automated identification of mass spectra by the reverse search

plished in a shorter period. Because of this, radiation programming should be beneficial t o those systems which use peak height measurements of the AA or A F signals rather than integration techniques. In those cases t h a t t h e atomization process goes through a n oxide formation s tep and volatilization of t h e oxide occurs a t a temperature much lower than the atomization temperature ( 1 6 ) , it is essential, to prevent sample loss due to oxide formation, that t h e atomizer achieve f he desired final temperature instanta- neously. I t is in these situations t h a t t h e radiation method of' programmed heating can provide better powers of detec- tion for certain elements compared t o other programming techniques. Also, recent work by Lundgren et a1 ( 4 ), who used radiation controlled heating with a graphite furnace, has demonstrated tha t radiation programming can facili- t a te analyses which are impossible with conventional heating methods. These authors demonstrated that cadmium can be determined in a NaCl matrix with no nonspecific absorption interference from the salt, when the furnace heating is radiation controlled.

LITERATURE CITED (1) G. F. Kirkbright, Analyst (London), 96, 609 (1971). (2) J. D. Winefordner and T. J. Vickers. Anal. Chern., 44 (5), 150R (1972). (3) J. D. Winefordner and T. J. Vickers, 46 (5), 192R (1974). (4) G. Lundgren, L. Lundrnark, and G. Johansson, Anal. Chern., 46, 1028

(5) M. P. Bratzel. R. M. Dagnall, and J. D. Winefordner, Anal. Chim. Acta.,

(6) M. P. Bratzel, R. M. Dagnall, and J. D. Winefordner, Aoof. Soectrosc.,

(1 974).

48, 197 (1969). . . .

24, 518 (1970).

355 (19731. (7) S. R. Goode, Akbar Montaser, and S. R. Crouch, Appl. Spectrosc., 27,

~ ~ ~I

(8) Akbar Montaser. S. R. Goode. and S. R. Crouch, Anal. Chern., 46, 599

(9) Akbar Montaser and S. R. Crouch, Anal. Chern., 46, 1817 (1974). (10) A. D. Wilson, Appl. Opt., 2, 1055 (1963). (1 1) S. H. Praul and L. V. Hrnureik, Rev. Sci. lnstrurn.. 44, 1363 (1973). (12) I. Langrnuir, Phys. Rev., 2, 329 (1913). (13) G. R. Fonda, Phys. Rev., 21, 343 (1923). (14) G. R. Fonda, Phys. Res., 31, 260(1928). (15) R. S. Asarnoto and P. E. Novak, Rev. Sci. lnstrurn., 38, 1047 (1967). (16) D. J. Johnson, T. S. West, and R. M. Dagnall. Anal. Chim. Acta.. 67, 79

(1974).

(1973).

RECEIVED for review April 22, 1974. Accepted September 24,1974.

Automated Identification of Mass Spectra by the Reverse Search

Fred P. Abramson

Division of Laboratory Medicine, Department of Pathology and Department of Pharmacology, George Washington University School of Medicine, Washington. D.C. 2003;

A new method for the automatic identification of mass spectra which used the library spectrum as the basis of the comparison is described. This process, called reverse search, is contrasted with other methods for mass spectral library searches where the unknown spectrum itself be- comes the basis. The reverse search is shown to be fully automated, requiring no operator judgment to output qualitative and quantitative data. The other significant feature of a reverse search is its inherent rejection of interference. A specific compound obscured by other compounds may still be identified by this method. A number of areas of routine analysis are suggested where this system could have significant application.

This paper presents a situation of da ta interpretation where the order of comparison between known and unknown da ta is of great significance. An arbitrary distinction can be made between the two possible mechanisms for searching a library: forward and reverse. A forward search method compares a n unknown to a library entry, while a reverse search compares a library entry to a n unknown. Al- though these two cases seem similar, the significant advantages o f a reverse search will be described.

