Anal. Chem. 1989, 6 1 , 73-77 (28) Nomenclature, Symbols, Units and their Usage in Spectrochemical Analysis-I1 Data Interpretation: Spectrochim Acta, Part 8 1078, 338, 241-245. (29) Long, G. L.; Winefordner, J. D. Anal. Chem. 1083, 55, 712A-724A. (30) Liteanu, C.; Rica, 1. Statfsticel Theory and Methodology of Trace Ana@&; Wiiey: New York, 1980; Chapter 7. (31) Patkln, A. J. Ph.D.Dissertetlon, Cornell Unlverslty, 1983. (32) ZSZT Cemera Tubes 4849 l H Series : RCAlSolM State Division: Lancaster, PA, 1977. (33) Martin, A. In Advances h €iecbon/cs and Electron phvslcs, Hawke. P. W., Ed.; Academic Press: New York. 1986 Voi. 67, pp 183-323. (34) Leverenz, H. W. An Introduction to Luminescence of Solids; Wiiey: New York, 1950; pp 381-390. (35) Traxylmayr, U.; Reldling, K. Znt. J . Mass. Spectrom. Zon Phys. 1084, 6 1 , 261-276.
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(38) Taylor, R. C.; Hattrick, M. C.; Maiina, R. F. Rev. Sci. Znstrum. 1083, 54, 171-175. (37) Robinson, G. A. The S///mn Intenslfler Target Tube: Seeing h the Dark; RCAlSold State Division: Lancaster, PA, 1977. (38) Turner, L. K.; Mantus, D. S.; Ling, Y. C.; Morrison, G. H. In 36th ASMS Conference on Mass Spectrometry and Applied Topics; San Francisco, CA. June 1988.
RECEIVED for review August 12, 1988. Accepted October 7, 1988. This work was supported by National Science Foundation, the Office of Naval Research, and the Materials Science Center at Cornell.
Differential Gas Chromatographic Mass Spectrometry Amit Ghosh and Robert J. Anderegg* Department of Chemistry, University of Maine, Orono, Maine 04469
A computer program for extracting single-component mass spectra from poorly separated chromatographic peaks of a gas chromatographic mass spectrometric (GC-MS) data set Is described. On the basis of the successive subtraction of pairs of raw mass spectra, the “ditferentiai mass spectra” are free from background lons and extraneous lons from coelutlng compounds. The cleaned up spectra allow good comparison to library spectra and facllltate reliable ldentiflcation. The technique also increases the apparent chromatographic resolution by sharpening the peaks. The method Is similar in some respects to other forms of derivative spectroscopy but has the advantage of reducing chemical noise. The approach is conceptually slmple, rapid, and easy to implement. Components that have an elution ditference of only one scan can be resolved. I n a direct comparlson, the method compares favorably to other readily available techniques for spectral cleanup.
