1989
Anal. Chem. 1984, 56,1989-1993
AIDS FOR ANALYTICAL CHEMISTS Establishing Identlfication Llmits of Model Compounds in Capillary Gas Chromatography/Fourier Transform Infrared Spectrometry J. R. Cooper a n d L. T. Taylor*
Department of Chemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061 Improvements in lightpipe design, liquid nitrogen-cooled detectors, high throughput optics, and computers have made analysis of very complex volatile samples by gas chromatography/Fourier transform infrared spectrometry (GC/FT-IR) possible. High efficiency, narrow bore capillary columns which provide the separating power needed for such complex samples have been interfaced successfully with FT-IR spectrometers (1-4). Such reports usually contain GC measured infrared spectra as well as static spectra of one or two standards that have been run to show the sensitivity of the GC/FT-IR method. These standards usually represent low nanogram quantities of compounds which often contain a strongly absorbing functionality. Compounds that have been used include isobutyl methyl methacrylate (5,6)ethyl acetate (4,7),anisole (8) and isobutyl acetate (I).While such studies have proven useful, these models may not represent the type of compounds found in ”real world” samples and thus may not represent average GC/FT-IR sensitivity. For example a standard mixture of priority pollutants has been analyzed by capillary GC/FT-IR and identified by spectral search routines (9). The amount of each pollutant needed, however, for spectral search identification was not in the nanogram range but was on the order of 4-8 s. In other words, published detection limits do not reflect the amount of material required for a spectral search identification. This paper seek to determine (a) the “detectionlimits” for a variety of compounds representing both polar and nonpolar functionalities, (b) the reliability (or ability at all) of the available spectral search routines to correctly match the known compound spectrum to a library f i e spectrum, and (c) the linearity of absorbance/concentration plots determined from infrared data. The term “detectionlimit” can be referred to in several ways and in many cases is user defined. Traditionally the term is defined as a recorded response giving a signal twice or three times the background noise level. We seek to define the term in relation to the ultimate goal of our work which is actual sample identity (Le., identification limit). This identity may come as a result of spectral search routines and/or to a lesser degree a visual “side-by-side”comparison of sample and library infrared spectra. The work to establish GCIF’T-IR “identification limits” has been performed keeping in mind the types of problems encountered when attempting to analyze complex, unknown samples. Some of these problems are discussed below. The minimum amount of component identifiable can be determined only if a reliable absorbance vs. concentration plot can be generated. Separate plots may need to be constructed for different functional classes and even for particular compounds of a single class. Also, the difficulties of establishing such “identification limits” include the fundamental fact that FT-IR data collection measures transient occurrences within the lightpipe. The actual point where the maximum absorbance occurs may be missed depending on the frequency of these transient measurements. In addition, in order to make 0003-2700/84/0358-1989$01.50/0
GC/FT-IR identification limit data truly meaningful, the same background noise level should be reproduced, ideally, between actual sample and standard compound; i.e., the same number of coadded F’T-IR data scans should be used for both analyses. Some care is required in obtaining such data. For example, while the coaddition of more scans would decrease the overall noise level by the square root of the number of scans, the resulting increase in the data collection time frame (s/file) may result in an apparent chromatographic coelution of adjacent components. Our rule of thumb regarding an acceptable time frame is that several data files should define a chromatographic peak. EXPERIMENTAL SECTION Instrumental Methods. All infrared spectra were generated by a Nicolet 6000C FTIR with a Nicolet 7000 external lightpipe (and detector) bench. A Varian 3700 gas chromatograph was interfaced to the 7000 bench via a heated transfer line. The gold-coated lightpipe (1.5 mm i.d. X 40 cm) was maintained at a temperature of 230 O C . Interferograms consisting of 2048 data points were collected which resulted in 8-cm-’ resolution infrared spectra after Happ-Genzel apodition and Fourier transformation to the frequency/absorbancedomain. Twelve data scans were collected and coadded per data file. Combined with a mirror velocity of 2.22 cm/s, a data collection time frame of 1.09 s/file resulted. A narrow band mercury-cadmium-telluride (MCT) detector with spectral windows from 5000 cm-’ to 710 cm-’ was used. The infrared throughput at the lightpipe temperature (230 “C) was adjusted just below the point