2684
Anal. Chem. 1990, 62, 2684-2688
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LITERATURE CITED (1) Ahmed, E. M.; Dennison, R. A.; Shaw, P. E. J. Agric. Food Chem. 1978, 2 6 , 368-372. (2) Ahmed, E. M.; Dennison, R. A.; Dougherty, R. H.; Shaw. P. E. J . Agric. FoodChem. 1978, 2 6 , 187-191. (3) Salo, P.; Nykanen, L.; Suomalainen, H. J . Food Sci. 1972, 3 7 , 394-398. (4) Buttery, R. G.: Guadagni, D. G.; Ling, L. C. J. Agric. food Chem. 1978, 26, 791-793. (5) Buttery. R. G.; Guadagni. D. G.; Ling, L. C. J . Agric. food Chem. 1973, 21. 198-201. (6) Mihara, S.; Masuda, H. J. Agric. Food Chem. 1988, 3 6 , 1242-1247. (7) Kaliszan, R. Quantitative Structure-ChromatographicRetention Reia tionshlps: Wlley-Interscience: New York, 1987. (8) Rohrbaugh, R. H.; Jurs, P. C. Anal. Chem. 1985, 5 7 , 2770-2773. (9) Hasan. M. N.; Jurs, P. C. Anal. Chem. 1988. 6 0 , 978-982. (10) Stanton, D. T.; Jurs, P. C. Anal. Chem. 1989, 6 1 , 1328-1332. (1 1) Buydens, L.; Massart, D. L.; Geerlings, P. Anal. Chem. 1983, 55, 738-744. (12) Buydens, L.; Coomans. D.; Vanbelle, M.; Massart, D. L.; Vanden Driessche, R. J. Pharm. Sci. 1983, 72, 1327-1329. (13) Bermejo, J.; Guilien, M. D Anal. Chem. 1987, 5 9 , 94-97. (14) Raymer, J.; Wiesler. D.; Novotny, M. J . Chromatogr. 1985, 325, 13-22. (15) Gerasimenko, V. A,; Nabivach, V. M. J. Chromatogr. 1990, 498, 357-366. (16) Osmialowski, K.; Halkiewicz, J.; Kaliszan. R. J. Chromatogr. 1986, 361, 63-69. (17) Osmialowski, K.; Halkiewicz, J.; Radecki. A.; Kaliszan, R. J . Chromatogr. 1985, 346, 53-60. (18) Brugger. W. E. Structure-Activity Relationship Studies of Odorant Compounds Using Pattern Recognition Techniques. Ph.D. Dissertation, The Pennsylvania State University. 1977. (19) Stuper, A. J.; Bugger, W. E.; Jurs, P. C. Computer-Assisted Studies of Chemical Structure and Biologicai function ; Wiley-Interscience: New York, 1979; pp 83-90. (20) Jurs, P. C.; Chou, J. T.; Yuan, M. I n Computer-AssistedDrug Design; Olson. E. C., Christoffersen, R. E. Eds.; American Chemical Society: Washington, DC, 1979; pp 103-129. (21) Schnabel, K.-0.; Belitz, H.-D.; Ranson, C. v. Z . Lebensm.-Unters.Forsch. 1988, 187, 215-223. (22) Jennings, W.; Shibamoto, T. Qualitative Analysis of Flavor and Fragrance Volatiles by Glass Capiilary Gas Chromatography; Academic: New York, 1980. (23) Brugger, W. E.; Jurs, P. C. Anal. Chem. 1975, 4 7 , 781-783.