INTRODUCTION The combination of gas chromatography (GC) and continuously scanning mass spectrometry (MS) provides an extremely powerful analytical tool for the characterization of complex mixtures. A difficulty remains, however, in that not all components in the mixture may be chromatographically resolved, even with high-resolution capillary GC (I). This leads to mass spectra that represent mixtures and are therefore subject to errors in interpretation or unreliable results in library retrieval routines. A large number of strategies have evolved to clean up the mass spectra of mixtures, ranging from the simplest background subtraction routine to sophisticated spectral reconstruction (2,3)or fador analysis (4,5). Although the methods vary widely in their approach, required computing power, and analysis time, they all have a common goal: to extract clean spectra from dirty ones. The ’dirt” can arise from instrument background (air, pump oil vapor, water), chromatographic septum or column bleed, or coeluting interferent, and it can produce spectra in which ion intensities are distorted or extraneous ions are present that have no relation to the compound of interest. In a number of types of spectroscopy, it has been shown that taking the first or higher derivative of the analytical signal *Author to whom correspondence should be addressed. 0003-2700/89/0361-0073$01.50/0
can result in improved spectral resolution (6). The idea is that
the analytical signal is changing at a different rate than that of the noise. We undertook this study to see if a similar approach could be used with repetitively scanned GC-MS data to provide spectra of better quality. To obtain the ‘differential” of a GC-MS data set, we subtracted the raw ion abundances of ions of the same mass in successive pairs of spectra, plotting the result. Compounds whose concentrations are increasing are represented in this subtracted plot by ions with positive abundances. That is, the abundance of ions for such compounds is greater in spectrum number X + 1than in spectrum number X. Compounds whose concentrations are decreasing appear with negative abundances. The ions of these compounds will be less abundant in spectrum X + 1than in spectrum X. Compounds present in about the same concentration in both spectra X and X + 1are not observed, because the abundances of their ions will subtract to zero. Despite the obvious simplicity of the approach, we find that it is capable of substantial spectral improvement. The method was first evaluated with simulated data using a commercial spreadsheet program. Finally, we tested the program on actual GC-MS data from the analysis of fuel oil and compared it to alternative methods of spectral cleanup.
EXPERIMENTAL TECHNIQUES GC-MS experiments were simulated by using an IBM PC-XT with 640K core memory as we have previously described (7). LOTUS 1-2-3(version 2.0) was used as the spreadsheet without any modification. For the analysis of real data, programs were written in FORTRAN on the data system of a Hewlett-Packard 5985 B GC-MS system. The data fide used was a GC-MS analysis of a fuel oil using capillary column GC and electron ionization mass spectrometry. Two consecutive scans (mass spectra) of a chromatogram (total ion plot) are considered. The absolute abundances of the masses in the first scan are subtracted from the absolute abundances of the corresponding masses in the later scan. The substracted intensities are plotted either in the positive or negative directions. They are normalized to a maximum of 100, taking the highest absolute values for each direction. These normalized intensities plotted against their m / z values give a ’differential mass spectrum”,which is assigned the same scan number BS the later of the two original spectra. The unnormalized abundances of the differential mass spectrum are summed to give a total ion abundance, in both the positive and negative directions, at that scan. Similarly,the total ion abundances are calculated for each scan for the entire scan range of the chromatogram. The total ion abundances are normalized to a maximum of 100 and plotted 0 1988 American Chemical Society
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Figure 1. (a) Total ion plot and (b) dlfferentlal GC-MS profile of the
simulated data. against their scan numbers to give the “differential GC-MS” profile (Figure lb).
RESULTS AND DISCUSSION Regardless of the assumed peak shape, compounds eluting from a chromatograph will increase in concentration to a maximum (the top of the chromatographic peak) and then decrease in concentration back down to zero. If the chromatograph is coupled to a repetitively scanning mass spectrometer, the ion abundances associated with the compound will likewise rise and fall with time. Our goal was to see if spectral improvement would be realized by having the computer progress through the data set, subtracting each mass spectrum from the one that follows it. In our “differential mass spectra”, ion abundances have positive values during that time when the concentration of the compound is increasing, are approximately zero near the top of the peak when the concentration is changing slowly, and have negative values when the concentration is decreasing. Any chemical noise in the spectrum (column bleed, pump oil vapor, air) will be more or