Automated identification processes are especially valu- ahle when operating a gas chromatograph/mass spectrometer system. owing to the large number of' unknown peaks frequently encountered. My experience with library searches of mass spectral da ta has been unsatisfactory. T h e prin- cipal difficulty is t h a t conventional searches provide equiv- ocal answers regarding the composition of the spectrum in

question. This is especially bothersome when analyzing materials of biological interest, because few such compounds are included in commercial lihraries. Furthermore, biological samples are often complex and, even following gas chromatography, multiple compounds may be present in a n unknown mass spectrum causing inaccuracies.

F e a t u r e s of Forward Search Techniques. T h e numerous methods for computerized searches of mass spectral da ta u p to 1970 have been reviewed ( 1 ) . Several additional papers have appeared subsequently (2-5 1. All of these search methods are in the forward sense; t h a t is, they process a n unknown spectrum for comparison to their library. As a consequence, they suffer from interferences, d of automation, and an inflexibility of their compound identification algorithms as will be described.

T h e presence of significant levels of interference may artificially suppress the relative intensity of relevant masses and produce a bad fit. Even more import,antly. when data are compressed (e .g . , saving only the two largest peaks in a 14-amu region), interferences of any nature may cause relevant masses to be excluded. To eliminate interferences, the operator must first detect such admixed spectra, then identify some other spectrum t o subtract from the first to remove this interference, and, finally, determine how much of this second spectrum t o subtract from the first. In addition, the operator must decide which, if any, of the multiple suggestions reported by most forward search methods is the correct answer. These human interventions make the automation of the identification process difficult.

T h e algorithm generating the similarity index in a forward search is fixed. Whatever the method (if any) for re- ducing spectra, whatever the method for increasing the im-

ANALYTICAL CHEMISTRY, VOL. 47. N O . 1, JANUARY 1975 4 5

portance of large peaks over small peaks, and whatever the mathematical equations for generating similarity indices, a n individual forward search method will use a single set of rules t o compare each compound in the library because the processirig is based on the unknown data. This differs from methods for manually interpreting mass spectra which ac- knowledge that certain compounds are interpreted using one set of rules, other classes of compounds by other sets of rules, and many compounds by unique schemes altogether. These judgments cannot be made on the basis of the unknown spectrum.

Obviously, a n unknown peak may be either larger or smaller than the reference library peak a t the same mass. T h e forward search method must treat both of these cases similarly. T h a t program cannot determine whether the unknown peak is: too large due t o additive interference a t that mass; too small due t o a large intensity a t some other mass artificially supressing the relative intensity of this mass; absent because i t has been eliminated in the da ta compression step; or absent because it had zero intensity. Were this differentiation possible, specific actions based on the type of deviation observed would result.

T o make an identification, enough intensity information is present in the computer to generate a compound specific integral. Except for one at tempt using forward search techniques ( 5 ) , such information is not outputted because a complete chromatogram is seldom searched. Intensity information from a single point in a chromatogram is incom- plete quantitative data and is without value unless isotope ratio data are obtained.

T h e goals of other search methods are to rank the five to ten compounds in the library which most resemble the unknown. The correct answer may appear on the list, often a t the top, when it is contained in the library. Incorrect compounds will sometimes head the list when the correct compound is in the library and always when the correct compound is not in the library. Screening procedures, which pre-select library compounds for further consideration, do not frequently eliminate all output when the correct compound is not in the library so tha t answers are generally provided. T h e operator must then determine which, if any, of the compounds listed agree with his unknown spectra. This may be a time consuming process requiring skill in mass spectral interpretation.

E X P E R I M E N T A L Mass spectra were obtained on a Dupont Model 21-491 mass

spectrometer equipped with a Varian 2700 gas chromatograph and a metal jet separator interface. A Dupont Model 21-094 data system was expanded by an additional 4K of core to the Hewlett- Packard 2100A computer and a second Hewlett-Packard 797OB digital magnetic tape unit. The 6-ft X 2-mm Pyrex column was packed with 3% OV-17 on 100/120 mesh Gas Chrom-Q (Applied Science) and was programmed from 150 to 250 "C at So/minute. The mass spectrometer was scanned at 2 seddecade, giving a repetitive cycle time of approximately 7 seconds.