(24) Stuper, A. J.; Jurs, P. C. J . Chem. Inf. Comput. Sci. 1978, 76. 99-105. (25) Rohrbaugh, R. H.; Jurs. P. C. UDRAW; Quantum Chemistry Exchange, Program 300, 1988. (26) Burkert, U.; Allinger, N. L. Mdecular Mechanics; ACS Mor\ograph 177; American Chemical Society: washington, DC, 1982. (27) Clark, T. A. Handbook of Computational Chemistry: A Ractical GUMe to Chemical Structure and Energy Calculations; Wiley: New York, 1985. (28) Rohrbaugh, R. H.; Jurs, P. C. Anal. Chim. Acta 1987, 799, 99-109. (29) Abraham, R. J.; Griffiths, L.; Loftus, P. J . Comput. Chem. 1982, 3 , 407-416. (30) Abraham, R. J.; Smith, P. E. J. Comput. Chem. 1988, 9 , 288-297. (31) Stanton. D. T.; Jurs, P. C. Anal. Chem. 1990, 6 2 , 2323-2329. (32) Neter, J.; Wasserman, W.; Kutner, M. H. Applied Linear Statistical Models, 2nd ed.; Richard D. Irwin: Homewood, IL. 1985; pp 430-436. (33) Small, G. W.; Jurs, P. C. Anal. Chem. 1983, 55, 1121-1127. (34) Neter, J.; Wasserman, W.; Kutner, M. H. Applied Linear Statistical Models, 2nd ed.; Richard D. Irwin: Homewood. IL. 1985; pp 417-429. (35) Beisley, D. A.; Kuh, E.; Welsch, R. E. Regression Diagnostics: Identiwing Influential Data and Sources of Collinearity; Wiley-Interscience: New York, 1980. (36) Allen, D. M.; Technical Report 23, Department of Statistics, University of Kentucky: Lexington, KY, 1971. (37) Snee. R. D. Technometrics 1977, 19, 415-427. (38) Randic, M. J . Chem. Inf. Compur. Sci. 1984, 2 4 , 164-175. (39) Neter, J.; Wasserman, W.; Kutner. M. H. Applied Linear Statistical Models, 2nd ed.; Richard D. Irwin: Homewood, IL, 1985; pp 382-390. (40) Draper, N. R.; Smlth, H. Applied Regression Analysis. 2nd ed.;WlleyInterscience: New York. 1981; p 420.
RECEJYED for review June 19,1990. Accepted September 17, 1990. This work was supported by the National Science Foundation (Grant CHE-8815785) and by the Department of Defense (NDSEG Graduate Fellowship DAAL03-89-G0069). Portions of this paper were presented at the 41st Annual Pittsburgh Conference and Exposition on Analytical Chemistry and Applied Spectroscopy, New York, NY, March 8, 1990. Portions of this paper were also presented a t the 199th Annual American Chemical Society National Meeting, Boston, MA, April 24, 1990.
Prediction of Gas Chromatographic Retention Indexes for Polychlorinated Dibenzofurans Albert Robbat, Jr.* and Christos Kalogeropoulos
Department of Chemistry, Trace Analytical Measurement Laboratory, T u f t s University, Medford, Massachusetts 02155
A model has been developed by using molecular connectlvlty to describe the relatlonshlp between the molecular structure of polychlorinated dlbenzofurans (PCDFs) and thdr gas chromatwaphlc linear temperature-programmed retention characteristicson a 30-m fused silica column coated with a D E 5 stationary phase. Model varlables account for the number of chlorlne atoms present, the position and the reiationship on each aromatic ring, the chiorlne atam lnteractlon between the two rings, and the skeletal structure of each PCDF isomer.
INTRODUCTION The chemical, physical, and suspected toxicity similarities between polychlorinated dibenzo-p-dioxins (PCDDs) and the *To whom correspondence should b e addressed.