less constant or changing very slowly with time. Ion abundances from these sources will subtract to near zero. The results are cleaner mass spectra, free from background ions. In the case of two compounds incompletely resolved by the chromatograph, the ions from each will contaminate, to some degree, the mass spectrum of the other. If, at any time, the concentration of one compound begins to decrease while that of the other compound is still rising, the differential mass spectra will display the spectrum of the increasing component with ion abundances in the positive direction and the spectrum of the decreasing component with ion abunadances in the negative direction. In this way, relatively clean mass spectra can be obtained for both compounds, even though their spectra overlap. The background, as described above, is virtually eliminated from both spectra. A few cases are simulated in Figure la. These data were generated by using a commercial spreadsheet program (Lotus 1-2-31,as we have described elsewhere (7). Compounds A, B,
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and C are simulated as eluting with Gaussian peakshape, and an exponentially increasing background is superimposed. As consecutive scans are subtracted, the ion abundances associated with compound A appear with positive values, indicating that the concentration is rising. As A reaches its maximum concentration and begins to decline, the corresponding ion abundances in the differential mass spectra have negative values. At the same time, the ion abundances for the compound B are appearing on the positive side, because the concentration of B is now increasing. Later in the plot, the abundances of B ions are negative as the concentration of B diminishes. Still later, compound C goes through a positive and negative cycle. The background ions, because of the slowly changing concentration, contribute little to any of the spectra. The summed ion abundances in both the positive and negative directions are plotted in Figure lb, and regions where the mass spectra of various components are located are indicated. Figure 2a shows one of the simulated mass spectra from the region of overlap of compounds A and B (Figure la, scan 22). Ions are present from both compounds. In the differential mass spectrum (Figure Zb), some ion abundances are positive, indicating an increasing concentration, while others are negative, indicating a decreasing concentration. These correspond to ions from compound B (increasing) and compound A (decreasing), respectively. The mass spectra of these components are cleaner than in the original data set and would presumably yield better results in retrieval routines or interpretation efforts (see below). It is also interesting to note that the apparent chromatographic resolution is improved in the total ion profile that results from summing the positive and negative ion abundances (Figure lb). Although no additional information is present in the data set, the information is presented in a fashion that is perhaps more useful than before. Clearly the greatest potential of a method of spectral deconvolution is in the analysis of a very complex data set. Petroleum products offer one such instance. Figure 3a shows
ANALYTICAL CHEMISTRY, VOL. 61, NO. 1, JANUARY 1, 1989 1001
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Figure 5. (a) Raw mass spectrum of scan 227 In the fuel oil data set. (b) Reconstructed mass spectrum and (c) dlfferentlal mass spectrum of the same region of the chromatogram.
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F W e 4. Dlfferentlal mass spectra of (a) scan 226, (b) scan 227, and (c) scan 228 of the data set in Figure 3.
a portion of the total ion profile from GC-MS analysis of a fuel oil. Figure 3b shows the same data set after processing by our "differential GC-MS" routine. It can be seen that the background noise has decreased markedly and that the apparent GC resolution has improved. Differential mass spectra at scans 226, 227, and 228 are shown in parts a-c of Figure 4, respectively. There are at least three major components overlapping in this region. Ions of component 1(for example, m/z 141,156) are on the negative side of scans 226-228 and are decreasing gradually because the concentration of component 1is also decreasing. This was identified as ethylnaphthalene. Component 2 maximizes at scan 226 and ita pure mass spectrum is obtained in the positive side of that scan. It can be noted that the ion abundances of the component 2 (for example m/z 145,160) are negligible in scan 227, this provides information that component 2 has maximized at scan 227. The ion abundances then become negative in scan 228, as the concentration of component 2 decreases. This compound was identified as dimethyltetrahydronaphthalene. The ion abundances of component 3 (for example m / z 57, 71, 85) go from positive in scan 227 to negative in scan 228. Therefore, the concentration of com-
