plished in a shorter period. Because of this, radiation programming should be beneficial t o those systems which use peak height measurements of t h e AA or A F signals rather than integration techniques. I n those cases t h a t t h e atomization process goes through a n oxide formation step a n d volatilization of t h e oxide occurs a t a temperature much lower than t h e 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 instantaneously. I t is in these situations t h a t t h e radiation method of' programmed heating can provide better powers of detection 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 t h a t radiation programming can facilit a t e 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 t h e salt, when t h e furnace heating is radiation controlled.
LITERATURE CITED G. F. Kirkbright, Analyst (London), 96, 609 (1971). J. D. Winefordner and T. J. Vickers. Anal. Chern., 44 (5),150R (1972). J. D. Winefordner and T. J. Vickers, 46 (5),192R (1974). G. Lundgren, L. Lundrnark, and G. Johansson, Anal. Chern., 46, 1028 (1974). (5) M. P. Bratzel. R. M. Dagnall, and J. D. Winefordner, Anal. Chim. Acta., 48, 197 (1969). (6) M. P. Bratzel, R. M. Dagnall, and J. D. Winefordner, Aoof. . . Soectrosc., . 24, 518 (1970). (7) S. R. Goode, Akbar Montaser, and S. R. Crouch, Appl. Spectrosc., 27, 355 (19731. (8) Akbar Montaser. S. R. Goode. and S. R. Crouch, Anal. Chern., 46, 599 (1974). (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 (1973). (1) (2) (3) (4)
~
~
~I
RECEIVEDfor 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 becomes 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 d a t a interpretation where t h e order of comparison between known and unknown d a t a 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. Although these two cases seem similar, t h e significant advantages o f a reverse search will be described. Automated identification processes are especially valuahle when operating a gas chromatograph/mass spectrometer system. owing to t h e large number of' unknown peaks frequently encountered. My experience with library searches of mass spectral d a t a has been unsatisfactory. T h e principal difficulty is t h a t conventional searches provide equivocal answers regarding t h e composition of t h e 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 d a t a u p to 1970 have been reviewed ( 1 ) . Several additional papers have appeared subsequently (2-51. All of these search methods are in t h e 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 t h e 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. T o eliminate interferences, t h e operator must first detect such admixed spectra, then identify some other spectrum t o subtract from t h e first to remove this interference, and, finally, determine how much of this second spectrum t o subtract from the first. In addition, t h e operator must decide which, if any, of t h e multiple suggestions reported by most forward search methods is the correct answer. These human interventions make the automation of t h e identification process difficult. T h e algorithm generating t h e similarity index in a forward search is fixed. Whatever t h e method (if any) for reducing spectra, whatever t h e method for increasing t h e im-
A N A L Y T I C A L C H E M I S T R Y , VOL. 47. N O . 1 , J A N U A R Y 1975
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portance of large peaks over small peaks, a n d whatever t h e 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 t h e library because t h e processirig is based on t h e unknown data. This differs from methods for manually interpreting mass spectra which acknowledge 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 t h e unknown spectrum. Obviously, a n unknown peak may be either larger or smaller than the reference library peak a t t h e same mass. T h e forward search method must treat both of these cases similarly. T h a t program cannot determine whether t h e 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 t h e d a t a compression step; or absent because it had zero intensity. Were this differentiation possible, specific actions based on t h e 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 attempt 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 incomplete quantitative data and is without value unless isotope ratio data are obtained. T h e goals of other search methods are to rank t h e five to ten compounds in t h e library which most resemble t h e unknown. T h e correct answer may appear on t h e list, often a t t h e 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 t h e library. Screening procedures, which pre-select library compounds for further consideration, do not frequently eliminate all output when the correct compound is not in t h e library so t h a t answers are generally provided. T h e operator must then determine which, if any, of t h e compounds listed agree with his unknown spectra. This may be a time consuming process requiring skill in mass spectral interpretation. EXPERIMENTAL 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 a n additional 4K of core to the HewlettPackard 2100A computer and a second Hewlett-Packard 797OB digital magnetic tape unit. T h e 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 R e v e r s e Search Method. T h e two initial 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, quantitation, 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 t h e assumed compound with the library spectrum. Most notably, this reverse strategy of selecting a library spectrum and extract-
46
ing from the unknown spectrum t h e intensities fitting t h e reference is the foundation of this new search pr0gra.m. Reverse search has two main advantages. First, by selecting only t h e intensities of t h e unknown corresponding to a reference spectrum, most interference is ignored. This enables t h e identification of a particular unknown among merged spectra even if the unknown produces few significant peaks in t h e total mass spectrum. This parallels the "manual" method previously described which allows identification of a compound u p to a point where the interference totally obscures it. A general statement of t h e 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 variable, 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 t h e 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 t h e 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 t h a t unknown. Second, selecting masses from the unknown based on each library spectrum permits much of t h e 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 t h e 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 spectrum includes no impurities. Deviations from the expected intensity may be rationalized by remembering t h a 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 t h a t compound present. A large number of negative deviations may occur when the intensity selected to normalize t h e 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 categories 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 combinations 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 t h e 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 t h e 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 t h e goal of quantitative analysis of the sample.