dibenzofurans (PCDFs) are the motivation for continued development of analytical methods capable of identifying the 75 PCDD and 135 PCDF isomers ( I ) . In particular, PCDFs appear to be resistant to chemical and biological degradation. PCDFs have been found in soil, river and ground waters, and effluent waters from incineration, as well as in fly ash (2-5). To date, high-resolution capillary gas chromatography/maw spectrometry (GC/MS) has been the primary mode of identification. Nonetheless, MS does not provide sufficient PCDF ion fragment differentiation to confirm the identity of specific isomers. Increasing cost differentials between GC with MS and electron capture detection (ECD) has prompted the development of two-column identification (6-8). Isomer-specific measurement is necessary because of the major toxicological differences within the PCDF family of compounds (9-14). Past research from this laboratory has resulted in the development of predictive models for the identification and confirmation of nitrated aromatic hydrocarbons (15-2 7) and
0003-2700/90/0362-2684$02.50/00 1990 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 62, NO. 24, DECEMBER 15, 1990 2885
c1
Table 1. Molecular Connectivities, m OX 'X 2X
3xa 4Xa 5Xa
~ t
X0
6X v a 3 vc
OXV 1 va X
6
*XV
5X Ea 4xpca
3X c a
3X v a 4 va
5x"
X
5X " a
a
X
5X vc 4xwa Sxwa
BXw a
a Molecular connectivities used to obtain eq 3 (Le., Pearson correlation coefficient 50.96 and no steplike behavior between and I).
Table 11. Equation 3, Regression Coefficients and Statistical Information 1
v
constant = . $ = + $ + $ + $ i $ = 4 . 6 z 4 z
(3*.v)(6xv)
(3xv)(4xv) paths: a,m b.g,h,n,o p,q 0,f.i.l d,ej,k Figure 1. Example calculation of the first-order valence path molecular connectivity of 1,6-dichIorodibenzofuran.
chlorinated biphenyls (18). Others have developed GC chromatographic prediction models for both PCDFs and PCBs (19-22). Hale et al. (19) have published retention indexes for 115 PCDFs on a DB-5 (5% diphenyk9470 dimethyl:l% vinyl polysiloxane) stationary phase. In this paper, we report on a multivariate regression model capable of predicting the linear temperature-programmed GC retention characteristics of PCDFs based on the Hale index. The model relates PCDF molecular structure (through molecular connectivity descriptors) with the experimentally measured retention indexes.
EXPERIMENTAL SECTION Molecular connectivities, as devised by Randic (23)and later expanded by Kier and Hall (24),are topological descriptors of a compound's molecular structure based on skeletal atom groupings. The designation "xt defines a specific fragment type associated with a molecule. The superscript m indicates order, while superscript t indicates fragment type, e.g., paths of one or more bonds, clusters (a tripod-like structure) where three atoms are adjacent to a single atom, and path clusters that are similar to a cluster with one or more atoms connected to any of the three atoms that form the cluster. A valence value (av), equal to the valence of an atom minus the number of hydrogen atoms attached to that atom, or a nonvalence value (a), the number of non-hydrogen atoms that are connected to the atom of interest, is assigned to each atom of the molecule excluding the hydrogen atoms. The valence molecular connectivity indexes ("f, "xW,"xw)were calculated by using the valence values of the atoms that account for the nature of non-hydrogen atoms and their differences in bond orders. The nonvalence molecular connectivity indexes ("x, "xC, "xw) were calculated by using the nonvalence values where all non-hydrogen atoms and bonds between them are considered to be identical. Shown in Figure 1 is an example of atom value assignments for 1,6-dichlorodibenzofuranand the corresponding 'xVcalculation. Molecular connectivity calculations were made on a Digital Equipment Corp. Series 10 (DEC 10) computer. The retention index for the 115 PCDF isomers were experimentally measured by Hale and co-workers (19)and were used in this paper. Multiple regression analyses were made on a Digital VAXII/780 computer using the SAS statistical package. The MINITB statistical package was used to select 25 of the possible 120 "xt and product terms used in developing the predictive model. The random selection procedure was repeated at least 50 times. An apparent "best" statistical equation was established on the basis of the retention model criteria. RESULTS AND DISCUSSION Shown in Table I are the 24 "xt descriptors evaluated in this study. The best single-variable regression between retention index I and "xt yielded eq 1, with r2 = 0.967 and s = 49.32. This equation did not predict the relative retention
(5x'p")( 6 ~ v ) (3~c)(3~')
(3xc)(3x)
(5xw)(4xv) 13412.5
2636.8 17157 -6734.4 -35289.0 -535.2 259.5
S
F value DF P n
20.82 3212 (6,108) 0.9944 115
times with sufficient accuracy to be useful in identifying PCDF isomers.
