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Evaluation of computer-assisted second-derivative ultraviolet spectrophotometry for analysis of binary mixtures
Analytica Chimica Acta, 223 (1989) 395-402 Elsevier Science Publishers B.V., Amsterdam -
395 Printed in The Netherlands
EVALUATION OF COMPUTER-ASSISTED SECOND-DERIVATIVE ULTRAVIOLET SPECTROPHOTOMETRY FOR ANALYSIS OF BINARY MIXTURES
F. DELAYE DPpartement de Physique, Facultt des Sciences, Universit6 de Dakar, Dakar (Senegal) M.D. GAYE and J.J. AARON* Znstitut de Topologie et de Dynamique des SystBmes, associ6 au C.N.R.S., Universitk Paris VZZ, 1, rue Guy de la Brosse, 75005 Paris (France) (Received 13th October 1988)
SUMMARY Three second-derivative spectra identification (DESPI) programs have been developed. The DESPI-1 program is used for the computerized recognition of the spectral components in known binary mixtures. The DESPI-2 and DESPI-3 programs are applied to the automatic identification of unknown binary mixtures. The performances of these computer-assisted procedures are evaluated and compared for nineteen single compounds (purines and pyrimidines) and twelve binary mixtures.
Many ultraviolet visible spectrophotometers can be combined with microcomputers to provide data acquisition, storage and treatment. As a result, second- and higher-order derivative spectra can now be obtained very easily. The usefulness and limitations of derivative spectrophotometry and the possibility of deconvoluting complicated derivative spectra by computerized resolution have been studied by Griffiths et al. [ 11. Biomedical applications of derivative spectrophotometry have been reviewed by Fell [ 21. Several other authors [ 313] have shown that derivative spectrophotometry is useful for the identification and quantitation of mixtures of organic compounds. In most of these studies, procedures for identifying and quantifying the analytes in mixtures were relatively time-consuming because they were based on visual comparison of the main features of the derivative spectra with those of single compounds. Here, three computer programs are described for resolving second-derivative spectra and elucidating the components of binary mixtures. Computer-assisted identification is applied to mixtures of purines and pyrimidines and compared with a previous method of identification [ 131.
0003-2670/89/$03.50
0 1989 Elsevier Science Publishers B.V.
EXPERIMENTAL
Instrumentation A Beckman model 3600 recording spectrophotometer equipped with a derivative accessory and a Varian model DMS-200 spectrophotometer were used to obtain second-derivative spectra d2A/U2 =f(L). The spectral parameters were set at the same values as previously [ 13,141. An Apple-2 microcomputer was used for data storage and treatment. Procedure Second-derivative absorption spectra (200-400 nm) of single components and mixtures were obtained at room temperature [ 13,141 and stored, with 2nm intervals, on disk. The solutions used were prepared in demineralized water. Three derivative spectra identification (DESPI) programs were designed. The DESPI-1 program was used for identifying the characteristic bands (maxima, minima, shoulders) of the second-derivative spectra of standard binary mixtures. The spectral features were compared to those of the known single compounds which were stored in the microcomputer. Spectral recognition was considered to be acceptable when (d2A/cU2)A values (A is the absorbance) corresponding to mixtures and single compounds were practically identical at a characteristic wavelength (A), within a defined tolerance area. This area was
Fig. 1. Comparison of stored and experimental second-derivative UV spectra of an adenine/cytosine (1: 9) mixture: (- - - ) output derivative spectrum obtained by using the DESPI-1 program; (----) experimental spectrum. The dotted lines indicate the tolerance area.