R E S U L T S A N D DISCUSSION F e a t u r e s of the Reverse Search Method. T h e two ini-

tial goals of the reverse search method were: generation of answers which require no interpretation; and identification of compounds from admixed spectra without manual intervention. Along with these goals, flexible algorithms, quanti- tation, and increased automation were developed.

When interpreting spectra containing large levels of interference oneself, i t is often more successful t o tentatively assume the presence of a compound and to compare those peaks in the spectrum appropriate t o the assumed compound with the library spectrum. Most notably, this reverse strategy of selecting a library spectrum and extract-

ing from the unknown spectrum the intensities fitting the reference is the foundation of this new search pr0gra.m. Re- verse search has two main advantages. First, by selecting only the intensities of the unknown corresponding to a reference spectrum, most interference is ignored. This enables the identification of a particular unknown among merged spectra even if the unknown produces few significant peaks in the total mass spectrum. This parallels the "manual" method previously described which allows identification of a compound up to a point where the interference totally obscures it.

A general statement of the problem is that the set of data comprising the unknown is too large. Sterling and Pollack (6) note that, based on the predictive strength of each vari- able, the group with the highest predictability should be selected. T h e reverse search uses a highly predictable sub-set of data by selecting only those data from the unknown which are relevant to the comparison being made. In this way, the quality of the analytical results limits identification. If the mass spectrum of a compound exists with relatively little isobaric interference, the reverse search should identify it, no matter how much extraneous information is contained in the complete unknown spectrlim. This process is not limited to the identification of a single compound from a n admixed spectrum but will match any number of recognized compounds by the sequential comparison of each library spectrum t o tha t unknown.

Second, selecting masses from the unknown based on each library spectrum permits much of the flexibility in identifying compounds as would be used manually. T h e computer based reverse search mimics human interpretation by selecting masses for each comparison, reflecting how masses were selected in each library spectrum. If the masses included in the reference spectrum are optimal for identifying each compound, a flexible, compound-specific algorithm will result.

Using the reference spectrum as the basis of the comparison also permits differentiating those cases where the unknown spectrum has a greater intensity a t some mass compared to the reference spectrum from those cases where the unknown is smaller. I t is assumed that the reference spec- t rum includes no impurities. Deviations from the expected intensity may be rationalized by remembering tha t mass spectra are additive, not subtractive. There is no way that one or two intensities from a selected library compound can be below the allowed tolerance in the unknown spectrum and still have tha t compound present. A large number of negative deviations may occur when the intensity selected to normalize the data was too large because of interference. Automatic renormalization is then attempted. Positive deviations arise because of interferences and a certain number of these are allowed. From this reasoning. several cate- gories of known/unknown comparisons exist; those with few negative deviations which are discarded, those with few positive deviations which are saved, those with many negative deviations which are renormalized, those with many positive deviations which are discarded, and certain combi- nations of these which are processed further.

When a satisfactory fit t o a library spectrum has been found, not only are the identity, similarity index, and scan number stored but also the intensities of all masses from the unknown which were used to identify it. This sum of ions accumulates when another spectrum yields a fit to the same compound, as will occur in the sequential scans from a chromatographic peak. Because the reverse search always examines a n entire analysis, rather than pre-selected individual spectra, these compound-specific intensities are relevant to the goal of quantitative analysis of the sample.

46 ANALYTICAL CHEMISTRY, VOL. 47, NO. 1, JANUARY 1975

Table I . S u m m a r y of the Differences between F o r w a r d a n d Reverse Search