I = 337.07(3~)- 40.82
(1)
Backward, stepwise multiple linear regression analyses performed on the "xt descriptors resulted in eq 2, a five-descriptor equation, with r2 = 0.992 and s = 24.60. However,
I = 1478.2(3~v) + 9849.2(3~c)- 45926.2(3~vc)-
+
9 9 9 1 ( 5 ~ v ) 13453.3(4~v) - 12983.4 (2) the plot of residual versus I,,, revealed a pattern of over/ under prediction of I , while the plot of Iprdversus I,,, resulted in a nonlinear response. Equations containing six or seven descriptors did not significantly reduce s. Multiple linear regression analyses were performed utilizing "xt descriptors that were not highly correlated with each other (Pearson correlation 10.96) and did not produce single-variable regression analyses exhibiting steplike behavior. The latter resulted in the clustering of the data points at particular I values. The goal was to produce a model that predicted I values with the correct elution order (and accuracy) and with random noise about the zero value in the residual versus experimental I plots. Fifteen descriptors, identified in Table I, and their products were used to develop the model. Because of the large number of variables to be regressed and the high demand on CPU time, multiple regression analyses were performed on randomly selected subsets (containing 25 variables) of the total set of variables. (Note: 120 descriptors resulted from 15 "xt's and their products). Shown in Table I1 are the coefficients, variables, and statistics for a six-variable equation whose root mean square error, s, is 20.82 index units. This represented an average percent error of less than 1.5 over the entire measured PCDF index range on the DB-5 stationary phase. the number, Table I11 lists the retention index range, Irmge, n, of data points for a given I , and the I,,, value for each isomer as reported by Hale and co-workers (19). The I,,, was used to develop eq 3 (Table 11). The predicted I values and their corresponding residuals are also listed in Table I11 for each isomer. It should be mentioned, however, that a few six-descriptor equations were found that had similar predictive capabilities relative to eq 3. Nonetheless, eq 3 is expected to be representative of the "best" equation if an exhaustive evaluation were performed, i.e. all possible combinations of the 120 descriptors. The "xt terms found include two cluster terms, viz., 3xc and 5x*. The first encodes information about the level of chlorine substitutions, while the second includes that information as well as the position of the cluster center
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ANALYTICAL CHEMISTRY, VOL. 62, NO. 24, DECEMBER 15, 1990
Table 111. Comparison of Predicted vs Measured Retention Indexes of PCDFs
obsd no.
conven. tional nomenclature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
13241314171824163727122836264623341913713813624913414712414814624724824623912712812334913912623723834734823634614923412913681468246813471378124713481346124812461367137912681478146723682467136912371238-
this study
Hale e t al. (19)
obsd
Irmge
n"
L"
Ipred
residuals
1736-1743 1746-1753 1746-1753 1757-1762 1883-1886 1911-1916 1910 1925 1912
3 3 3 3 3 2 1 1 1
1739 1749 1749 1760 1884 1913 1910 1925 1912