397
defined as the amount of tolerable deviation from the (d2A/&2)A values, visualized as the surface surrounding the spectral profile symmetrically and containing these characteristic (d2A/dJ,2)n values. The DESPI-2 program was designed to list all single compounds which are predicted to be present in the mixtures, taking into account the agreement between the (d2A/a2), values of the mixtures and of those of the data bank, in a preset wavelength range. Decreasing tolerance areas were fixed, in order to narrow the number of possible compounds and, therefore, to lead to correct recognition of the mixture composition. A similar procedure was utilized for the DESPI-3 program, in which the possible components of the mixture were classified according to the frequency of appearance of the characteristic (d2A/U2)1 values, in the preset wavelength range. All three DESPI programs must be applied only with stored reference second-derivative spectral data generated by the same spectrophotometer under instrumental conditions (slitwidth, sensitivity, scan speed) similar to those utilized for identifying unknown mixtures. The programs are available on request to the authors. TABLE 1 Comparison of computer-assisted binary mixtures Mixture”
Adenine (A)/cytosine (C) (1:9) Adenine (A)/cytosine (C) (1:l) Adenine (A) /purine (P) (1:9) Adenine (A)/purine (P) (1:l) Cytosine (C)/thymine (T) (1:l) Cuanine (G ) /6-iodopurine (IP)(l:l) Thymine (T) /uracil (U) (1:l) Thymine (T)/6-methylpurine (MP) (1: 1)
and manual recognition methods of the second-derivative
Identification
spectra of selected
of wavelengthsb (nm)
Computer-assisted’
Manuald
(284)C, 266 A+C, 254 A, 246 C, 216 272 A, 268 A+C, 256 A, -216 A
(285)C, (283)A, (276)A, 267 A+C, 260,gc,G A, 208,203 C 282 A+C, 272 A, 266 A+C, 258 A, 215A, 206A (281)A, GP, (275) -F_ 271 A, 265 A, 260A+P,xP,230, (211)P,gP, 206 P (282)A. (279)A+P, =A, 267 A, 260 A, 216x 286 T, 283 C, 267 C+T, 248 C, -228 T %C,%T,2&3C %%IP, 281 IP, 273 IP, 266 IP, 260 IP, 257 IP, 243 G, 218 IP, 216 IP, 210 IP, 208 IP, 202 IP’ %T&Q)U,~T+U, (209) T +u, \___,- c
gP,272P,26OP,gP
280A,aA,266A,mA 286 T, 266 T, 250 C, 240 T, 228 T, 212 C. 210 T 292 IP, 274 IP, 216 IP, 210 IP, -ZIP 282T+U, (E)U,224T+U, (208)U 288 MP, 286 MP, 282 MP, 276 MP, (264)MP,256 MP,!& T+MP, -226 MP, -210 MP
(288)T, (286)T, (282) T, 275 MP, (264)T+s, 258=, 246 MP, 225 T, 200 MP’ -210 MP, (208)MP, -
“Concentrations were between 1 x 10e5 and 9 X 10-s M. bWavelengths of maxima are underlined; other wavelengths correspond to minima; shoulders are given in parenthesis. cCharacteristic wavelengths were identified by the DESPI-1 program with variable tolerance areas (see text for explanation). ‘Values taken from [ 131 unless otherwise mentioned. ‘This work.
398
TABLE 2 Effect of tolerance area (TA, expressed in relative unite) on the identification of binary mixtures with the DESPI-2 program Mixture
Range” (nm)
TA
Number of compounds
Adenine/cytosine (1:9)
200-300
Adenine/cytosine (1:l)
200-300
1.5 1.2 1.0 0.9 0.8 1 0.8 0.75 0.7 1.0 0.5 0.4 1.0 0.5 0.3 0.9 0.7-0.5 0.5 0.4 0.5 0.4 0.3 0.7 0.4 0.3 0.2 1.7 1.5 1.2 l-0.5 0.7 0.65 0.63 0.6 1 0.8 0.5 1 0.7 0.6-0.5 1 0.5 1 0.6
7 4 1 1 1 7 3 2 1 7 2 2 17 4 2 6 1 3 2 4 3 1 15 5 3 2 10 8 4 1 4 3 2 1 9 4 2 8 2 1 8 1 9 1
Adenine /purine (1:9)
200-250
250-300
Adenine/purine (1:l)
200-300 200-250 250-300
Cytosinefthymine (9:l)
250-300
Cytosine/thymine (1:l)
200-300
Theobromine/theophylline (1:9)
200-300
Theobromine/theophylline (1:l) Guanine/G-iodopurine (1:9)
200-300
200-310
200-260 260-310
Identificationb
f3 , 4 t 5 96 98918 _I
2,596 216 2 3,5,11,X?, 15,17,19 lJ, 17 l2,17 1,2,3-8,lO,ll,lJ 13,14,16-19 2, ll,l2,17 l2,17 2,3-6,18 :56 -9 9 296 2,11,l2,17 j,l2,17 f-8 lo-12 ,_,_* 13 16 17t 19 4,5: 6,11, s 495, Is 5,16 1,3,9, 10,12,l3,14, Is, 17,19 1,3,9, 10,12,14,16,17 3,9,10, s
Is ‘4 4,5,6 $56
495 4 ,,_,_,, 3 4 5 6 11P15918t 19
2, ffi,596
4,s
3,4,5,$11,12,17,19. 3919
399 TABLE 2 (continued) Mixture
Range” (nm)
TA
Guanine/G-iodopurine (1:l) Thymine/uracil (1:l)
200-320
1 0.7-0.5 1 0.8 0.7 0.6-0.5 1.0 0.5 0.45
4 1 3 3 2 1 7 2 2
0.4 0.5 0.45 0.4 0.4 0.35 0.3
1 2 2 1 4 3 3
II ll,lJ 11,lJ
0.2
1
II.