of computer A/D overflow with minimal computer attenuation of IR signal. GC Conditions and Standards. The Varian 3700 GC was fitted with an on-column injector designed for use with wide bore fused silica columns. A syringe with a 0.17 mm 0.d. fused silica needle was used. The GC column was 60 X 0.33 mm i.d. fused silica with a chemically bonded DB-5(equivalent to SE-54) liquid phase of 1-pm film thickness. The column was fed through the heated transfer line ( 1mm i.d. stainless steel tubing) up to the entrance of the lightpipe, giving a virtually zero dead volume path. Nitrogen makeup gas was introduced to the transfer line resulting in a combined measured gas flow (column and make-up) of 8 mL/min through the lightpipe. The column temperature program was 60 O C for 2 min followed by 7 OC/min to 220 “C. A column linear helium flow velocity of 33 cm/s or -2.5 mL/min was used. All standard solutions were diluted with HPLC grade chloroform which contained no ethanol preservative. For all cases, a 0.5-pL injection volume was used. Spectral Search Routines. At least two methods of computer searching were available with the Nicolet software (5,IO). We chose to employ the method which uses any one of four search algorithms. One of these algorithms involves computing the intensity difference between the sample spectrum (S)and each member of the reference fie library (R)via a least-squaresmetric calculation. We call the resulting numerical value a “match value”, Le., lower value indicates a better match.
-
“match value” = MsQ = C(Si- RJ2 More consistent search results were obtained by using this type of calculation over the other three. The other algorithms include a calculation based on an absolute intensity difference, and two 0 1984 American Chemical Society
1990
ANALYTICAL CHEMISTRY, VOL. 56, NO. 11, SEPTEMBER 1984
Table I. Spectral Search Results" compound
ng (yes)
pyrrole Nfl-dimethylformamide amyl alcohol 3-picoline nonane cyclohexanol cyclohexanone di-tert-butyl ketone o-ethyltoluene cyclohexyl acetate m-cresol nitrobenzene anisole o-anisidine quinoline 2-tert-butyl-6-methylphenol
80 57 238 178 145 94
200 263 68 96
ng (no) 28 250 190 113 81 66 100 220 40 250 60
100 74 273 164 273 218 200 250 "The amount (ng) injected onto the capillary column by on-column injection is given along with a yes or no which indicates positive or negative "identification".
i 8 YIY G PYRROLE
calculations are done by taking the first derivative of the spectra. These algorithms are summarized below and are explained more thoroughly by Lowry and Huppler (5). 90
3520
30+o
2560
2bao
1600
.izo
b o
NAVENUMBERS
Flgure 1. FT-IR of peak file spectrum compared with EPA vapor phase spectrum of pyrrole.
It has been stated that the derivative searches can minimize the effects of sloping base line and broad nonspecific spectral features (such as wavelike base lines originating from interferometer instability and/or poor subtraction procedures). In practice, the MADand MsD algorithms are of little additional advantage over M B and MsQ algorithms for low SIN spectra. In most cases in this study, the MsQ algorithm was used more successfully. The search routine allows for several user-defined regions to be specified before the search begins. The 400-700 cm" region corresponding to MCT-A detector cutoff was ignored in all search routines. In extremely poor SIN situations, the 3700-4000 cm" region was also blanked. The search data base used was compiled by the Environmental Protection Agency and contains 3310 spectra reduced to 16 cm-' resolution. Several other model spectra were generated in-house and added to this library. The five best hits were listed and considered.
RESULTS AND DISCUSSION The actual sensitivity of FT-IRcannot be discussed without recognizing several limiting fadors regarding the FT-IRsignal. These factors include the general theory that the signal to noise ratio will improve by a factor equal to an increase in the square root of the number of coadded scans. The second fador involves the nonlinear response over the infrared region due to the infrared source, MCT detector, etc. These factors combine to influence the overall background noise which is present throughout an FT-IR analysis. The infrared noise spectrum was generated with the same lightpipe temperature, mirror velocity, and number of coadded scans used for the standard compounds tested. Generally, a peak to peak noise level of less than 0.002 absorbance units was observed over the entire working region of 4000-710 cm-l. As mentioned earlier, the term "identification limit" is being used in a broader sense in this report to indicate the concentration at which the actual identification of the compound (using infrared data) is no longer reliable. This approach is desirable because infrared absorbance a t two or three times the background noise level may indicate a functionality, but not necessarily an accurate compound identification. A computerized method based on spectral library search comparisons produced a listing of the most probable compound identities.