1738 1746 1720 1733 1921 1926 1921 1926 1912
1 3 29 27 -37 -13 -11 -1 0
1929-1931 1930 1930-1937 1934-1936 1943-1945 1943-1949 1951-1955 1932-1943 1950-1964 1974-1975 2051-2060 2067-2072 2068-2075 2082 2088 2086 2083-2088 2097-2102 2085-2098 2098-2099 2089-2103 2098-2104 2110-2113 2104-2115 2129 2113 2125 2123-2125 2119-2130 2134 2131-2134 2149-2151 2150-2153 2139-2143 2147-2155 2151 2148
3 2 2 2 2 3 2 3 3 2 5 4 4 1 2 1 2 2 5 2 3 2 2 2 1 1 1 2 4 1 2 3 3 2 3 3 1
1930 1931 1934 1935 1944 1946 1953 1939 1959 1975 2057 2070 2072 2082 2088 2086 2085 2100 2094 2099 2097 2101 2111 2109 2129 2113 2125 2124 2125 2134 2132 2150 2151 2141 2152 2151 2148
1958 1942 1930 1916 1938 1924 1937 1939 1950 1964 2072 2078 2099 2105 2116 2098 2120 2103 2118 2089 2085 2097 2112 2119 2123 2131 2129 2127 2132 2158 2141 2150 2144 2129 2136 2139 2137
-28 -1 1 4 19 4 22 16 0 9 11 -15 -8 -27 -23 -28 -12 -35 -3 -24 10 12 4 -1 -10 6 -18 -4 -3 -7 -24 -9 0 7 12 16 12 11
2225-2228 2241-2244 2254 2246-2265 2260-2265 2259-2269 2276 2262 2273-2274 2263-2267 2271-2273 2269-2277 2281 2290 2286-2290 2296-2298 2304-2306 2293-2299 2290-2298 2307-2308
2 3 1 4 5 3 1 2 2 5 2 4 1 1 2 2 2 3 3 2
2227 2242 2254 2257 2263 2264 2276 2262 2274 2264 2272 2273 2281 2290 2288 2297 2305 2296 2294 2307
2222 2264 2245 2278 2256 2289 2282 2290 2297 2306 2273 2260 2296 2293 2301 2287 2288 2288 2307 2313
5 -22 9 -21 7 -25 -6 -28 -23 -42 -1 13 -15 -3
-13 10 17 8 -13 -6
no. 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
conventional nomenclature 1234123623491469127813491267234712791249234823462378236734671269123912891346812468234791347813467123681247812467134792346912479234681346912347124691234812346123781236712379234891348923478234671248912369123491238912346813468124678134679124679124689123478123467123678123479123679123689123469234678123789123489123467812346791234689123478912346789-
this study
Hale et al. (19) Lmge
no
Imem
I p d
residuals
2306-2317 2306-2307 2308 2314 2317-2325 2324-2326 2327-2333 2337 2340-2342 2334-2335 2338-2342 2336-2342 2336-2340 2349-2359 2361-2362 2361-2367 2369 2405-2408
3 3 1 1 4 2 3 1 2 3 3 2 4 5 3 5 1 5
2310 2307 2308 2314 2322 2325 2329 2337 2341 2335 2340 2339 2338 2354 2362 2364 2369 2406
-33 -12 -7 14 -1 1 0 9 38 8 8 20 -33 1 25 41 29 64
2462-2472 2467-2472 2467-2470
2 3 2
2467 2469 2469
2465-2466 2472-2474 2474-2478 2479 2495
2 2 3 1 2
2465 2413 2476 2479 2495
2495 2497 2506-2510 2495-2496 2507 2540
2 1 3 2 1 1
2495 2497 2508 2496 2507 2540
2521
1
2521
2551 2555 2559 2546
2 1 1 1
2551 2555 2559 2546
2593 2650
1 1
2593 2650
2686 2708 2704-2708
1 1 3
2686 2708 2706
2719-2722
2
2120
2748
7
2748
2895-2900 2913-2914 2922-2924 2985-2987 3140-3151
5 3 5 4 3
2898 2913 2922 2986 3147
2344 2319 2315 2300 2323 2324 2329 2328 2303 2327 2332 2319 2371 2353 2337 2323 2340 2342 2423 2457 2442 2472 2415 2469 2492 2495 2466 2486 2467 2480 2482 2517 2486 2527 2525 2511 2512 2487 2524 2509 2543 2520 2519 2542 2555 2536 2684 2653 2694 2666 2669 2684 2727 2716 2702 2705 2701 2712 2722 2720 2725 2755 2920 2914 2930 2955 3179
25 -3 -6 -30 7 -10 12 15 -22 11 -19 -29 -4 28 -3
8 25 40 31 57 -34
2 -19 -10
15
28 -22 -1 -8 31 -32
n equals the number of chromatographic experiments.