Thymine/G-methylpurine (1:l)
200-300
200-300
200-250
250-300
Number of compounds
Identificationb
3,% 16,19 f *_1_ 13 18 613918 8913 F4 5 11,12 9-9 17 19 l;,b 11,lJ
: 12 16 1_17 11: 12: u l&12,17
“Preset wavelength range in which the program was applied. bCompounds are numbered as follows:. (1) 5-iodouracil(2) adenine; (3) hypoxanthine; (4) theobromine; (5) theophylline; (6) caffeine; (7) 5-bromouracil; (8) 5-fluorouracil; (9) 6-iodopurine; (10) 5-mercaptouracil; (11) 6-chloropurine; (12) purine; (13) thymine; (14) uric acid; (15) 5aminouracil; (16) cytosine; (17) 6methylpurine; (18) uracil; (19) guanine. The number corresponding to a compound really present in the mixture is underlined. RESULTS AND DISCUSSION
The evaluation of the spectral identification procedures was based on a spectral data bank consisting of the second-derivative spectral characteristics of nineteen single compounds (purines and pyrimidines) and twelve of their binary mixtures. Second-derivative spectra of mixtures stored in the data bank could be visualized and compared with experimental spectra (Fig. 1) . Computer-assisted recognition of the spectral components of known binary mixtures
The DESPI-1 program was used for automatic elucidation of the origin of the different spectral features (peaks, troughs and shoulders) of the secondderivative spectra of standard (known) binary mixtures. This program allowed derivative spectral features to be recognized by comparing them with the stored spectral data of individual compounds. For all mixtures of the purines and pyrimidines studied, most components of the derivative spectra were attributed satisfactorily (Table 1). The computer-assisted identification of the wavelengths of the characteristic peaks (and troughs) was essentially identical
400 TABLE 3 Effect of tolerance area (TA, expressed in relative unite) on the identification of binary mixtures with the DESPI-3 program Mixture
Range” (nm)
TA
Adenine/cytosine (1:9)
200-300
1.0
Adeninejcytosine (1:l)
200-300
Adenine/purine (1:9)
‘200-300
Adenine/purine (1:l)
250-270 270-290 200-300
Cytosinelthymine (1:l)
200-300
Cytosine/thymine (9: 1)
200-300
Theobromine/theophylline (1:l)
200-400
Theobromine/theophylline (1:9)
200-300
Guanine/G-iodopurine (1:9)
200-300
Identification (frequency ) b*c
16(51), 9(50), 3(49),4(49), 5(49), 10(49), 12(49), 19(49),2(48),6(48),8(48),11(48), 17(48), 18(48) 0.5 X(47), 2(46), 5(46) 0.4 %(46), 5(42),6(42),2(41), 4(41) 0.35 16(46), 2(41) 1.0 ~(51),3(51),4(51),5(51),6(51),18(51),8(51), 9(49), 11(49), 13(49),NJ48), 12(48), 17(48), 19(48), 7(48). 0.5 2(50), 4(46), 5(47), 6(48),16(44), 17(44) 0.4 2(49),5(44),6(44),16(43),12(43), 11(43), 3(43). l2(51),3(51),5(51), 11(51), 17(51), 19(51), 1.0 4(50), 14(50), 16(50), 10(49), 2(48), 6(48), l2(51), 17(51), 11(49),2(45) 0.5 17(49), l2(45), 11(41),2(38) 0.2 g(33), 11(33), 17(32),2(23) 0.1 0.2 12(10), 17(10),2(9) 0.2 12(11), 17(11),2(10),3(10) 0.5 2(51),5(48),6(46),4(45),11(45),17(45),3(44), 12(43) 0.2 2(48), 12(38) 0.1 2(36), 12(28) l6(51), 10(5O),l3(49), 19(49),9(49), 5(49), 1.0 3(49), 4(49) 0.7-0.516(51),13(48) 1.0 fi(50), 10(50), 12(50), 17(50), 3(49), 9(49), 19(49), 4(48), 5(48), 15(48), 2(47), 6(47), 11(47), 14(47),13(46) fi(50), 10(49), 12(48), 4(46), 6(45), 7(45), 0.7 17(45), 11(45), 5(44), 17(44), 2(43), 3(43), ~(42) 0.5 l6(50), 10(49), 4(46), 12(46), 17(44), 5(43), 6(43),11(43),2(41),3(4O),l3(36) 4(101),5(101) 0.5 0.2 4(50), 5(49) 4(51), 5(50), 6(50) 0.5 4(49),5(48) 0.2 4(46),5(35) 0.1 9(56),19(56), 3(56), 4(56), 5(56), 1.0 0.7 19(56), 3(56),9(54) 0.4 19(53), 9(47) 0.2 19(41), 9(33)
