ing a n extra factor in a two-level factorial design doubles (or triples if it is a three-level design) the number of experiments required; in going from four to five factors in a two-level factorial design, 16 additional experiments would be required for the initial design. Although adding more factors does increase somewhat the number of steps necessary to reach the optimum using the simplex method, this number is still very much smaller than that for a factorial design. The number of experiments necessary to reach the optimum for the study reported in this paper was 26. This includes the initial simplex, all subsequent retained vertices, all failed reflections and expansions, and all duplicate measurements. Figures 5, 6 and 7 show various ways of presenting information obtained from these 26 experiments only. Statistics has often been used to help answer, in order, three questions commonly encountered in chemical research (51): (11 Does an experimentally-measured response depend on certain factors? (2) What equation does the dependence best fit? (3) What are the optimum levels (51) R . M. Driver, Chern. Brit., 6, 154 (1970)
of the important factors? As Driver ( 5 1 ) has pointed out, "The questions are so related that it is possible for the experimenter not to know which one he wishes to answer and, in particular, many people try to answer question 2 when in fact they need the answer to the narrower question 3." When optimization is the desired goal, questions of significant factors and functional relationships are usually of interest only in the area of the optimum (7, 27). The simplex method offers a means of rapidly and efficiently finding the optimum, a f t e r which the functional relationship and then the significant factors can be determined. ACKNOWLEDGMENT The authors thank M. H. Kutner and B. A. Blumenstein of the Biometry Department and Computing Center, Emory University, for their assistance with the factorial experiments and regression analysis and T. E. Francis for his help with preliminary studies. Received for review January 18, 1974. Accepted April 10, 1974. The National Science Foundation, through Grant GP-32911, supported this work.
Applications of Artificial Intelligence to Chemistry Use of Pattern Recognition and Cluster Analysis to Determine the Pharmacological Activity of Some Organic Compounds Kenneth C. Chu Computer Systems Laboratory, Division of Computer Research and Technology, National institutes of Health, Bethesda, Md. 20074
This paper reports the use of a variety of pattern recognition techniques, such as the learning machine and the Fisher discriminant, and cluster analysis techniques, such as the nearest neighbor calculation, minimal spanning tree algorithm, shortest spanning path algorithm, hierarchical clustering algorithm, and nonlinear mapping, to classify a set of highly diversified organic molecules into their pharmacological activity of sedative and tranquilizer. The cluster analysis techniques had a prediction rate of 86 to 94% correct while the pattern recognition techniques had a rate of 85 to 86% using augmented atom fragments to represent the chemical structures.
A basic premise in drug research is that the pharmacological activity of any molecule is dependent on its structure and that structural changes may lead to changes in the activity ( l a , 2 ) . Most quantitative studies of structure-activity relationships (SAR), notably those using free energy relationships, such as the Hansch analysis ( I h ) , other physiochemical approaches (IC), and molecular orbital approaches ( I d , l e ) , correlate the structures of a (1) (a) E. J. Ariens in "Drug Design," E. J. Ariens. Ed., Academic Press, New York, N.Y., 1971, Vol. I, pp 1-270. (b) C. Hansch, ibid.. Vol. I , pp 271-342. (c) J. K. Seydel, ibid., Vol. I, pp 343-379. (d) A. J. Wohl, ibid., Vol. I, pp 381-404. (e) R. L. Schnaare. ibid., Vol. I , pp 405-449. ( 2 ) A. Burger in "Medicinal Chemistry," 3rd ed., A. Burger, Ed., WileyInterscience, New York. N.Y., 1970, part I.
set of derivatives to their biological activity. However, there are no common quantitative methods to classify and predict the pharmacological activity of a large, diverse group of organic compounds. For instance, no analytical methods exist which can classify organic compounds as a possible sedative, tranquilizer, antineoplastic agent, etc. Yet, in recent years the development of artificial intelligence (AI) techniques particularly in the fields of pattern recognition and cluster analysis may allow such methods to become a reality (3). A recent paper reported the correlation of the mass spectra of some drugs to their pharmacological activity using AI techniques ( 4 ) . Since SAR are known to exist, this paper will examine a variety of the computer-based pattern recognition (PR) and cluster analysis (CA) techniques which may achieve the above goal utilizing the substructure of the drugs. METHOD AND RESULTS The drugs examined in this study represent 30 sedatives, of which 18 are barbiturates, and 36 tranquilizers, of which 25 are phenothiazine derivatives. All are listed in Table I ( 4 ) . The non-barbituric sedatives are compounds 19-30 while the non-phenothiazine derivatives are 33, 35, 38, 42, 46, 48, 62, 63, 64, 65, and 66. Furthermore, these data were used in the correlation of mass spectra to phar(3) B. R. Kowalski and C. F. Bender, J. Arner. Chern. SOC.,94, 5632 (1972); 95, 686 (1973). ( 4 ) K. H. Ting, R. C. Lee, G. W. A. Milne, M . Shapiro, and A. M . Guarino. Science, 180, 41 7 (1973).