Fonvard search

Data bas i s of search is unknown spectrum

Arbi t rary intensities a r e selected from unknown

Positive or negative deviations a r e approximately equal in weight

Spectra a r e not adjusted for fit

Relatively sensitive to interference o r mix - t u r e s

Qualitative data only

Identifies complete un- knowns from a large l ibrary

Ranked l ibrary compound suggestions as output

Substantial operator inter- action and judgment r e - qui r e d

Library s ize limited by per ipheral s torage

Search algorithms fixed

R a e r s e search

Data bas i s of search i s l ibrary spectrum

Only intensities c o r r e - sponding to l ibrary compound a r e selected

Sign and number of the deviations a r e diagnostic

Auto in at i c r e norm aliz a- tion

Relatively insensitive to interference o r mix- t u r e s

Qualitative and quantitative data simultaneously

compounds from a limited l ibrary

each l ibrary compound

Identifies pre- selected

Yes 'No responses for

Automatic operation

Library s ize limited by

Search algorithms flexi- core s torage

ble

T h e reverse search also greatly improves the automation capabilities over the conventional spectrum-oriented search procedures. Among previously described systems, only one ( 5 ) will process a n entire chromatogram automatically. Other searches require a separate input for each spec- t rum to be searched. T h e reverse search programs have been written to always search a continuous block of data. T h e only input required is the identification number of the C X run and the number of the library being searched against. T h e implicit ability to remove most interferences, t h e automatic subtraction of background, and the generation of yes/no answers for each compound included in the library remove all other responsibilities from the operator.

It appears tha t many analyses for which a computerized library search might be useful, will be concerned with certain classes of compounds which could be contained in a limited library. T h e 8K core size of the computer system being used restricts each library, a t present, to 100 compounds with 10 peaks per spectrum.

T h e members of such classes of compounds as abused drugs (7, 81, endogenous steroids (except for cy and 8 iso- mers) (9-12), amino acids (131, prostaglandins (14-161, and chlorinated pesticides ( 1 7, 18) have readily differen- tiated spectra. In each example, a 100-compound library seems sufficient. It is also t rue that , in general, well-differ- entiated spectra allowed identification from a limited number of masses. This is a desirable result statistically. Not only should the set of data be reduced t o a relevant sub-set, but this sub-set should be as small as possible consistent with accurate prediction and efficient computation (6). As a result, a library search using a limited number of compounds which are readily interpreted from one another may produce a definitive answer rather than the several best suggestions of the identity of a compound as provided by forward search methods. Where the spectra of two com-

pounds differ only slightly, common ions may be included in the library to be reported under a common name. If a category needs more than 100 compounds, sequential searches may be called u p on two or more libraries containing the compounds in question and the separate reports combined. Each library is t o be tailored to the problem a t hand and therefore specific. This will remove the inaccuracies present when commercial libraries containing spectra from numerous laboratories are searched. Table I summa- rizes the concepts and capabilities of forward and reverse searches.

There appears t o be no previous discussion of the relative merits of forward and reverse library search methods with regards t o interference and noise rejection, automation capabilities, and accuracy. There are, however, several examples, and not only in mass spectral interpretation, where this principle has been used. Bonnichsen et n l . (19) developed a n off-line computer method for the determina- tion of specific barbiturates from mass spectra with each drug having its own pre-programmed identification algorithm. They found good accuracy and a n important improvement in accessability to non-technical personnel. A simple reverse algorithm which pre-screens mass spectra t o see if a n acceptable number of masses are present, has been described recently by Isenhour (20). T h e important feature of this program was tha t it allowed mixtures t o be analyzed without operator intervention. T h e reverse search described here is more general and powerful than those described previously.

An interesting parallel to the interpretation of mass spectra is computerized medical diagnosis. Here, again, one has a library and a n unknown but now the library is of the diagnostic criteria for a variety of disease entities. The un- knowns are observations (history, physical, and laboratory findings) which frequently are more numerous than neces- sary to define any single disease entity. A forward search in the clinical situation compares the observations from a patient with the typical observations for each disease entity (21 ). When some of the abnormalities in a patient's pattern are due to other diseases or unrelated conditions a n error may result because those specific unrelated abnormalities have also been included in the comparison. A reverse search would subsequentially select only the values defin- ing each disease from a patient's profile. Such a n approach has been implemented (22, 23) and appears capable of more accurate diagnoses than a forward method. I t also allows multiple diseases t o be found where a forward search would fail.