401 TABLE 3 (continued) Mixture
Range” (nm)
TA
Identification (frequency) b*c
Guanine/G-iodopurine (1:l)
200-300
Thymineluracil (1:l)
200-300
Thymine/G-methylpurine (1:l)
200-300
1.0 0.5 0.4 0.3 1.0 0.7 0.5 0.3 0.6
19(61),9(61), 16(61),3(61) 9(61), 6(57),19(56) 9(60), 6(55),19(46) 9(59), ~(42) 13(51),18(51),8(51) 13(51),8(51),18(50) 13(51), 18(49) 18(43),13(42) l7(51), 5(51), 11(51),5(49), 12(49), 16(47), 2(46), 3(46), 10(46),6(44),l3(42) lJ(51), 11(51), 12(49),4(46),5(44),2(44), 6(43), 10(45), 16(47),3(39) 17(50), 11(50), 12(47),4(45),5(43),3(33)
0.5 0.4
abSee footnotes to Table 2. ‘The numbers in parentheses correspond to the frequencies of appearance of the characteristic (d2A/cU2)1 values in the tolerance region.
to that obtained manually [ 131 for most mixtures. Various tolerance areas were selected in order to improve the accuracy of the computer-assisted identification. Evaluation of the DESPI-2 program The performance of the DESPI-2 program for identifying unknown binary mixtures was evaluated by investigating the effect of varying the tolerance areas on the number of possible compounds ascribed to these mixtures, and on the accuracy of their attribution (Table 2). Several preset wavelength ranges were also chosen. For a fixed wavelength range, a decrease in the tolerance area reduces the number of compounds predicted to belong to the mixtures, and improves the accuracy of identification. However, in several cases, only one compound could be identified with certainty in a binary mixture, even when low tolerance areas were used. For example, in adenine/cytosine (1: 9) and cytosine/thymine ( 1: 1) and (9 : 1) mixtures, only cytosine was detected, while in adenine/cytosine (1: 1) and adenine/purine (1: 1) mixtures, only adenine was identified. In contrast, theobromine and theophylline were detected in ( 1: 1) and ( 1: 9) mixtures of these compounds (Table 2 ) . Evaluation of the DESPI-3program Table 3 shows the effect of varying the tolerance area on the identification of unknown binary mixtures, with the DESPI-3 program. In this procedure, the possible compounds were classified according to the frequency of appearance of their characteristic (d2A/cU2) values in the tolerance region. Decreased
402
tolerance areas significantly improved the identification of mixtures. Indeed, in most cases, when low tolerance areas were chosen, the highest frequency of appearance was obtained for the real components of mixtures, and the identification procedure was satisfactory. For instance, adenine/cytosine ( 1: 1 ), adenine/purine ( 1: 1) , cytosine/thymine ( 1: 1) , theobromine/theophylline (1: 1 and 1:9 ) , guanine/6-iodopurine (1: 1 and 1: 9)) and thymine/uracil ( 1: 1) mixtures were identified unequivocally. In contrast, in adenine/purine (1: 9) and cytosine/thymine (9 : 1) mixtures, only the more abundant compound could be identified (Table 3). The DESPI-3 program is more accurate than the DESPI-2 program for identification purposes of mixtures. Conclusion
The application of computer-assisted retrieval techniques to second-derivative spectral data is extremely useful and easy for identifying the components of binary mixtures with good accuracy. Relatively simple programs are efficient in most cases, in spite of the similarity of the second-derivative spectra of the selected compounds. More sophisticated programs and larger spectral data banks should improve the reliability of derivative spectrophotometry for the identification of unknown mixtures.
REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14
T.R. Griffiths, K. King, H.V.St.A. Hubbard, M.-J. Schwing-Weill and J. Meulemeestre, Anal. Chim. Acta, 143 (1982) 163. A.F. Fell, Trends Anal. Chem., 2 (1983) 63. T.C. O’Haver and G.L. Green, Anal. Chem., 48 (1976) 312. G. Talsky, L. Mayring and H. Kreuzer, Angew. Chem., Int. Ed. Engl., 17 (1978) 785. B. Morelli, Analyst, 107 (1982) 282; 108 (1983) 870,1506; Anal. L&t., 21 (1988) 43. M. Gil, N. Iza and J. Morcillo, Anal. Quim. Ser. A., 79 (1983) 473. M.A. Korany, A.M. Wahbi, M.A. Elsayed and S. Mandour, Anal. Lett., 17 (1984) 1373. A.H. Lawrence and J. Kovar, Anal. Chem., 56 (1984) 1731. K. Kitamura and R. Majima, Anal. Chem., 55 (1983) 54. Y.R. Tahboub and H.L. Pardue, Anal. Chem., 57 (1985) 38. M.E. Mohamed, Anal. Lett., 19 (1986) 1323. M.C. Ortega, A. Reyes, M. Morel1 and J.J. Laserna, Anal. Lett., 19 (1986) 1097. J.J. Aaron and M.D. Gaye, TaIanta, 35 (1988) 513. J.J. Aaron and D. Gningue, Analusis, 13 (1985) 426.
395 Printed in The Netherlands
EVALUATION OF COMPUTER-ASSISTED SECOND-DERIVATIVE ULTRAVIOLET SPECTROPHOTOMETRY FOR ANALYSIS OF BINARY MIXTURES
F. DELAYE DPpartement de Physique, Facultt des Sciences, Universit6 de Dakar, Dakar (Senegal) M.D. GAYE and J.J. AARON* Znstitut de Topologie et de Dynamique des SystBmes, associ6 au C.N.R.S., Universitk Paris VZZ, 1, rue Guy de la Brosse, 75005 Paris (France) (Received 13th October 1988)
SUMMARY Three second-derivative spectra identification (DESPI) programs have been developed. The DESPI-1 program is used for the computerized recognition of the spectral components in known binary mixtures. The DESPI-2 and DESPI-3 programs are applied to the automatic identification of unknown binary mixtures. The performances of these computer-assisted procedures are evaluated and compared for nineteen single compounds (purines and pyrimidines) and twelve binary mixtures.
Many ultraviolet visible spectrophotometers can be combined with microcomputers to provide data acquisition, storage and treatment. As a result, second- and higher-order derivative spectra can now be obtained very easily. The usefulness and limitations of derivative spectrophotometry and the possibility of deconvoluting complicated derivative spectra by computerized resolution have been studied by Griffiths et al. [ 11. Biomedical applications of derivative spectrophotometry have been reviewed by Fell [ 21. Several other authors [ 313] have shown that derivative spectrophotometry is useful for the identification and quantitation of mixtures of organic compounds. In most of these studies, procedures for identifying and quantifying the analytes in mixtures were relatively time-consuming because they were based on visual comparison of the main features of the derivative spectra with those of single compounds. Here, three computer programs are described for resolving second-derivative spectra and elucidating the components of binary mixtures. Computer-assisted identification is applied to mixtures of purines and pyrimidines and compared with a previous method of identification [ 131.
0003-2670/89/$03.50
0 1989 Elsevier Science Publishers B.V.
EXPERIMENTAL
Instrumentation A Beckman model 3600 recording spectrophotometer equipped with a derivative accessory and a Varian model DMS-200 spectrophotometer were used to obtain second-derivative spectra d2A/U2 =f(L). The spectral parameters were set at the same values as previously [ 13,141. An Apple-2 microcomputer was used for data storage and treatment. Procedure Second-derivative absorption spectra (200-400 nm) of single components and mixtures were obtained at room temperature [ 13,141 and stored, with 2nm intervals, on disk. The solutions used were prepared in demineralized water. Three derivative spectra identification (DESPI) programs were designed. The DESPI-1 program was used for identifying the characteristic bands (maxima, minima, shoulders) of the second-derivative spectra of standard binary mixtures. The spectral features were compared to those of the known single compounds which were stored in the microcomputer. Spectral recognition was considered to be acceptable when (d2A/cU2)A values (A is the absorbance) corresponding to mixtures and single compounds were practically identical at a characteristic wavelength (A), within a defined tolerance area. This area was
Fig. 1. Comparison of stored and experimental second-derivative UV spectra of an adenine/cytosine (1: 9) mixture: (- - - ) output derivative spectrum obtained by using the DESPI-1 program; (----) experimental spectrum. The dotted lines indicate the tolerance area.