ponent 3 is increasing in scan 227 and decreasing in scan 228. This compound was identified as 2-methyltridecane. The simplicity of our approach is its greatest advantage. It can be implemented with little difficulty on most mass spectrometer data systems, and yet it provides a substantial improvement in spectral quality. Despite the importance of spectral cleanup, commercial GC-MS data systems provide little in the way of usable aids. On the Hewlett-Packard instrument, the choices are limited to "background subtraction", in which the ion abundances of a chosen mass spectrum are subtracted from every other mass spectrum in the data set, and "spectral subtraction", in which the ion abundances of one (or more) mass spectrum are subtracted from (or added to) another spectrum. The former of these approaches is inadequate if the background changes in kind or intensity during the course of the analysis. The latter approach, spectral subtraction, can be used very effectively but requires a considerable level of interactive decision-making on the part of the analyst and so is less amenable to automated analysis. Although not available in the standard data processing package, spectral reconstruction as described by Biller and Biemann (2) is available in a very early (ca. 1979) version of the Hewlett-Packard software (SPEED). Other methods of spectral deconvolution (3-5) require rather sophisticated programming and/or external computing capability and so are not routinely available. In order to evaluate the differential mass spectra, we compared the results to those obtained with the Biller/Biemann routine for the same fuel oil data set as described above. Figure 5a shows a raw mass spectrum from the data set at scan 227. We believe this spectrum represents the compound 2-methyltridecane, with a molecular weight of 198. Scan 227 was chosen as the scan in which the mass chromatogram of m/z 198 maximizes. Figure 5b shows the reconstructed mass spectrum of the same compound, determined by using the Biller/Biemann algorithm. This spectrum was also chosen as the position where the m / z 198 mass resolved mass chromatogram maximizes. Although the spectrum is somewhat
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Flgure 6. Mass spectrum of 2-methyltridecane from the spectral library. For consistency with Figure 5, only masses above m l z 50 are
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different, it cannot be said to be much improved relative to the raw mass spectrum. In Figure 5c is the differential mass spectrum (our algorithm) for the same scan. For ease of comparison, only the ions with positive ion abundances are plotted; the full spectrum is the same as in Figure 4b. This spectrum was obtained automatically as each spectrum in the data set was subtracted from the one following it. The noise level in the spectrum is substantially decreased; the ion series at m / z 57,71,85, etc. is prominent, and the molecular ion is clearly visible. The mass spectrum of authentic 2-methyltridecane from the spectral library is shown in Figure 6. The differential mass spectrum more closely matches the library spectrum than do either the raw or reconstructed mass spectra. When each of the three spectra was subjected to a library search, only the differential mass spectrum (Figure 5c) had the correct compound as the first hit. For the reconstructed mass spectrum (all other search parameters the same), the correct compound was found as hit number 10; for the raw spectrum, the correct compound was not found in the best ten matches. The reasons for the poor performance of the spectral reconstruction become obvious if the data are examined closely. The fuel oil is an extremely complex mixture, and complete chromatographic resolution is impossible. In the region of the chromatogram under study, scans 225-229, at least four different compounds are eluting. The background noise level is high throughout the chromatogram. Figure 7a shows a portion of the mass chromatogram of m / z 91, an ion that appears almost continuously throughout the analysis. The spectral reconstruction routine is based on the location of maxima in the mass chromatographic profiles, and it finds many as the noise level randomly fluctuates. Figure 7b shows the reconstructed mass chromatogram, now reflecting where m / z 91 maximizes. This background ion, and others that behave similarly, will still appear in the majority of reconstructed mass spectra. The m/z 91 ion is prominent in Figure 5b. In contrast, with the differential mass spectra, a local background is subtracted a t every scan. The result is cleaner spectra (no ion at m/z 91 is observed in Figure 5c) and better performance in subsequent interpretive efforts, whether by manual or automated means. The greatest weakness of the differential mass spectral approach occurs when two coeluting compounds share common ions. In this case, the ions appear with abundances only in the positive or negative direction (but not both). The ion abundances in those circumstances are distorted, a common failing with most of the other reported spectral cleanup routines. In spite of this drawback, the differential mass spectra offer some very exciting advantages over other methods. The subtraction routine is very fast. The bulk of the computer time in our program is involved with retrieving the spectra for subtraction. The inherent speed of the calculation means that the differential mass spectra could be calculated in real time during data collection. The differential mass spectra can
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Portions of the (a) raw mass chromatogram and (b) reconstructed mass chromatogram of m / z 9 1 in the fuel oil data set.
