 33 19 100

ethyl ether ethyl propyl ether propyl ether butyl ether isopropyl ether methyl tert-butyl ether ethyl tert-butyl ether sec-butyl ether

0

6 3 100

0 0

0 14

6 10 1

86 29 19 19 3 9

4

2 11

0

0

3

0 0

10

7 6

0

6

100

1 0

14

0 0

5

16 19 52

0 0 0 0 0

2

20

0

0 0 0 1

0

0

38 0 0 0 0 1 0

7

100

0

0

42

11

0

"The intensity observed at m / z 87 for 2- and 3-pentanone probably arises both from the I3C parent ion and a small amount of protonated parent neutral. The latter results from proton transfer to the parent neutral by selected fragment ions.

2

3

1

2

2 2 3

3

3

3

3

'

2

2

3

,'I I

C.3CL.3AT

X Y D CU3CL.UNT

'L9PPI4G E R R O R = 0 . 1 0 1 6 2R::::lhL 3I:dENSION = 1 4 STAX1 FEATCRES i 4 0 7 1 , 4 0 7 3

I

3 3

3

3

I I

3

the ketones), but the two can be distinguished from the ketones by features 39 and 55. Cu" Data. Twelve features are selected from 46 observed to provide 100% recognition of the data set yielding a P/F of 2.3, which again can be considered low. From the NLM for Cu+, Figure 3, no clear separation of the three classes is evident, and it appears that extensive subclustering and heavy overlap of the classes occur. From the low P/F and the lack of any clear class separations in the NLM, it might be suspected that the 100% recognition accuracy is based upon circumstantial, rather than chemical, information. From an examination of the feature and intensity listing in Tahle IV, however, true distinctions in reactivity become apparent. Ketones. A condensation peak is observed for all of the ketones with Cu' and is the h w e peak for 8 of the 11 compounds. Features 4086.4100. and 41 14 were wletterl, corre-

2

I

22 2 2

2

I

3

3

Figure 2. Nonlinear mapping of E1 data set using the best features. Class 1 is ketone, class 2 is aldehyde, and class 3 is ether.

I

1

Figure 3. Nonlinear mapping of Cu" data set using best primary product features. Class 1 is ketone, class 2 is aldehyde, and class 3 is ether.

sponding to condensation peaks. These condensations are also observed with aldehydes (except for 4114,since no aldehydes in the data set are large enough), but a t lower intensity. Feature 4028 (CuC2H4"/CuCO+) is selected and appears to be importan' to ketones. Other reaction products are observed, but none is unique to the ketones. Aldehydes. For seven of the eight aldehydes, the base peak observed corresponds to a hydride abstraction: Cu+ + RCHO

-

RCO+ + CuH

(1) This reaction is not observed for the ketones suggesting that the hydride abstracted may be from the aldehyde terminus. Thus, the features 4057, 4071, 4085, and 4099 are all selected and are important in rlistingriiqhing the aldehvdes from the other rlasses. Onlv f(,p 2.2-dimethvlpropanal is the loss of


1941

Table IV. Intensities of the Best Cu+ Primary Product Features compound

4028

4054

4057

4071

4085

4087

4086

4099

4100

4101

4114

4130

Class 1 propanone butanone 2-pentanone 3-pentanone 2-hexanone 3-hexanone 3-heptanone 3-methyl-2-butanone 3,3-dimethyl-2-butanone 2,4-dimethyl-3-pentanone 4,4-dimethyl-2-pentanone

33 0 100 0 6

31 0 10 0 0 0

0 51 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0 0 0

35 0 0 0

0 100 0 0 0

0 0

80 100 0 0

38 79

83

0

0 0

8 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0

71 0 0 0

0 0 0

0 0 0

0 0 0

0 100

0 0 0

0 0 100 4 6

0 0 0 0 0 41 0 0

0 0 0 48 0 0 6 11

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0

0 0 0 0 0

0 0

0 0 0 0 0 0 0 0

0 0 0

100

10

100

0 0 100 99

0 0 0 0 0 0 0 0 0 0 0

Class 2 propanal butanal pentanal hexanal 2-methylpropanal 2,2-dimethylpropanal 2-ethylbutanal 2-methylpentanal

0

0

0 0 0 3 19

0 0 0 7 0 0 0

2 0

96 0 0 0

0 100 0 0

0 0 0 0

100 98

5 7

0 0 100

0 0

11

0 0 51 0 0

0 0 58 0 0

0

0 0 0 0

0 0 100 0 0

0 0 0 0

100 100

0 0

0 0 0 0 0

Class 3 ethyl ether ethyl propyl ether propyl ether butyl ether isopropyl ether methyl tert-butyl ether ethyl tert-butyl ether sec-butyl ether