397
defined as the amount of tolerable deviation from the (d2A/&2)A values, visualized as the surface surrounding the spectral profile symmetrically and containing these characteristic (d2A/dJ,2)n values. The DESPI-2 program was designed to list all single compounds which are predicted to be present in the mixtures, taking into account the agreement between the (d2A/a2), values of the mixtures and of those of the data bank, in a preset wavelength range. Decreasing tolerance areas were fixed, in order to narrow the number of possible compounds and, therefore, to lead to correct recognition of the mixture composition. A similar procedure was utilized for the DESPI-3 program, in which the possible components of the mixture were classified according to the frequency of appearance of the characteristic (d2A/U2)1 values, in the preset wavelength range. All three DESPI programs must be applied only with stored reference second-derivative spectral data generated by the same spectrophotometer under instrumental conditions (slitwidth, sensitivity, scan speed) similar to those utilized for identifying unknown mixtures. The programs are available on request to the authors. TABLE 1 Comparison of computer-assisted binary mixtures Mixture”
Adenine (A)/cytosine (C) (1:9) Adenine (A)/cytosine (C) (1:l) Adenine (A) /purine (P) (1:9) Adenine (A)/purine (P) (1:l) Cytosine (C)/thymine (T) (1:l) Cuanine (G ) /6-iodopurine (IP)(l:l) Thymine (T) /uracil (U) (1:l) Thymine (T)/6-methylpurine (MP) (1: 1)
and manual recognition methods of the second-derivative
Identification
spectra of selected
of wavelengthsb (nm)
Computer-assisted’
Manuald
(284)C, 266 A+C, 254 A, 246 C, 216 272 A, 268 A+C, 256 A, -216 A
(285)C, (283)A, (276)A, 267 A+C, 260,gc,G A, 208,203 C 282 A+C, 272 A, 266 A+C, 258 A, 215A, 206A (281)A, GP, (275) -F_ 271 A, 265 A, 260A+P,xP,230, (211)P,gP, 206 P (282)A. (279)A+P, =A, 267 A, 260 A, 216x 286 T, 283 C, 267 C+T, 248 C, -228 T %C,%T,2&3C %%IP, 281 IP, 273 IP, 266 IP, 260 IP, 257 IP, 243 G, 218 IP, 216 IP, 210 IP, 208 IP, 202 IP’ %T&Q)U,~T+U, (209) T +u, \___,- c
gP,272P,26OP,gP
280A,aA,266A,mA 286 T, 266 T, 250 C, 240 T, 228 T, 212 C. 210 T 292 IP, 274 IP, 216 IP, 210 IP, -ZIP 282T+U, (E)U,224T+U, (208)U 288 MP, 286 MP, 282 MP, 276 MP, (264)MP,256 MP,!& T+MP, -226 MP, -210 MP
(288)T, (286)T, (282) T, 275 MP, (264)T+s, 258=, 246 MP, 225 T, 200 MP’ -210 MP, (208)MP, -
“Concentrations were between 1 x 10e5 and 9 X 10-s M. bWavelengths of maxima are underlined; other wavelengths correspond to minima; shoulders are given in parenthesis. cCharacteristic wavelengths were identified by the DESPI-1 program with variable tolerance areas (see text for explanation). ‘Values taken from [ 131 unless otherwise mentioned. ‘This work.
398
TABLE 2 Effect of tolerance area (TA, expressed in relative unite) on the identification of binary mixtures with the DESPI-2 program Mixture
Range” (nm)
TA
Number of compounds
Adenine/cytosine (1:9)
200-300
Adenine/cytosine (1:l)
200-300
1.5 1.2 1.0 0.9 0.8 1 0.8 0.75 0.7 1.0 0.5 0.4 1.0 0.5 0.3 0.9 0.7-0.5 0.5 0.4 0.5 0.4 0.3 0.7 0.4 0.3 0.2 1.7 1.5 1.2 l-0.5 0.7 0.65 0.63 0.6 1 0.8 0.5 1 0.7 0.6-0.5 1 0.5 1 0.6
7 4 1 1 1 7 3 2 1 7 2 2 17 4 2 6 1 3 2 4 3 1 15 5 3 2 10 8 4 1 4 3 2 1 9 4 2 8 2 1 8 1 9 1
Adenine /purine (1:9)
200-250
250-300
Adenine/purine (1:l)
200-300 200-250 250-300
Cytosinefthymine (9:l)
250-300
Cytosine/thymine (1:l)
200-300
Theobromine/theophylline (1:9)
200-300
Theobromine/theophylline (1:l) Guanine/G-iodopurine (1:9)
200-300
200-310
200-260 260-310
Identificationb
f3 , 4 t 5 96 98918 _I
2,596 216 2 3,5,11,X?, 15,17,19 lJ, 17 l2,17 1,2,3-8,lO,ll,lJ 13,14,16-19 2, ll,l2,17 l2,17 2,3-6,18 :56 -9 9 296 2,11,l2,17 j,l2,17 f-8 lo-12 ,_,_* 13 16 17t 19 4,5: 6,11, s 495, Is 5,16 1,3,9, 10,12,l3,14, Is, 17,19 1,3,9, 10,12,14,16,17 3,9,10, s
Is ‘4 4,5,6 $56
495 4 ,,_,_,, 3 4 5 6 11P15918t 19
2, ffi,596
4,s
3,4,5,$11,12,17,19. 3919
399 TABLE 2 (continued) Mixture
Range” (nm)
TA
Guanine/G-iodopurine (1:l) Thymine/uracil (1:l)
200-320
1 0.7-0.5 1 0.8 0.7 0.6-0.5 1.0 0.5 0.45
4 1 3 3 2 1 7 2 2
0.4 0.5 0.45 0.4 0.4 0.35 0.3
1 2 2 1 4 3 3
II ll,lJ 11,lJ
0.2
1
II.