relative to the biphenyl linkage and the furan-phenyl ring cyclization. The remaining descriptors describe bond paths
around the molecule. For example, 3xv,4xv,and 6xvprovide information with respect to consecutive atom valencies in
ANALYTICAL CHEMISTRY, VOL. 62, NO. 24, DECEMBER 15, 1990
60
2887
Table IV. Comparison of Predicted Retention Indexes of 20 PCDFs Iprd
obsd no. 10 48 87 88 92 93 99 106 108 113 116 117 118 119 123 125 126 127 129 130
n ; i -20 w -60
Hale et al. (29)
this study
1923 2190 2418 2423 2471 2473 2497 2524 2542 2572 2660 2665 2675 2680 2714 2728 2734 2736 2772 2780
1946 2147 2423 2457 2469 2492 2482 2487 2509 2555 2653 2694 2666 2669 2702 2701 2712 2722 2725 2755
1 , 1700
2000
2300
Figure 2. Plot of residuals versus I,
2600
2900
3200
based on eq 3.
32004 2900
1700 Figure 3. Plot of I,,
I
I
I
I
I
2000
2300
2600
2900
3200
versus I,,,
based on eq 3.
relation to the respective bond paths. The products of these descriptors (vs '"xt's alone) remove the structure from the model (Le., produce random noise in the residual plots; see Figure 2). Figure 3 illustrates the linearity of the Iprdversus 4"plot. Slight improvement of the model was realized if observation numbers 86 and 114 (-3 times s of eq 3) were removed from the data set, yielding a new mean square error of 19.14 index units. Equations containing seven variables were statistically only slightly better than the six-variable case, while those containing less than six were not much better than the single-product variable case (s = 30). Moreover, the best multiple-parameter equation for PCDFs that met the predictive model objectives required all '"xtproduct terms. In contrast, the model developed for polychlorinated biphenyls (PCBs) required a five-descriptor equation containing a single-product term (18). Validation of the model and determination of the minimum number of compounds required to produce a reliable predictive tool was evaluated by randomly selecting between 20 and 90 compounds (with each subset incremented by five). Ten random compound sets within each subset were regressed by using eq 3. The 10 randomly selected sets within the 50 and 55 compound subsets, respectively, produced an average root mean square error of 21.03 f 1.4 index units. This compares favorably with the 90 compound subset, i.e., s = 20.83 f 0.6 (Note: Recall that s for eq 3 equaled 20.82). Cross-comparison between the predicted and measured I values for the remaining 45 PCDF compounds yielded r2 = 0.999. The total number of data points used to develop the Hale et al. (19) model 3 was 272. For most isomers, single chromatographic experiments were performed while some isomers retention data consisted of one to five data points. Thus, model 3 was based on a weighted average of retention data. Equation 3 (this study) was not. It was based on the single
isomeric retention index reported in the manuscript. Discussion with the authors revealed GC retention run-to-run variations for isomers having multiple retention data was approximately 7 index units. Therefore, direct comparison of the two models for predicting PCDF retention behavior was difficult. Nevertheless, the two models in combination may provide a better "tool" for retention predictions of unknown PCDFs in a "real world" sample. Table IV compared the index predictions of our and Hale's models for 20 PCDF compounds for which data did not exist. The models provided a mechanism for determining which isomers may interfere in the analysis of those isomers believed to be toxic. In addition, since 50-55 PCDFs were needed to develop a reasonable predictive model, it was suggested that the same number of polybrominqted dibenzofurans (PBDFs) should be required for model development comparable to eq 3. This proposition was based on retention characteristic similarities between the PCDFs and PBDFs (presumably due to halogen atom size difference only) (25). This is important since PBDFs are a major problem in the brominated flame retardant industry.
LITERATURE CITED ( 1 ) Rappe, C. Environ. Sci. Technol. 1984, 78, 78A-90A. (2) Buser, H. R.; Bosshardt, H.-P.; Rappe, C. Chemosphere 1978a, 7, 109. (3) Buser, H. R.; Bosshardt, H.-P.; Rappe, C. Chemosphere 1978b, 7,
.-.