F e a t u r e s of the R e v e r s e Search P r o g r a m s . This sec- tion mentions only certain essential features. A more complete description is available from the author.

Library. Each library record contains space for ten masses and ten intensities for up to 100 compounds followed by a second record containing the corresponding 20 character names for each compound. A library resides in core for a n entire analysis. Spectra are put into the library using whatever criteria are deemed optimal, although normally the largest ten intensities are chosen. T h e number of libraries is limited only by tape storage.

Search T h e search process selects peaks from the unknown based on each library compound. T h e tolerance for any single peak is h1l?. T h e direction of any intolerable deviation is also noted for differentiation of the positive/negative possibilities described earlier. Those acceptable peaks must have an average error of to allow a match to be declared. T h e intensities for these peaks are accumulated so tha t the total intensity for a compound in a continuous set of scans, such as a gas chromatogram, is determined. If

ANALYTICAL CHEMISTRY, VOL. 47. N O . 1. JANUARY 1975 47

Table 11. Computer Input Dialog= SCAN1 GCRUN GW COMMEN GASTRIC DRUG SCREEN

-~

AQRATE 10 1 READY

MECALC -~ GCRUN GW 5 : -

READY

SEARCH GCRUN LIBNO

READY

GO -

3 : 1 - GW

a Input dialog is underlined. The sample is an extract of stomach contents of a drug overdose patient. The semicolon instructs the computer to take the last GCRUN number printed out by the software as input to the present program (in this case GW 6). The slash indicates that no more parameters are to be changed in that program. SCAK 1 is the data acquisition phase and MECALC converts ion emergence t ime into mass/charge. The other parameters printed out by the computer are the residual values from the last time each particular program was run.

Table 111. Search Program Outputa SPECIAL LIBRARY SEARCH GC ID GW 6 DATE 5’12 ‘73 AQRATE 10 SCTTME 2 RESPWR 1100 HIMASS 600 THRESH 8

GASTRIC DRUG SCREEK

LIBNO. 1 LIB NAME TEST SEQLEN 77

TDE KTITY HIT SUM 10x3 SCAN QirALlTY *2** 0

PHESOBAKBITAL 55 10-160 43971 D I A Z E PAM 104 10-084 44861

READY 0 A limited library including drug spectra named T E S T has been

searched against chromatogram GW 6. Two drugs, phenobarbital and diazepam. have been identified. SCAS is the scan number where the compound first appeared (see Figure 1). The first number in HIT QUALITY indicates the number of library compound masses which were within acceptable limits (10 is maximum). The second number is the hit quality (000 is a perfect fit) in parts per 1024.

no compound fits have been found, the report reads “NO COMPOUNDS IDENTIFIED”.

Performance of the Reverse Search Program. Table I1 exemplifies the limited dialogue required for an entire analysis, including data acquisition, data processing, and compound identification. T h e software automatically upgrades the GC ID number and the entry “;” refers to the most recent GC ID number used. Note tha t if this were a repetitive series of analyses, each would have the same input characters. Thus, input data prepunched on paper tape may be used repetitively to automate the entire computer procedure. For illustrative purposes, a computer-reconstructed chromatogram of the data gathered by this re-

Figure 1. Computer reconstructed chromatogram of the data generated by the dialog in Table II

This is similar to a normal gas chromatogram except the abscissa is the mass spectrum number which is equivalent to the conventional time axis. Data acquisition begins after the solvent peak

Figure 2. Mass spectrum of methaqualone with gross interference

This spectrum was taken during a chromatogram of methaqualone while a mixture of perfluoroalkane and cyclohexane was simultaneously admitted to the mass spectrometer. The ordinate is expanded by a factor of 2. Note that the largest peak in methaqualone is only the ninth largest peak in the combined mass spectrum

quest is shown in Figure 1, but neither chromatograms nor mass ,spectra need be displayed for compound identification. T h e results of this acquisition and search process are in Table I11 where both the qualitative and quantitative data obtained from the program are seen. There has been one compound identified for each chromatographic peak. In each case, all ten reference ions have been matched within the allowable limits and the average deviation of these ten is also within limits. T h e column SUM IONS re- flects the approximately equal concentrations which are seen in the chromatogram.