Table 11. Computer Search Routine Results for Injection of 80 ng of Pyrrolea EPA vapor phase library no.
match value
3302 371 3308 302 3309
1050 1059 1094 1218
567
pyrrole
bromoform 3-phenylpyridine l,l,l-trichloroethane 4-azafluorene
" See Figure 1. Squared algorithm used. 4000-3700 cm-' and 715-400 cm-' regions skipped. All infrared spectra were individually searched by using the previously described algorithms. Where applicable, background noise subtractions were performed in an effort to improve the infrared (frequency) base line. "Identification" for the 16 compounds that were examined has been defined as the ability of any one of these search routines to give the correct compound identification (i.e., compound listed first). The results for these 16 model compounds (which represent ketones, ethers, phenols, alkanes, etc.) are given in Table I. The success of the search routines is obviously dependent upon the amount of vaporized compound present in the IR cell. In addition, the uniqueness of the infrared absorbance pattern of the compound of interest over the entire spectral region may dictate how low identification limits can go. For example, 80 ng of pyrrole can be identified unambiguously even though the apparent infrared information seems minimal (see Figure 1and Table 11). The rather unique 718-cm-' band and the N-H stretching band may account for the low identification limit. Other factors which will affect the search result include the final infrared S I N ratio and the infrared base line stability. Because the infrared response c w e is nonlinear over the range 4000-700 cm-l, the correspondingbackground noise levels are also not constant over that same region. Due to nonlinear response, unique high energy absorbances, such as a N-H stretching vibration, may not always be utilized to advantage
ANALYTICAL CHEMISTRY, VOL. 56, NO. 11, SEPTEMBER 1984
1991
EPR VFIPOR W 9 S E 872 Y-CPIESOL
/
A
I
h -
P ?i
C
Figure 2. FT-IR of peak file spectrum compared with the EPA vapor
phase spectrum of m-cresol. because of high background noise. An identification which was limited due to these factors is shown in Figure 2. The spectrum of m-cresol at 250 ng shows many infrared bands. SIN, however, above 2600 cm-' is reduced considerably. Despite the high noise level, a band around 3600 cm-', characteristic of a phenol, is still apparent. The search routines appear not to function even though information is discernible. The poor results obtained from 250 ng of m-cresol illustrate a problem area in regard to spectral searching. In many situations, especially low SIN, a level base line (also devoid of wavelike properties) was needed for successful spectral searching. Although the derivative searches, i.e., MAD and MsD, are supposed to compensate for such base line anomalies, we found no great advantage over the MSQalgorithm. Base line correction via a background noise subtraction technique was often performed on spectra in an effort to improve the infrared (frequency) base line. Although computerized spectral searching may be limited at times by the aforementioned properties, good identification limits were achievable for many types of compounds. For example, the search routines function successfully for N,Ndimethylformamide at 57 ng, anisole a t 100 ng, cyclohexyl acetate a t 68 ng, and 2-tert-butyl-6-methylphenol at 250 ng (Table I). Spectra which represent the least amount of compound positively identified by search routine algorithms and the greatest amount not identifiable by the library search algorithms for N,N-dimethylformamideand cyclohexyl acetate are shown here as illustrative examples (Figures 3 and 4). One final feature of the search routine results can generally be recognized by merely scanning the compounds listed. Quite often, although positive identification may not have been achieved, the compounds listed can give an indication of the type of functional group(s) which may be present in the unknown spectra. For example, amyl alcohol was not identified even at 250 ng but the compounds that were listed represented alcohol functionality. Also, spectral search results for 94 ng of cyclohexanone gave other alkyl-substituted cyclohexanones as possibilities. Nonane, of course, gave alkanes in the list even at concentrations much less than the "identifiable" quantity. A substituted derivative of the compound type is
c?OL13
3i3D
3365
2590 212J w4~Fvd-l?E-c
lC5C
.IYC
7 1
Figure 3. FT-IR of peak spectrum less background compared with the EPA vapor phase spectrum of (A) N,Ndimethylformamide, (B) the least amount of compound positively identified by the search routines (standard mlxture C), and (C) the greatest amount not identifiable by the search routines (standard mixture D).