AlQ 1
(4) Czuczwa, J. M.; Hies, R. A. Natl. Meet.-Am. Chem. Soc.. Div. Environ. Chem. 1983, 23 (2), 74. (5) Tong, H. Y.; Shore, D. L.; Karasek, F. W. Anal. Chem. 1984, 56, 2442. (6) Mazer, T.; Hileman, F. D.; Nobel, R. W.; Brooks, J. J. Anal. Chem. 1983. 55. 104. (7) Yasuhara; A.; Hiroyasu, I.; Masatoshi, M. Environ. Sci. Technoi. 1987, 27, 971. (8) Afgan, 8. K.; Carron, J.; Goulden, P. D.; Lawrence, J.; Leger, D.; Onuska, F.; Sherry, J.; Wilkinson, R. Can. J . Chem. 1987, 65, 1086. (9) Tong, H. Y.; Karasek, F. W. Chemosphere 1986, 75 (9-12). 1141-1 146. (10) Crummett, W. B. Chemosphere 1983, 72 (415), 429-446. (11) Adams, R. E.; Thomason, M. M.; Strother, D. L.; James, R. H.; Miller, H. C. Chemosphere 1986, 75 (9-12), 1113. (12) Davies, I. L.; Bartle, K. D.; Williams, P. T.; Andrews, G. E. Anal. Chem. 1988, 60 (3), 204. (13) Christophersen, A. S.; Biseth, A.; Skuterud, B.; Gadeholt. G. J . Chromatoar. 1987. 422. 117. (14) Ligon: W. v., ir.; Mgy, R. J. J . Chromatogr. 1984, 294, 87. (15) Robbat, A., Jr.; Corso, N. P.; Doherty. P. J.; Marshall, D. Anal. Chem. 1988, 58, 2072. (16) Doherty, P. J.; Hoes, R. M.; Robbat, A,. Jr.; White, C.M. Anal. Chem. 1984. 56. 2697. (17) Robbat, A.,-Jr.; Corso, N. P.; Doherty, P. J.; Wolf, M. H. Anal. Chem. 1986, 58, 2078.
2688
Anal. Chem. 1990, 62, 2688-2691
(18) Robbat, A., Jr.; Xyrafas, G.; Marshall, D. Anal. Chem. 1888, 60, 982. (19) Hale, H. D.; Hileman. F. D.; Mazer, T.; Shell, T. L.; Noble, R. W.; Rook, J. J. Anal. Chem. 1985, 57, 640. (20)Sissons, D.; Wetti, D. J . Chromatogr. 1971, 6 0 , 15-32. (21) Devillers, J. Fresinius' 2.Anal. Chem. 1988, 332,61-62. (22) Hasan, M. N.; Jurs, P. C. Anal. Chem. 1988. 6 0 , 978-982. (23) Randic, M. J . Am. Chem. SOC. 1975, 97, 6609.
(24) Kier, L. E.; Hall, L. H. Molecular ConnecfMty in Chemisfry and Drug Research; Academic Press: New York, 1976. (25) Buser, H. R. Environ. Sci. Technol. 1988, 20,404-408.
RECEIVED for review July 3, 1990. Accepted September 5, 1990.
Determination of Monosaccharides in Cellulosic Hydrolyzates Using Immobilized Pyranose Oxidase in a Continuous Amperometric Analyzer Lisbeth Olsson and Carl Fredrik Mandenius*J Department of Pure and Applied Biochemistry, Chemical Center, University of Lund, Box 124, S-221 00 Lund, Sweden
Jindrich Volc Institute of Microbiology, Czechoslovak Academy of Sciences, Prague 4, Krc, Czechoslovakia
The pyranoses, glucose, xylose, and galactose, were determined in a flow hjectlon syatem using an knmobilred enzyme reactor wlth pyranose oxMase (EC 1.1.3.10). Oxygen consumed by the oxidation was measured wRh an amperometric electrode (Clark type). The electrode response, after partial transfer of the sample through a dialysls membrane, was linear betw6en 0.6-30 mM glucose, 1.0-50 mM xylose, and 2.0-100 mM galactose with an accuracy of f7.0%. The hrfluence of the matrix of a W e medium, spent sumte liquor, a pyranose-contrrhrlngbyproduct from the pulp industry, was investlgatted and found negligible with respect to sensitivity and stabMty, at least up to 2000 measurement cycles of the analytical system. Application of the system to continuous monitoring of ethanollc fermentation was also demonstrated.