0 0 0 2 0 0 0 0

0 0 0

0 0 0 0 0

0 0 0 0 0 0 0 0

0 0

0 4 0 0 0 0

hydride not the base peak (but 51% ). The base peak is instead CuC4&O+, arising from the loss of CHI, and is approximately equal in intensity to m / z 71 (methyl abstraction). Condensation of aldehydes with Cu+ is also observed, but usually having below 50% of the base peak intensity. Ethers. Some condensation is observed for six of the eight ethers. Hydride abstraction peaks are the base peaks for five ethers and observed for two more (seven out of eight). Selected features 4087 and 4101 correspond to hydride abstractions, and differ from the hydride abstraction peaks for ketones by two mass units. Only for sec-butyl ether is no hydride abstraction observed. The base peak instead corresponds to CuC4H90+,which was not selected. Sc+ Data. A 100% recognition accuracy of the data set is possible by using eight selected primary products out of 48 observed for scandium ion. The P/F ratio is an acceptable 3.4 and the NLM, Figure 4, shows three very distinct clusters. Comparing these results to those obtained for EI, one can conclude that Sc+ is better than E1 in separating the three classes. The intensities of these chosen features for the data set are given in Table V. Although the major product for Sc+ with the organics is the oxide ( S O , feature 6016), this feature was not chosen because it is observed for all three classes and, thus, does not help to separate them. Ketones. The smaller ketones produce only the oxide peak with Sc+. The larger ketones, however, form product ions resulting from C-C and C-H bond cleavages. Except for feature 6096 (ScC6H80+),the product ions are not unique to ketones. This product results from either multiple dehydrogenation of the mass 100 ketones (and is observed with less intensity for mass 100 aldehydes) or from loss of either H 2 0 or CH, + H2 from the mass 114 isomers. Aldehydes. A unique ion, 6018 (ScOH2+),is observed for the branched aldehydes. Present at essentially 0 reaction time, this ion is apparently formed at the surface and desorbed upon

0 0 0 0

0 0 0 0 1

0 100 0

0 0 0

0 2 0

11 100 0

0

0 0 0 0

3 28

0 0 0

100

4 100

0 0 0

0

0 4 0

20 0 0 0 0

SC?CL.CAT AND SC3CL.UXT \‘A.P”:NC ERROR = 0.0:74 DIXENSION = e START FEATURZS = 6 0 1 1 , 6 0 3 3

O?ICIIAL

I

3 22

‘I

‘

3

Figure 4. Nonlinear mapping of Sc’ data set using best primary product features. Class 1 is ketone, class 2 is aldehyde, and class 3 is ether.

Figure 5. Reaction mechanism for Sc+ with aldehydes. laser impact. Double resonance ejection confirms that it does not come from a gas-phase ion/molecule reaction. A possible explanation for formation of this ion, as no water is observed in the E1 spectra for these compounds, is a catalysis process occurring at the metal surface. The bare metal may catalyze the dehydration of the branched aldehydes, leaving behind trimethylenemethane type species. Another ion, observed for every aldehyde, is ScCH20+ (feature 6030). A reaction

1942


A

6016

A

H$q

H

H

Figure 6. Reaction mechanisms for Sc+ with ethers. ORIGINAL DIMENSiOI = 4 ST.9RT FEUTLIRES = 4 1 1 4 . 6 0 1 8

2

3 3

z2 2

21 3 3

I

3

39,

3 3

3 2

Figure 7. Nonlinear mapping for Sc+ data set using best 500-ms features. Class 1 Is ketone, class 2 is aldehyde, and class 3 Is ether.

mechanism for its formation is proposed in Figure 5. After initial oygen interaction, a subsequent hydride shift with the loss of an alkene would account for formation of this ion. Ethers. Feature 6017 (ScOH') appears to be unique to ethers. A mechanism for the formation of this ion is given in Figure 6. The Sc+ initially interacts with the oxygen atom with a subsequent transfer of a hydride. The resulting intermediate decomposes to produce the hydroxide and a hydrocarbon radical. The formation of ScCH20+is also observed for ethers and a possible mechanism for this reaction is also shown in Figure 6. This peak is not observed for the secondary ethers and ethyl tert-butyl ether, for which other C-0 bond cleavages are dominant. 500-ms Data. A 100% recognition of the 500-ms data set is possible by using just 4 of 107 observed products, with a respectable P/F of 6.8. Table VI lists the intensities for these chosen features. Only feature 6075 ( m / z 45 30, ScCH,O+) was selected in common with the corresponding feature, 6030, from the primary product data set. The other three features correspond to primary product ions which, interestingly, were not selected as features in the best trial with the primary product data set. This is not unreasonable, however, since the relative intensities of the primary products a t 500 ms have changed from those observed under the conditions optimal for the primary product spectra. From the NLM for this data set in Figure 7, an excellent separation of the ketones and aldehydes from the ethers is obvious. The ketones and aldehydes, however, cluster together and are only very subtly distinct. All aldehydes except butanal produce a small amount