also listed first for cyclohexanol (192ng) and nitrobenzene (96 ng). Absorbance vs. Concentration. Another objective of this work was to determine whether an absorbancelconcentration relationship could be generated from IR data collected onthe-fly. A linear Beer-Lambert relationship was envisioned where absorbancelconcentration plots could be used for determining the concentration of functionally similar compounds. In one case the most intense peak was chosen for construction of the plot, although this choice may not have represented the compound's more unique functionality. In the other case the peak chosen represented the absorbance peak most characteristic of the compound in question. For many compounds, the "most characteristic" peak absorbance was not the most intense peak. The slope, y intercept, and linear correlation (squared) were generated with the absorbance/concentration data by applying a linear regression calculation. Generally, as the concentration of absorbing species increases, a point (or region) is usually reached where transmittance/concentration and also absorbancelconcentration data lose linearity. Over the working region used here, less than 500 ng, linearity appears to be preserved. The linear correlation values were in most cases greater than 0.95. Exceptions were cyclohexanol at both 2940 cm-l(0.806) and 1070 cm-* (0.704). Other low values may be due to base line in-
1992
ANALYTICAL CHEMISTRY, VOL. 56, NO. 11, SEPTEMBER 1984 EPFI VAPOR PHASE 10r3 RCETIC
RCiO. CYCLOhEXY-
..
AMOUNT INJECTED
...
.
(ng)
Figure 5. Plot of absorbance of indicated wavenumber band vs. amount Injected onto GC column. 0 0-6-c
r
t
0 0240
m
cr
0 0060
OOOOoO
80
160
240
320
400
480
560
640
AMOUNT INJECTED ( n g )
Flgure 6. Plot of absorbance of indicated wavenumber band vs. amount injected onto GC column.
d9VEW13EA5
Flgure 4. FT-IR of peak file spectrum less background flle spectrum compared with the EPA vapor phase spectrum of (A) cyciohexyl acetate, (B) the least amount of compound positively identified by the search routines (standard mixture C), and (C) the greatest amount not identified by the search routines (standard mixture E).
stability (especially above 3000 cm-l) and poor SIN conditions where subtraction techniques may affect the actual intensity. Background water absorbance in certain IR regions can lead to problems when the amount of background water changes over the course of a chromatographic run. Examples of these situations include 2-tert-butyl-6-methylphenol at 3640 cm-' (0.880)and amyl alcohol at 1045 cm-' (0.8713), corresponding to the relatively weak 0-H and C-0 vibrations, respectively. Even with a dry nitrogen purge these small background changes have been found to be significant for low SIN spectra. Typical absorbance vs. concentration plots are shown in Figures 5 and 6. Not surprisingly, the majority of the y intercepts gave negative values, indicating that for certain very low injected amounts, absorbance intensity was lost in the infrared background noise. By reviewing the slopes of the plots for each vibrational mode, one can obviously confirm that the C=O stretching mode in amides, C-H stretching vibration in alkanes, and the C-0 stretch in esters are three of the strongest infrared absorbing functionalities. At the other end, compounds such as picolines, quinolines, and substituted benzenes give markedly reduced infrared sensitivity. These compounds, however, will most likely be the type of compounds present in "real world" samples such as aviation fuels, coal extracts, or priority pollutants.