Thymine/G-methylpurine (1:l)
200-300
200-300
200-250
250-300
Number of compounds
Identificationb
3,% 16,19 f *_1_ 13 18 613918 8913 F4 5 11,12 9-9 17 19 l;,b 11,lJ
: 12 16 1_17 11: 12: u l&12,17
“Preset wavelength range in which the program was applied. bCompounds are numbered as follows:. (1) 5-iodouracil(2) adenine; (3) hypoxanthine; (4) theobromine; (5) theophylline; (6) caffeine; (7) 5-bromouracil; (8) 5-fluorouracil; (9) 6-iodopurine; (10) 5-mercaptouracil; (11) 6-chloropurine; (12) purine; (13) thymine; (14) uric acid; (15) 5aminouracil; (16) cytosine; (17) 6methylpurine; (18) uracil; (19) guanine. The number corresponding to a compound really present in the mixture is underlined. RESULTS AND DISCUSSION
The evaluation of the spectral identification procedures was based on a spectral data bank consisting of the second-derivative spectral characteristics of nineteen single compounds (purines and pyrimidines) and twelve of their binary mixtures. Second-derivative spectra of mixtures stored in the data bank could be visualized and compared with experimental spectra (Fig. 1) . Computer-assisted recognition of the spectral components of known binary mixtures
The DESPI-1 program was used for automatic elucidation of the origin of the different spectral features (peaks, troughs and shoulders) of the secondderivative spectra of standard (known) binary mixtures. This program allowed derivative spectral features to be recognized by comparing them with the stored spectral data of individual compounds. For all mixtures of the purines and pyrimidines studied, most components of the derivative spectra were attributed satisfactorily (Table 1). The computer-assisted identification of the wavelengths of the characteristic peaks (and troughs) was essentially identical
400 TABLE 3 Effect of tolerance area (TA, expressed in relative unite) on the identification of binary mixtures with the DESPI-3 program Mixture
Range” (nm)
TA
Adenine/cytosine (1:9)
200-300
1.0
Adeninejcytosine (1:l)
200-300
Adenine/purine (1:9)
‘200-300
Adenine/purine (1:l)
250-270 270-290 200-300
Cytosinelthymine (1:l)
200-300
Cytosine/thymine (9: 1)
200-300
Theobromine/theophylline (1:l)
200-400
Theobromine/theophylline (1:9)
200-300
Guanine/G-iodopurine (1:9)
200-300
Identification (frequency ) b*c
16(51), 9(50), 3(49),4(49), 5(49), 10(49), 12(49), 19(49),2(48),6(48),8(48),11(48), 17(48), 18(48) 0.5 X(47), 2(46), 5(46) 0.4 %(46), 5(42),6(42),2(41), 4(41) 0.35 16(46), 2(41) 1.0 ~(51),3(51),4(51),5(51),6(51),18(51),8(51), 9(49), 11(49), 13(49),NJ48), 12(48), 17(48), 19(48), 7(48). 0.5 2(50), 4(46), 5(47), 6(48),16(44), 17(44) 0.4 2(49),5(44),6(44),16(43),12(43), 11(43), 3(43). l2(51),3(51),5(51), 11(51), 17(51), 19(51), 1.0 4(50), 14(50), 16(50), 10(49), 2(48), 6(48), l2(51), 17(51), 11(49),2(45) 0.5 17(49), l2(45), 11(41),2(38) 0.2 g(33), 11(33), 17(32),2(23) 0.1 0.2 12(10), 17(10),2(9) 0.2 12(11), 17(11),2(10),3(10) 0.5 2(51),5(48),6(46),4(45),11(45),17(45),3(44), 12(43) 0.2 2(48), 12(38) 0.1 2(36), 12(28) l6(51), 10(5O),l3(49), 19(49),9(49), 5(49), 1.0 3(49), 4(49) 0.7-0.516(51),13(48) 1.0 fi(50), 10(50), 12(50), 17(50), 3(49), 9(49), 19(49), 4(48), 5(48), 15(48), 2(47), 6(47), 11(47), 14(47),13(46) fi(50), 10(49), 12(48), 4(46), 6(45), 7(45), 0.7 17(45), 11(45), 5(44), 17(44), 2(43), 3(43), ~(42) 0.5 l6(50), 10(49), 4(46), 12(46), 17(44), 5(43), 6(43),11(43),2(41),3(4O),l3(36) 4(101),5(101) 0.5 0.2 4(50), 5(49) 4(51), 5(50), 6(50) 0.5 4(49),5(48) 0.2 4(46),5(35) 0.1 9(56),19(56), 3(56), 4(56), 5(56), 1.0 0.7 19(56), 3(56),9(54) 0.4 19(53), 9(47) 0.2 19(41), 9(33)