INTRODUCTION In view of the constantly expressed need for environmentally safe energy conversion processes, analytical methods for the determination and monitoring of analytes, notably metabolites, in such processes are of the utmost importance for their operation. Conversion of lignocellulose into a fermentable monosaccharide hydrolyzate requires continuous determination in order to evaluate the quality, maximal yield, and process condition for a subsequent fermentation ( I , 2). Biosensors, e.g. enzyme electrodes and enzyme thermistors, provide the means for such analysis using specific enzymes (3, 4 ) . Glucose for example, can be determined by using glucose oxidase (EC 1.1.3.4),which has high specificity versus other metabolites (5). Other monosaccharides in lignocellulosic wastes, e.g. xylose, galactose, and mannose, usually require whole cells or enzymes or enzyme sequences that are cofactor dependent or are not stable enough for long-term usage (6-8). This paper describes the analytical use of an enzyme exhibiting specificity for three monosaccharides abundant in lignocellulose (glucose,galactose, and xylose), pyranose oxidase (EC 1.1.3.10)from the fungus Phanerocheate chrysosporium Present address: Kabi Peptide Hormones, Research and De-
velopment, s-11287 Stockholm, Sweden.
(9, 10). The enzyme, which is also referred to as glucose 2-oxidase, oxidizes the hydroxyl group at the C-2 position of the pyranose ring of hexoses and pentoses, thus differing from the extensively employed glucose oxidase which oxidizes at the C-1 position. Immobilized pyranose oxidase (PROD) has recently been shown by us to exhibit high operational stability thereby making it suitable for use in on-line analytical systems, for example FIA systems (11). The on-line analyzer used in this paper is operated with immobilized PROD contained in a reactor which is followed by an amperometric electrode for oxygen determination. A relative measure of the total pyranose concentration in a sample of several analytes in a crude process liquid or an absolute value of individual monosaccharides in a sample without competing analytes could be determined. Stability and reproducibility are evaluated and arguments for process analysis considered. EXPERIMENTAL SECTION Enzyme Purification. PROD was purified from the basidomycetes P. chrysosporium,Strain K-3, according to a procedure previously described (IO,11). An inoculum of the organism was cultivated in shake flasks for 11 days on a glucose-corn steep medium. After harvesting and disruption of the cells, the resulting homogenate was centrifuged and supplemented with ammonium sulfate to increase the ionic strength of the solution. PROD was then purified by hydrophobic chromatography, anion-exchange chromatography, and gel filtration. Substantially pure PROD was obtained with a specific activity of 12 units/mg protein. Enzyme Immobilization. The purified PROD was immobilized onto controlled pore glass (CPG) with a mean pore diameter of 2215 A and a mesh size of 120/200 (CPG-10-2000 from Electro-Nucleonics,Inc.) by using the glutardialdehydeactivation method (12). The CPG was first boiled in 5% HN03for 45 min and thereafter extensively washed with distilled water. To a solution of 2 g of (y-aminopropy1)triethoxysilane(Sigma Chemical Co.) in 18 mL of water was added 1 g of CPG. The pH was adjusted to 3.5 and the solution heated to 75 "C for 3 h while gently being stirred. The CPG was washed and dried for 4 h at 115 "C. Activation with glutardialdehyde was performed by adding 25 mL of 2.5% glutardialdehyde (grade 11, Sigma Chemical Co.) in 0.1 M sodium phosphate buffer, pH 7.0. The reaction was carried out under reduced pressure for 30 min followed by 30 min at normal pressure. After the activated glass was washed with distilled water, the enzyme was coupled in 50 mM phosphate
0003-2700/90/0362-2688$02.50/0 0 1990 American Chemical Society