The time required for a search has been quite satisfying. For a library of about 35 compounds, the search appears to be carried out a t tape speed, i . e . , there is no noticeable hes- itation following the inputting of each data block.

The improvement in specific identifications from spectra containing more than one compound which the reverse search provides can be demonstrated. A small sample of methaqualone was introduced uza the gas chromatograph while a n artificial interference was generated by leaking a mixture of perfluoroalkane and cyclohexane a t high levels into the mass spectrometer from its own inlet system. T h e resulting spectrum appears in Figure 2. For reference, the ten largest peaks of methaqualone occur a t masses (intensities): 65(17), 76(12), 77(9), 91(36), 132(11), 233(30), 235(100), 236(17), 250(50), and 251(9). A forward search technique which picks out the largest intensities in each 14 mass unit range would find four of these masses among the 80 masses selected from the mass spectrum in Figure 2 and none of these four would be in the right ratio with the other peaks selected for comparison. This will result in a poor or

48 ANALYTICAL CHEMISTRY, VOL. 47, NO. 1, JANUARY 1975

nonexistent correlation between those masses found and the library spectrum of methaqualone. In contrast, t h e reverse search extracts only the ten relevant masses from the spectrum in Figure 2 for a comparison. Without using the background subtract capability, the reverse search found t h a t eight of the ten intensities selected were within its allowable range yielding a HIT QUA1,ITY of 8-120. T h e background intensities a t masses 7'7 and 251 added t o the selected masses caused them t o be discarded as big positive deviations.

T h e remaining mechanical functions can be automated (sample injection, solvent bypass valve). I t is t o be expected tha t , under the supervision of a mass spectrometer spe- cialist, such a computerized GC/MS system will become automatic and capable of processing a large number of routine samples without operator intervention. In addition, the ease of operation will make qualitative and yuantita- tive answers t o routine problems accessible t o a wide range of users without requiring them t o understand either mass spectrometers or mass spectra.

ACKNOWLEDGMENT T h e assistance of Norris Huse and Royce Howard of Du-

pont Instruments is gratefully acknowledged. I also thank Mario Werner for many helpful discussions.

LITERATURE CITED (1) R. G. Ridley in "Biochemical Applications of Mass Spectrometry,'' G.

(2) L. E. Wangen, W. S . Woodward, and T. L. Isenhour. Anal. Chem., 43,

(3) S R. Heller. Anal. Chem., 44, 1951 (19721.

Waller, Ed.. Wiley-lnterscience. New York, N.Y., 1972, Chapter 6

1605 (1971).

(4) K. Kwock, R. Venkataraghavan, and F. W. McLafferty, J . Arner. Chem.

(5) C. E. Costello, H. S. Hertz, T. Sakai, and K. Biemann, Clin. Chem., 20,

(6) T. D. Sterling and S. V. Pollack, Ann. N.Y. Acad. Sci., 161, 632 (1969). (7) B. S. Finkle. D. M. Taylor, and E. J. Bonelli, J. Chromatogr. Sci., 10, 312

(1972). (8) N. C. Law, V. Aandahl, H. M. Fales, and G. W. A. Milne, Clin. Chim.

Acta, 32, 221 (1971). (9) P. Toft, B. A. Lodge, and M. B. Simard, Can. J. fharm. Sci., 7, 53

(1972). (10) H. Budzikiewicz. in "Biochemical Application of Mass Spectrometry," G.

Wailer, Ed., Wiley-lnterscience, New York, N.Y.. 1972, p 251. (11) J. R. Chapman and E. Bailey, Anal. Chem., 45, 1636 (1973). (12) E. M. Chambaz, G. Defaye, and C. Madani, Anal. Chern.. 45, 1090

(1973). (13) E. Gelpi, W. A. Koenig, J. Gilbert and J. Oro. J . Chrornatogr. Sci., 7, 604

(1969). (14) B. S. Middleditch and D. M. Desiderio, Anal. Biochem., 55, 509 (1973)

and earlier work cited therein. (15) 8. S. Samuelsson, E. Granstrom. D. Green, and M. Hamberg, Ann. N. Y.