A N A L Y T I C A L C H E M I S T R Y , V O L . 47, NO. 1 , J A N U A R Y 1 9 7 5
T a b l e 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 b a s i s of s e a r c h is unknown spectrum Arbitrary intensities a r e selected from unknown Positive o r negative deviations a r e approximately equal in weight Spectra a r e not adjusted for fit Relatively sensitive to interference o r mix tures Qualitative data only
Identifies complete unknowns from a large library Ranked library compound suggestions as output Substantial operator interaction and judgment r e qui r e d Library s i z e limited by peripheral storage Search algorithms fixed
R a e r s e search
Data b a s i s of s e a r c h i s library spectrum Only intensities c o r r e sponding to library compound a r e selected Sign and number of the deviations a r e diagnostic Auto in at i c r e norm aliz ation Relatively insensitive to interference o r mixtures Qualitative and quantitative data simultaneously Identifies pre- selected compounds from a limited library Yes 'No responses for each library compound Automatic operation
Library s i z e limited by c o r e storage Search algorithms flexible
T h e reverse search also greatly improves t h e automation capabilities over t h e 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 spect r u m 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 t h e identification number of t h e C X run and t h e number of t h e 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 t h e library remove all other responsibilities from t h e operator. It appears t h a 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 isomers) (9-12), amino acids (131, prostaglandins (14-161, and chlorinated pesticides ( 1 7, 1 8 ) have readily differentiated spectra. In each example, a 100-compound library seems sufficient. It is also true that, in general, well-differentiated spectra allowed identification from a limited n u m ber 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 t h e several best suggestions of the identity of a compound as provided by forward search methods. Where t h e spectra of two com-
pounds differ only slightly, common ions may be included in t h e 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 t h e compounds in question and t h e separate reports combined. Each library is t o be tailored to t h e problem a t hand and therefore specific. This will remove t h e inaccuracies present when commercial libraries containing spectra from numerous laboratories are searched. Table I summarizes the concepts a n d 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 t h e determination 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 t h a 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 t h e library is of the diagnostic criteria for a variety of disease entities. T h e unknowns are observations (history, physical, and laboratory findings) which frequently are more numerous than necessary to define any single disease entity. A forward search in the clinical situation compares t h e 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 defining 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 section 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 u p to 100 compounds followed by a second record containing t h e corresponding 20 character names for each compound. A library resides in core for a n entire analysis. Spectra are p u t into t h e library using whatever criteria are deemed optimal, although normally t h e 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 t h e 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 t h a t the total intensity for a compound in a continuous set of scans, such as a gas chromatogram, is determined. If
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Table 11. Computer Input Dialog= SCAN1 -~ GCRUN GW COMMEN GASTRIC DRUG S C R E E N AQRATE 10
1
READY MECALC -~ GCRUN
GW
5 -:
GW
3 -:
READY SEARCH GCRUN LIBNO
Figure 1. Computer reconstructed chromatogram of the data generated by the dialog in Table II
1
This is similar to a normal gas chromatogram except the abscissa is the
READY
mass spectrum number which is equivalent to the conventional time axis. Data acquisition begins after the solvent peak
GO 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 time into mass/charge. The other parameters printed out by the computer are the residual values from the last time each particular program was run. a
Table 111. Search Program Outputa SPECIAL LIBRARY SEARCH 6 DATE 5’12 ‘73 GC ID GW AQRATE 10 SCTTME 2 RESPWR HIMASS 600 THRESH 8
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
1100
GASTRIC DRUG SCREEK LIBNO. 1 LIB NAME TEST 77 SEQLEN TDE KTITY PHESOBAKBITAL D I AZ E PAM
SCAN
HIT QirALlTY
55 104
10-160 10-084
SUM 10x3 *2**
0
43971 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, t h e report reads “NO COMPOUNDS IDENTIFIED”.