Flgure 7.
be stored at the same time as, or even in place of, the raw spectra. No other cleanup procedure offers this possibility. Real-time cleanup makes the approach far more amenable to automated interpretation schemes. Secondly, even when two components elute at exactly the same scan, their spectra can be deconvoluted if the peak profiles are sufficiently different. To do this one must go to higher order differentials of the data set. For example, if two compounds elute at the same retention time, but one peak is twice as broad as the other, the “second differential”is used. The spectra are first subtracted (as described above), then the resulting “differential” mass spectra are sequentially subtracted. At the inflection points of each profile, on the rising and the falling sides of the curve, a clean spectrum of each pure component appears in either the positive or negative direction of the “second differential mass spectra”. Although we have not investigated any such situations, it seems that hgher order differentiationwould also allow the deconvolution of more than two components a t a time. Differential GC-MS is similar in many respects to UV-vis derivative spectroscopy. The derivative of a Gaussian peak is sharper than the original peak (8). This idea is exploited in derivative spectroscopy to enhance spectral resolution. A similar phenomenon is observed in differential GC-MS to get cleaner spectra from the raw data. Derivative UV spectroscopy cannot increase the amount of information already present in a spectrum but can enhance resolution from which one will be able to detect minor spectral features. Similarly, in differential mass spectra, there is no gain of information, but cleaner mass spectra are obtained for more reliable interpretation. In derivative UV spectroscopy, the improved spectral resolution is partially offset by a decrease in the signal-to-noise ratio (S/N). This is because the noise is high-frequency instrumental noise produced by the electronic Components and is directly proportional to the output voltage (49). In differential mass spectrometry, however, the unwanted noise is largely “chemical noise”, arising from background or coeluting components. Since this noise level is slowly changing, differentiation generally reduces the noise contribution, subtracting it to near zero. As a result, our data handling has the effect of simultaneously improving chromatographic resolution and increasing the S/N ratio. CONCLUSIONS A simple mass spectral cleanup program has been introduced. The proposed method can remove background ions from spectra and deconvolute overlapping peaks, even if they
A ~ I Chem. .
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have a very small elution difference. Higher order differentials can be used to deconvolute more complicated chromatographic situations. Although the technique has conceptual simplicity and relative ease of-implementation, it is capable of s u b s k i d spectral improvement and could even be generated in real-time during data collection. LITERATURE CITED (1) W l n g s . J. C.; Davis, J. M. Anal. Chem. 1983, 55, 418-424. (2) Biller, J. E.; Blemann, K. Anal. Lett. 1974, 7 , 515-526. (3) Dromey, R. G.; Steflk, M. J.; Rlndflelsch, T. C.; DuffleM, A. M. Anel. Chem. 1978, 48, 1368-1375.
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(4) Knwr, F. J.; Futrell, J. H. Anal. Chem. 1979, 57, 1236-1241. (5) Sharaf, M. A.; Kowalskl, B. R. Anel. Chem. 1982, 5 4 , 1291-1296. (6) Fell, A. F.; Scott, H. P.; Gill, R.; Foffat, A. C. J . Chromafogr. 1983. 273. 3-17. (7) Ghosh, A.; Morlson, D. S.; Anderegg, R. J. J . Chem Educ. 1988, 65, A 154-A 156. ( 8 ) Talsky, G.; Mayrlng. L.; Kreuzer, H. Angew. Chem., I n t . Ed. Engl. 1987. 17, 765-799. (9) O'Haver, T. C. Anal. Chem. 1979, 51, 91A-100A.
for review June 24, 1988- Accepted October 11,
1988.