Acad. Sci., 180, 138 (1971). (16) U. Axen, K. Green, D. Horlin, and B. S. Samuelsson, Biochern. Biophys.

Res. Comrnun., 45, 519 (1971). (17) J. N. Damico, R. P. Barron, and J. M. Ruth, Org. Mass Spectrom., 1,

331 (1968). (18) T. R. Kanter and R. 0. Mumma. Residue Rev., 16, 138 (1966). (19) R. Bonnichsen, C. G. Fri, B. Hedfjali, and R. Ryhage, Z. Rechfsmedizen,

70, 150 (1972). (20) T. L. Isenhour, Anal. Chem., 45, 2153 (1973). (21) G. Ramirez, R. C. Dinio. and H. C. Pribor, Comput. Bid. Med. 2, 39

(22) M. Lipkin, R. L. Engle, Jr., B. J. Davis, K. V. Zworykin, R. Ebald, M. Sen-

(23) R. L. Reece and R. K . Hobbie, Amer. J . Clin. Pathol., 57, 664 (1972).

Soc., 95, 4185 (1973).

255 (1974).

(1972).

drow, and C. Berkley, Arch. lnt. Med., 108, 124 (1961).

RECEIVED for review April 18, 1974. Accepted September 9, 1974. This paper was presented in par t a t the 21st Annu- al Conference on Mass Spectrometry and Allied Topics, American Society for Mass Spectrometry, San Francisco. Calif., 1973.

Negative Chemical Ionization Mass Spectrometry-Chloride Attachment Spectra

Harvey P. Tannenbaum,' J. David Roberts, and Ralph C. Dougherty

Department of Chemistry, Florida State University, Tallahassee, Fla. 32306

This paper explores the analytical potential of negative chemical ionization (NCI) mass spectrometry using methylene chloride as the reagent gas. The NCI mass spectrum of methylene chloride is dominated by CI-, HCIz-, and CH2C13- ions. Negative chemical ionization with this reagent gas results in chloride attachment to the substrate as the primary chemical ionization mode. The importance of chloride attachment and the sensitivity of the technique both increase with increasing ability of the substrate to form strong hydrogen bonds. The selectivity of the ionization makes this technique attractive for examining nonhydrogen- bonding substrates like ethers for traces of alcohol or acid impurities. Molecule anions resulting from resonance capture and fragment anions that were the result of disassociative capture were also observed in the spectra of specific substrates. Formation of molecule anions under these conditions appears to correlate with molecular electron affini- ties.

' Present address. E 1 I h P o n t de Nemours & Company, Tex- tile Fiber\. 1007 91arket Street. Wilmington, Del 19798

Negative chemical ionization mass spectrometry is an obvious extension of chemical ionization mass spectrometry (1-4) and nonreactive gas enhancement of negative ion mass spectra (5-7) . The bulk of the literature reports of negative ion mass spectra (8-16) have been concerned with spectra of compounds which readily form anions under NCI conditions. These compounds include haloalkanes (8-10), organometallics ( I O ) , nitroalkanes (12-14 ) , arid pesticidal compounds of the carbonate (15 ). organophos- phate ( I C s ) , and chlorinated hydrocarbon types ( 1 5 , 1 6 ) . In most of these cases, the spectra were not the result ot' chemical ionization in the usual sense. That is, the spectra were dominated by ions that resulted from resonance capture or disassociative capture of the thermalized electrons in the NCI plasma, and the abundance of ions that resulted from chemical reaction of reagent gas ions was generally low.

Chemical ionization with anions is a substantially "mil- der" form of ionization than corresponding reactions between cations and molecules. This is because the bonds tha t form between anions and molecules with few excep-

ANALYTICAL CHEMISTRY, VOL. 47, NO. 1. JANUARY 1975 49

automated identification of mass spectra by the reverse search

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