+

I

F w e 8. Nonlinear mapping for best features from data set combining primary product data for all 10 metal ions. Class 1 is ketone, class 2 is aldehyde, and class 3 is ether. 6018

A

2

I

Figure 9. Three-dlmenshl representationof data set using three Scf best features. Circles are ketones, triangles are aldehydes, and squares are ethers.

of 6075, while no significant amount is observed for the ketones. Combination of Data for All 10 Metal Ions. In another experiment, the best primary product features for all ten of the metal ions were combined and feature selection was performed. Only four features were required to produce 100% recognition accuracy of the data set, giving a P/F of 6.8. Table VI1 shows the intensities of the features which include three Sc+ features, 6017, 6018, and 6030 (&OH+, ScOH2+,and ScCH20+, respectively), and one Cu+ feature, 4114 (CuC7HI40+).The nonlinear mapping for this result is shown in Figure 8. Eight of the 11ketones are clustered at the origin (middle left side of plot) and the others a t the far right. The aldehydes and ketones form two distinct clusters. Figure 9


1943

Table V. Intensities of the Best Sc+ Primary Product Features

6017

compound

6018

6025

6030

6045

6066

6094

6096

0

0 0 0 0 0

0 0 0 0

Class 1 propanone butanone 2-pentanone 3-pentanone 2-hexanone 3-hexanone 3-heptanone 3-methyl-2-butanone 3,3-dimethyl-2-butanone

0 0 0 0 0

0

2,4-dimethyl-3-pentanone 4,4-dimethyl-2-pentanone

0 0

0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0

0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0

0 0 0

4

0 0 0 0 0 0 0 0 0 0 0

0

5 0 2 0

20 17 16

3

3 6 0 0

0 0 0 0

5

3

0 0

0 0 0

0

4 61 31


0 0

3

0 0 0 0 0 0 0 0

0 0 11

23 53 25

4

4 3

9

14

31 0 1

11

68

9

2

12

0 0 0 3 2


14 24 12 8

0 0 0 0

12

0 0 0

3 7 13

0

Table VI. Intensities of the Best Sc+ 500-ms Features

compound

6075

6077

6089

6121

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0

0

4

0

0

0

3 0 0

3 3

2 0

0 0

4

0 0 0

0 0 0

0

3 0 0

0 0 0 0


3 0

3 3 9 11

5 7

0 0 0 0 0 0 0 0

2

0

8 0

0 0

15 2

3

10

0 0 0

0

3

0


7 4 4

11 0

39 0 0

100 34

3

68 53

0

12

10 0

4

68

0

50

76

61 0

0 0

0 0

100 53

0

47

3 10

11

0

12

0 0 0 0

0 0 12

shows a three-dimensional plot of the data set, using the three selected Sc+ features. From the three-dimensional plot, the clustering of the patterns can be seen quite well. The ketones

0 0

0

0 0 2

8 0

24 13

Table VII. Intensities of the Best Primary Product Features for the Combination of Data for all 10 Metal Ions

4114

6017

6018

6030

0

0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0

0

4

0 0

0 0

3

0

0 0 0 0

0 0 0 0 11

Class 1 propanone 0 butanone 0 2-pentanone 0 3-pentanone 0 2-hexanone 0 3-hexanone 0 3-heptanone 100 3-methyl-2-butanone 0 3,3-dimethyl-2-butanone 0 2,4-dimethyl-3-pentanone 100 4,4-dimethyl-2-pentanone 99

0 0 0 0

0

0


0 0 0 0 0

3 0

23 53 25

4 4

3 12 14 11 9

Class 3

0

61 40 3 48

0 0 0

0 0 0 6 3 0 17

compound

Class 1 propanone butanone 2-pentanone 3-pentanone 2-hexanone 3-hexanone 3-heptanone 3-methyl-2-butanone 3,3-dimethyl-2-butanone 2,4-dimethyl-3-pentanone 4,4-dimethyl-2-pentanone