401 TABLE 3 (continued) Mixture
Range” (nm)
TA
Identification (frequency) b*c
Guanine/G-iodopurine (1:l)
200-300
Thymineluracil (1:l)
200-300
Thymine/G-methylpurine (1:l)
200-300
1.0 0.5 0.4 0.3 1.0 0.7 0.5 0.3 0.6
19(61),9(61), 16(61),3(61) 9(61), 6(57),19(56) 9(60), 6(55),19(46) 9(59), ~(42) 13(51),18(51),8(51) 13(51),8(51),18(50) 13(51), 18(49) 18(43),13(42) l7(51), 5(51), 11(51),5(49), 12(49), 16(47), 2(46), 3(46), 10(46),6(44),l3(42) lJ(51), 11(51), 12(49),4(46),5(44),2(44), 6(43), 10(45), 16(47),3(39) 17(50), 11(50), 12(47),4(45),5(43),3(33)
0.5 0.4
abSee footnotes to Table 2. ‘The numbers in parentheses correspond to the frequencies of appearance of the characteristic (d2A/cU2)1 values in the tolerance region.
to that obtained manually [ 131 for most mixtures. Various tolerance areas were selected in order to improve the accuracy of the computer-assisted identification. Evaluation of the DESPI-2 program The performance of the DESPI-2 program for identifying unknown binary mixtures was evaluated by investigating the effect of varying the tolerance areas on the number of possible compounds ascribed to these mixtures, and on the accuracy of their attribution (Table 2). Several preset wavelength ranges were also chosen. For a fixed wavelength range, a decrease in the tolerance area reduces the number of compounds predicted to belong to the mixtures, and improves the accuracy of identification. However, in several cases, only one compound could be identified with certainty in a binary mixture, even when low tolerance areas were used. For example, in adenine/cytosine (1: 9) and cytosine/thymine ( 1: 1) and (9 : 1) mixtures, only cytosine was detected, while in adenine/cytosine (1: 1) and adenine/purine (1: 1) mixtures, only adenine was identified. In contrast, theobromine and theophylline were detected in ( 1: 1) and ( 1: 9) mixtures of these compounds (Table 2 ) . Evaluation of the DESPI-3program Table 3 shows the effect of varying the tolerance area on the identification of unknown binary mixtures, with the DESPI-3 program. In this procedure, the possible compounds were classified according to the frequency of appearance of their characteristic (d2A/cU2) values in the tolerance region. Decreased
402
tolerance areas significantly improved the identification of mixtures. Indeed, in most cases, when low tolerance areas were chosen, the highest frequency of appearance was obtained for the real components of mixtures, and the identification procedure was satisfactory. For instance, adenine/cytosine ( 1: 1 ), adenine/purine ( 1: 1) , cytosine/thymine ( 1: 1) , theobromine/theophylline (1: 1 and 1:9 ) , guanine/6-iodopurine (1: 1 and 1: 9)) and thymine/uracil ( 1: 1) mixtures were identified unequivocally. In contrast, in adenine/purine (1: 9) and cytosine/thymine (9 : 1) mixtures, only the more abundant compound could be identified (Table 3). The DESPI-3 program is more accurate than the DESPI-2 program for identification purposes of mixtures. Conclusion
The application of computer-assisted retrieval techniques to second-derivative spectral data is extremely useful and easy for identifying the components of binary mixtures with good accuracy. Relatively simple programs are efficient in most cases, in spite of the similarity of the second-derivative spectra of the selected compounds. More sophisticated programs and larger spectral data banks should improve the reliability of derivative spectrophotometry for the identification of unknown mixtures.
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