Finally, the work presented demonstrates the wide variation in infrared sensitivity for various compound types. Certainly, when complex unknown samples are analyzed, detection limits should invariably be represented by the type of compound found in that sample. The rapid collection of transient measurements may complicate the construction of a Beer's law plot. Systematic deviations from linearity, then, may be due to the method itself. Also, the possibility of not recording the actual maximum absorbance always exists despite relatively rapid data acquisition rates. Finally, the specification of which vibrational frequency to use may determine the linearity of the data points. Despite these difficulties, unique combinations of infrared absorbance patterns coupled with nanogram sensitivity maintain GC/FT-IR as a powerful hyphenated technique for complex sample analysis. Registry No. Pyrrole, 109-97-7;N,N-dimethylformamide, 68-12-2; amyl alcohol, 71-41-0; 3-picoline, 108-99-6; nonane, 111-84-2;cyclohexanol, 108-93-0;cyclohexanone, 108-94-1;ditert-butyl ketone, 815-24-7;o-ethyltoluene, 611-14-3;cyclohexyl acetate, 622-45-7;rn-cresol, 108-39-4;nitrobenzene, 98-95-3; anisole, 100-66-3;o-anisidine,90-04-0;quinoline, 91-22-5;2-tert-butyl-6methylphenol, 2219-82-1.
LITERATURE CITED (1) Wiikins, C. L.; Glss, N.; Whlte, R. L.; Brlssey, G. M.; Onyiriuka, E. C. Anal. Chem. 1982, 54. 2260. (2) Grlffiths, P. R. "The Present State-of-the-Art of GCIFTIR and HPLCI FTIR"; Presented at 1982 Pittsburgh Conference, Atlantic City, NJ, March 6-13, 1302; No. 204. (3) Shafer, K. H.; Jakobsen, R. J. "Appllcations of GCIFTIR to Envlronmental Pollution Sample Analysis"; Presented at 1982 Pittsburgh Conference, Atiantlc City, NJ, March 8-13, 1983; No. 029. (4) Smith, S. L.; Garlock, S. E.; Adams, G. E. Appl. Specfrosc. 1983, 37, 192.
Anal. Chem. 1984, 56, 1993,-1994 (5) Lowry, S.R.; Huppier, D. A. Anal. Chem. 1081, 53,889. (6) Rossiter, V. Am. Lab. (Fairfield, Conn.) 1982, 14 (June), 71-79. (7) Gariock, S. E.; Adams, G. E.; Smith, L. Am. Lab. (Falrfield Conn.) 1982, 14 (Dec), 49-55. (8) Kuehi, D.; Lemeny, G. J.; Griffiths, P. R. Anal. Spectrosc. 1980, 34, 222. (9) Shafer, K. H.; Cook, M.; DeRoos. R.; Jakobsen, R. J.; Rasario, J. D. Appl. Spectrosc. 1981, 35,459.
1993
(IO) Lowry, S. R.; Huppler, D. A. Anal. Chem. 1983, 55, 1288.
RECEIVED for review January 18, 1984. Accepted April 16, 1984. The financial assistance of the Air Force Wright AerLaboratories, Wright-Patterson AFB, OH! is gratefully appreciated. Onautical
Automated Determination of Sulfur( I V ) Using the Schiff Reactlon Gregory L. Kok,* Sonia N. Gitlin, Bruce W. Gandrud, and Allan L. Lazrus National Center for Atmospheric Research, Boulder, Colorado 80307 The recent modification of the Schiff reaction by Dasgupta e t al. (I) to determine S(1V) in aqueous solution is an improvement over the generally used West-Gaeke (2)technique. The improvements by Dasgupta et al. include the removal of the mercuric chloride fixing solution, improvement in sensitivity, and unambiguous analysis of S(1V) bound as the formaldehyde adduct, hydroxymethanesulfonic acid (HMSA). The latter is important since Kok et al. have found that the HMSA is resistant to hydrogen peroxide oxidation (3). In measurement of Southern California cloud water, micromolar concentrations of S(1V) and hydrogen peroxide (H202)have been found to coexist (4). The original studies by Dasgupta et al. oriented the analytical technique to the analysis of SO2 in the gas phase ( I ) . In this paper the technique is adapted for automated analysis using the Technicon segmented flow system. The reagent concentrations are optimized for the analysis of S(1V) in cloud and precipitation samples.