Model for a Thermoelectric Enzyme Glucose Sensor Michael J. Muehlbauer,*JEric J. Guilbeau, and Bruce C. Towe Department of Chemical, Bio and Materials Engineering, Arizona State Uniuersity, Tempe, Arizona 85287
A new calorimetric sensor that detects glucose through the change In enthalpy produced by an enzymatlc reaction Is mathsinatkally modeled. The sensor Is a thennoplle attached to a membrane of immobllized glucose oxidase and catalase enzymes. The model predicts the temperature rise that occurs wlthln the membrane In response to the concentratlon of glucose. The dependence of the modeled temperature rlse on varlous condltlons such as the oxygen concentration, external heat and mass transfer, and enzyme loading Is sup ported wlth experhnentai data from a prototype sensor. The sensHlvlty of the sensor to glucose is characterized In terms of two quantttles: Its Initial value at low concentrations of glucose; and the range of response, specHled as the concentratlon of glucose at which the sensltlvlty devlates from Its lnltlal value by 10%. Each quantity Is represented slmply as a functlon of only two variables: an approxknate Thlele modulus and the mass transfer Blot rrumber. Each affects the two quantltles In opposing directlons. The lnltlal sensltlvity increases wlth an increase In Thlele modulus or a decrease in Blot number, whereas the range of response dimlnlshes.
INTRODUCTION Recent investigations with a thin-fii thermoelectric g l u m sensor have renewed interest in calorimetric enzyme probes (I). These probes operate by measuring the temperature rise produced in response to the heat evolved during an enzyme-catalyzed reaction of a specific substrate. Previous calorimetric probes were fabricated by using thermistors as the temperature-sensing element (2-5). Although extremely sensitive, these devices suffered due to poor rejection of common mode thermal signals and self-heating. Consequently, they could only be operated in controlled flow and temperature environments. The present sensor is thermoelectric and generates a passive signal with a much improved thermal common mode rejection ratio. No environmental control has been found necessary. The new sensor employs a thin-film thermopile to measure the evolved heat. Alternate thermoelectric junctions are coated with a membrane of immobilized glucose oxidase and 'Present address: Cytogam, Inc., 3498 N. San Marcos Pl., Suite 7, Chandler, AZ 85224. 0003-2700/89/0361-0077$01.50/0
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catalase. These enzymes catalyze the respective reactions glucose + O2 + H20 H202 gluconic acid + 80 kJ and H202
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1/202
+
+ H20 + 100 kJ
Each mole of glucose consumed produces 180 kJ of total heat. In operation, glucose diffuses to and reacts at the enzyme surface, producing heat that generates a temperature gradient across the thermopile. The result is a Seebeck voltage produced in proportion to the concentration of glucose. The efficient operation of the glucose sensor depends on the development of a substantial temperature rise inside the enzyme membrane in response to glucose. Likewise, to provide responsiveness to glucose, the concentration below which glucose remains the limiting reactant must be a reasonably high value. The various kinetic and transport parameters that control these two effects, however, often affect them in opposing directions. The optimum design of a reliably functioning glucose sensor therefore depends on the judicious selection of these parameters. For this purpose, a mathematical model that solves the descriptive energy and mass balances to provide an indication of the temperature response to glucose is essential. Previous mathematical models concerned with describing the transport and reaction of glucose inside membranes containing glucose oxidase have disregarded the energy balance (6, 7). These models were primarily concerned with the reaction of glucose as it related to the output of polarographic or potentiometric glucose Sensors. Hence the small variation of temperature was of no concern. In a new mathematical model described here, the heat balance is solved and the temperature arising a t the extreme end of the membrane, closest to the thermoelectric sensor, is of direct consequence with regard to the sensor output. To ascertain the validity of the model, results are compared with the experimental output from prototype thermoelectric sensors. As diagrammed in Figure 1,each sensor consists of a thin-film thermopile constructed on Mylar and mounted at the tip of a 3-mm-diameter catheter. The conductors of the sensor face the inside of the catheter, and the enzyme is immobilized on the external surface. Heat generated upon placement in a flow stream containing glucose is conducted across the Mylar for detection by the thermopile. When 0 1988 American Chemical Society