7 5 5

ethyl ether ethyl propyl ether propyl ether butyl ether isopropyl ether methyl tert-butyl ether ethyl tert-butyl ether sec-butyl ether

0

14

0 0 0 0 0 0

24

0

12

8 12

3 7 13

0 0 0 0 0 0 0 0

7 5 5 11 0

39 0 0

cluster a t the origin (except for 4,4-dimethyl-2-pentanone), having no intensity a t the chosen mass peaks. The ethers are

1944


distinct from the aldehydes and ketones by formation ,of ScOH+ (6017) and the aldehydes distinct from the ketones by ScCHzOf (6030). Feature 4114 ( m / z 63 114) was chosen for Cu+ in order to correctly classify 4,4-dimethyl-2-pentone, which produces a small amount of ScCH20+. Feature 4114 corresponds to the condensation of the mass 114 ketones with copper. Inclusion of this feature might be considered circumstantial, however, since Cu' also condenses with aldehydes and no aldehydes of mass larger than 100 were included in the data set. I t is interesting that three of the four chosen features were Sc+ products. This supports the conclusion that Sc+ is the best discriminator of the classes.

+

CONCLUSION From the high recognition accuracies obtained for most of the metal ions in the discrimination of the three classes, it is evident that laser desorbed metal ion CI can be useful in identification of analytical unknowns. The nonlinear mappings and the P/F ratios suggest that the data obtained for Sc' is more useful than E1 data for discriminating subtle differences between similar classes of compounds, such as ketones and aldehydes. The selectivities of the metal ions have also become apparent. Aldehydes can easily be distinguished from ketones by using Cu+ on the basis of a (P - H)' peak. Cr+ can be used in complex mixture analysis for which little fragmentation is desired or if molecular weight information is needed. Finally, La+ proves to be extremely sensitive to oxygen-containing compounds. Registry No. Sc', 14336-93-7;Y', 14782-34-4;La', 14175-57-6; V', 14782-33-3;Nb', 18587-63-8;Ta', 20561-66-4; Cr', 14067-03-9; Fe+, 14067-02-8;Co', 16610-75-6; Cu', 17493-86-6;propanone, 67-64-1; butanone, 78-93-3; 2-pentanone, 107-87-9;3-pentanone, 96-22-0; 2-hexanone, 591-78-6; 3-hexanone, 589-38-8;3-heptanone, 106-35-4;3-methyl-2-butanone,563-80-4;3,3-dimethyl-2-butanone, 75-97-8; 2,4-dimethyl-3-pentanone, 565-80-0; 4,4-dimethyl-2pentanone, 590-50-1;propanal, 123-38-6;butanal, 123-72-8;pentanal, 110-62-3;hexanal, 66-25-1; 2-methylpropanal, 78-84-2; 2,2-dimethylpropanal, 630-19-3; 2-ethylbutanal, 97-96-1; 2methylpentanal, 123-15-9;ethyl ether, 60-29-7; ethyl propyl ether, 628-32-0; propyl ether, 111-43-3;butyl ether, 142-96-1;isopropyl ether, 108-20-3;methyl tert-butyl ether, 1634-04-4;ethyl tert-butyl ether, 637-92-3; see-butyl ether, 6863-58-7.

LITERATURE CITED (1) Allison, J. I n Progress in Inorganic Chemistry; Lippard, S.J . , Ed.: Wiiey-Interscience: New York, 1986; Vol. 34, 628. (2) Freas, R. D.; Ridge, D. P. J. Am. Chem. Soc. 1980, 102, 7129. (3) Armentrout, P. 6.; Halle, L. F.; Beauchamp, J. L. J. Am. Chem. Soc. 1981, 103, 6501. (4) Halle, L. F.; Houriet, R.; Kappes. M.; Staley, R. H.; Beauchamp. J. L. J . Am. Chem. Soc. 1982, 104, 6293. (5) Lombarski. M.; Allison, J. I n t . J. Mass Spectrom. Ion Processes 1983, 4 9 , 281.