Performance of the Reverse Search Program. Table I1 exemplifies t h e 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 t h a 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 t h e data gathered by this re48
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 reflects the approximately equal concentrations which are seen in the chromatogram. T h e 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 hesitation following the inputting of each data block. T h e 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 t h e 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
A N A L Y T I C A L CHEMISTRY, V O L . 47, NO. 1, J A N U A R Y 1975
(4) K. Kwock, R. Venkataraghavan, and F. W. McLafferty, J . Arner. Chem. Soc., 95, 4185 (1973). (5) C. E. Costello, H. S. Hertz, T. Sakai, and K . Biemann, Clin. Chem., 20, 255 (1974). (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 (1972). (22) M. Lipkin, R. L. Engle, Jr., B. J. Davis, K. V. Zworykin, R. Ebald, M. Sendrow, and C. Berkley, Arch. lnt. Med., 108, 124 (1961). (23) R. L. Reece and R. K . Hobbie, Amer. J . Clin. Pathol., 57, 664 (1972).
nonexistent correlation between those masses found a n d t h e library spectrum of methaqualone. In contrast, t h e reverse search extracts only t h e ten relevant masses from t h e spectrum in Figure 2 for a comparison. Without using t h e background subtract capability, t h e reverse search found t h a t eight of t h e 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 a n d 251 added t o t h e 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 t h a t , under the supervision of a mass spectrometer specialist, 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 yuantitative 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 a n d Royce Howard of Dupont Instruments is gratefully acknowledged. I also thank Mario Werner for many helpful discussions.
LITERATURE CITED
RECEIVEDfor review April 18, 1974. Accepted September 9, 1974. This paper was presented in part a t the 21st Annual Conference on Mass Spectrometry and Allied Topics, American Society for Mass Spectrometry, San Francisco. Calif., 1973.
(1) R. G. Ridley in "Biochemical Applications of Mass Spectrometry,'' G. Waller, Ed.. Wiley-lnterscience. New York, N.Y., 1972, Chapter 6
(2) L. E. Wangen, W. S . Woodward, and T. L. Isenhour. Anal. Chem., 43, 1605 (1971). (3) S R. Heller. Anal. Chem., 44, 1951 (19721.
Negative Chemical Ionization Mass Spectrometry-Chloride Attachment Spectra Harvey P. Tannenbaum,' J. David Roberts, and Ralph C. Dougherty D e p a r t m e n t 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 nonhydrogenbonding 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 affinities.
'
Present address. E 1 I h P o n t d e Nemours & Company, T e x 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 ) . T h e 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 t h e carbonate ( 1 5 ). organophosphate ( I C s )and , chlorinated hydrocarbon types ( 1 5 , 1 6 ) . In most of these cases, t h e spectra were not the result ot' chemical ionization in the usual sense. T h a t is, the spectra were dominated by ions that resulted from resonance capture or disassociative capture of t h e 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 "milder" form of ionization than corresponding reactions between cations and molecules. This is because the bonds t h a t form between anions and molecules with few excep-
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