EXPERIMENTAL SECTION Reagents. Pararosaniline hydrochloride (PRA),J. T. Baker, was purified accordingto the technique of Scaringelli (5). A stock solution of 0.1% PRA was prepared in 1 N HC1. The analytical reagent was prepared by diluting 33 mL of the PRA solution and 40 mL of concentrated HCl in 250 mL of water. A stock buffered formaldehyde (HCHO) stabilizing solution was prepared from 8.5 mL of 37% HCHO, 36.4 g of 1,2-cyclohexylenedinitrilotetraacetic acid (CDTA) (Aldrich) and 8.0 g of NaOH diluted to 1L total volume. The final pH is approximately4.8. The CDTA is neutralized to the disodium salt and serves as both the buffer and chelating agent for removal of metal ion interferences. To stabilize the S(1V) from oxidation and to provide for color development with the PRA, buffered HCHO stabilizing solution must be added to the samples. A ratio of 1 part of stabilizing solution to 15 parts of sample is used. The addition of the stabilizing solution to the sample should be done immediately after sample collection. The automated Technicon sampler contains a reservoir which provides solution to the analytical system between actual samples. This background solution is used to provide the base line signal of the test. This solution is prepared by diluting 1part of stabilizing solution to 15 parts of deionized water. It is important that the concentration of HCHO, as derived from the stabilizing solution,be identical in both the sample and background solutions. In the absence of S(1V) some reaction of the PRA and HCHO takes place to produce a small absorbance. If the HCHO concentrations are not matched in the sample and background solutions a bias will occur in the analytical results. Removal of the hydrogen peroxide (H202)interference is accomplished by destroying the H202with the enzyme catalse. A catalase working solution is prepared by diluting bovine catalase (Sigma Chemical Co. (2-100) 1:lOO. Standards of S(1V) were prepared from sodium metabisulfite or sodium formaldehyde
bisulfite. The use of Brij-35 surfactant solution (Technicon Instruments) is not recommended, as a high background absorbance is produced. Without the surfactant, minor base line drift is observed when working at high sensitivity. Instrumentation. The analytical system is designed around a Technicon Auto Analyzer I1 system. The flow cell in the colorimeter has a path length of 5.0 cm and absorbance measurements were made at 580 nm. Figure 1 shows the flow system designed to automate the Dasgupta et al. procedure for the determination of S(1V). Reagent flow rates were chosen to parallel the dilutions used in the original procedure (1). The autosampler is setup with a timing cam which provides for 20 samples per hour with a 1:2 sample-to-rinseratio. The flow manifold shown in Figure 1 incorporates four steps necessary for the analysis. The first step is the removal of the air bubble which enters the system with the change of the sampling arm from the rinse to the sample position. The second step in the manifold is the addition of the segmenting air bubble. If the additional air bubble introduced by the sampling arm is not removed it will disrupt the segment sequencing. The use of the air bar on the Technicon pump I1 is highly recommended for precise addition of the segmenting bubbles in the flow system. This provides higher reproducibility under the high liquid flow conditions used in the analysis. The third step in the manifold is the addition of 0.10 mL m i d of 5 N NaOH. The purpose of the NaOH is to breakup the HMSA to form S032and HCHO. The one-turn mixing coil used after the addition of the NaOH is fabricated by cutting down a standard multiturn mixing coil. The short mixing coil reduces the time for air oxidtaion of the S(1V). In the fourth step acidic PRA is added. The mixing in this step is crucial, since a competition for the S(1V) is setup between the acidic PRA to form the colored complex and the HCHO to re-form unreactive HMSA. The manifold is designed so that the sample is added to the acidic PRA, providing the highest PRA concentration possible. The fiial pH of the system must be between pH 0.9 and 1.1. Following the addition of reagents, a 10-mindelay coil is used, allowing time for full color development.
RESULTS AND DISCUSSIONS The analytical technique is linear over the S(1V) concento 7.9 X lo4 M. The linear regression tration range 4.9 X equasion over this concentration range is: AbS = (4.31 x 104)[S(IV)- 0.016, r = 0.999 for n = 9. When extrapolated to zero concentration, the line gives a significant negative intercept. A series of calibration points over the concentration range 0-6.2 X lo-' M S(1V) indicate significant curvature and a zero intercept for the low concentration calibration. In detailed work on fluorimetric determination of S(1V) a t low concentrations, Dasgupta observed a similar negative intercept and curvature of the calibration line (6). The cause of this curvature is not presently understood.
0003-2700/S4/0356-1993$01.50/00 1984 American Chemical Society