(6) Mandich, M. L.; Halle, L. F.; Beauchamp, J. L. J. Am. Chem. Soc. 1984, 106, 4403. (7) Tolbert, M.; Beauchamp, J. L. J. Am. Chem. Soc. 1984, 106, 8117. (8) Aristov, N.; Armentrout, P. B. J. Am. Chem. Soc. 1984, 106, 4065. (9) Peake, D. A.; Gross, M. L. Anal. Chem. 1985, 5 7 , 115. (IO) Well, D. A.; Wilkins, C. L. J. Am. Chem. SOC. 1985, 107, 7316. (11) Lombarski, M.; Allison, J. Int. J . Mass Spectrom . Ion Phys . 1985, 65, 31. (12) Burnier, R . C.; Byrd, G. D.;Freiser, B. S. Anal. Chem. 1980, 52, 1641. (13) Burnier, R. C.; Byrd, G. D.; Freiser, B. S. J. Am. Chem. Soc. 1981, 103, 4360. (14) Byrd, G. D.; Burnier, R . C.; Freiser, B. S. J. Am. Chem. Soc. 1982, 104, 3565. (15) Byrd, G. D.; Freiser, B. S. J. Am. Chem. SOC. 1982, 104, 5944. (16) Jacobson, D. B.;Freiser, B. S.J. Am. Chem. SOC. 1983, 105, 5197. (17) Freiser, B. S. Talanta 1985, 3 2 , 697. (18) Jacobson, D. 6.; Frelser, B. S.J. Am. Cbem. SOC. 1985, 107, 72. (19) Jurs, P. C.; Eisenhour, T. L. Chemical Applications of Pattern Recognlfion ; Wiley: New York/London/Sydney/Toronto, 1975. (20) Varmuza, K. Pattern Recognltlon in Chemistry; Springer-Verlag: New York, 1975. (21) Brent, D. A.; Roth, 6.; Johnson, R. L.; Brunner, T. R. Biomed. Mass Spectrom. 1981, 8 , 440. (22) Brauner, A.; Budzikiewicz, H.; Boiand, W. Org. Mass Spectrom. 1982, 17, 161. (23) Van Der Greef, J.; Tas, A. C.; Bouwman, J.; Ten Noever De Brauw, M. C.; Shreurs, W. H. P. Anal. Chim. Acta 1983, 150, 45. (24) Boon. J. J.; Tom, A.; Brandt, 6.; Eijkel, G. 6.; Klstemaker, P. G.; Notten, F. J. W.; Mlkx, F. H. M. Anal. Chlm. Acta 1984, 163, 193. (25) Ziemer, J. N.; Perone, S. P.; Caprioli, R. M.; Seifert, W. E. Anal. Chem. 1979, 5 1 , 1732. (26) Chien, M. Anal. Chem. 1985, 5 7 , 348. (27) Munson, M. S. 6.; Field, F. H. J. Am. Chem. Soc. 1968, 8 8 , 2621. (28) Harrison, A. Chemical Ionization; Wiley-Interscience: New York, 1984. (29) Forbes, R. A.; Tews, E. C.; Wise, M. 6.; Freiser, B. S.; Perone, S. P. Anal. Chem. 1988, 5 8 , 684. (30) Lehman, T. A.; Bursey, M. M. Ion Cyclotron Resonance Spectrometry; Wiley: New York, 1976. (31) Beauchamp, J. L. Annu. Rev. Phys. Chem. 1971, 2 2 , 527. (32) Comisarow, M. 8. Lecture Notes in Chemistry, 31, 484, (1982). (33) Wanczek, K. P. Int. J. Mass Spectrom. Ion Phys. 1984, 6 0 , 11. (34) Wilkins, C. L. Mass Spectrom. Rev. 1986, 5 , 107. (35) Comisarow, M. 6.: Parisod, G.; Grassi, V. Chem. Phys. Lett. 1978, 5 7 , 413. (36) Thomas, 0.V.; DePaima, R. D.; Perone, S.P. Anal. Chem. 1977, 4 9 , 1376. (37) Forbes, R. A.; Tews, E. C.; Wise, M. 8.; Freiser, B. S.; Perone, S. P. J. Chem. I n f . Comput. Sci. 1986, 2 6 , 93. (38) Fukunaga, K. Introduction to Statistical Pattern Recognition ; Academic: New York, 1972. (39) Gray, N. A. 8. Anal. Chem. 1976, 4 8 , 2265.

RECEIVED for review August 11, 1986. Resubmitted April 3, 1987. Accepted April 15,1987. Acknowledgement is made to the Division of Chemical Sciences in the Office of Basic Energy Sciences in the United States Department of Energy (DE-AC02-80ER10689) for supporting this research and to the National Science Foundation (CHE-8310039) for providing funds for the advancement of FTMS methodology. R. A. Forbes would like to acknowledge fellowship support from Lawrence Livermore National Laboratory and the Office of Naval Research.

Evaluation of laser desorbed transition-metal ions as analytical

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