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Table I. List of Compounds Used in This Study Sedatives
1. Mephobarbital
2. Phenobarbital Barbital 4. Alphenal 5. Allobarbital 6. Butethal 7. Amobarbital 8. Aprobarbital 9. Pentobarbital 10. Secobarbital 11. Butalbital 12. Hexethal 13. Talbutal 14. Cyclobarbital 15. Heptabarbital 16. Probarbital 17. Hexobarbital 18. Thiamylal 19. Chloral betaine 20. Ethchlorvynol 21. Ethinamate 22. Phenaglycodol 23. Acetylcarbromal 24. Carbromal 25. Glutethimide 26. Methyprylon 27. Captodiamine 28. Methapyrilene 29. Pyrilamine 30. Tetrahydrocannabinol Tranquilizers 31. Acetophenazine 32. Carphenazine 3.
mol wt
Tranquilizers
246 232 184 244 208 212 226 210 226 238 224 240 224 236 250 198 236 254 281 144 167 214 278 236 217
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.
183
359 261 285 314 mol wt 411 425
mol wt
Meprobamate Mepazine Tybamate Methoxy promazine Trifluopromazine Oxanamide Acetyl promazine Pericyazine Perphenazine Mephenoxalone Aminopromazine Dixyrazine Methotrimeprazine Ectylurea Thioperazine Hydroxyzine Prochlorperazine Promethazine Chlorpromazine Methdilazine Thioridazide 3-Acetoxy Chlorpromazine 8-Acetoxy Chlorpromazine 3-Hydroxy-2-chlorpromazine 8-Hydroxy-2-chlorpromazine Promazine Propiomazine Triflupromazine Thiopropazate Chlorprothixene Deserpidine Diazepam Chlordiazepoxide Buclizine
218 310 274 314 407 157 326 365 403 223 327 427 328 156 446 374 373 284 318
296 370 376 376 334 334 284 340 350 445 315 578 284 299 432
Table 11. The 46 Basic Fragmentsa 1.
c = o / c=s
16. C*-C*-C*
8.
C C-N-C
I c-c-0 0
C C*-NH-C* (CYCLIC) 10. C-N-C (CYCLIC) 9.
I
19. 20. 21. 22. 23. 24. 25. 26.
CH3-C C-C* C*-C-C C=CH--C
CH3-N C-C-N
c-c-c
C 11. C*-C*H-C* 12. C*-C*-C*
(AROMATIC) (AROMATIC)
27.
H2C=C
C 13. C*-C*-C*
(AROMATIC)
28. 29.
C-CO-N C-C-C
C 14. C*-C*-C*
(AROMATIC)
I
I
I
I
c
(AROMATIC)
/ \
c
(CYCLIC) (CYCLIC)
c
0 a
Note: C * represent SP2 or SP3 carbon atoms.
1182
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(CYCYLIC)
33.
c-c-c
(CYCLIC)
34.
C-C-N
(CYCLIC)
35.
c-c/ c
36. 37.
(CYCLIC)
30. N--CO--N 31. C”-C--C*
S
15. C*-C*-C*
32. c-c-c*
N 17. CH3-0 18. C-C-C
(CYCLIC)
I
(AROMATIC)
I
2. C-0-H 3. c-0-c* 4. c*-s-c* 5. C-NH-C 6. C-NH2 7. C-N-C
0 (CYCYLIC)
\
0-CO-N c-c-c*
I
38. C*-C-N 39. c-co-c 40. CH3-N 41. c-c=c 42. C-C=N 43. C-F 44. c-CL 45. C-C-N
I c-c-0 1 C
46.
c
C
(CYCLIC) (CYCLIC) (CYCLIC)
+ (CYC)
R I
R‘LL O \ V N V O R
I1 0 Barbitals
Phenothiazines
CECH I CH3CH2CCH = CHCl I OH CH3
19
BrO 0 0 I II I1 II (C2H5)2CHCNHCNHCCH3
23 -
Figure 1. Summary of
-
-
20
Br 0
I II
21
22
0
/I
(C2H5)2CHCNHCNH2
-
25 -
24
26 -
structures used in this study
macological activity ( 4 ) . Although over half the drugs are derivatives of phenothiazines and barbiturates, there are many structures which are unrelated to these compounds, see Figure 1. The correlation of the structures of these atypical compounds to the pharmacological activity is our primary concern.
In pattern recognition and cluster analysis techniques, the first step of any analysis is to obtain computer-compatible representations (features) of the objects under study. For organic molecules, the selection of a suitable representation of the structure is extremely important. In this study, we use the atom-centered fragment or aug-
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I0l 1 N
e
C-c-C N
3
0
0
II
II
C-C-H
Ketone
Aldehyde
0 C-N-
c-c'\c-c-c-c 12 10
11 13 14 15
1. C'-N-C*(CYCLIC)
- CO - N (CYCLIC) 3. C' - N - C" (CYCLIC) 4. C - CO - N (CYCLIC) 2. N
5. C ;' C
6. N 7. 8.
c cc*
(CYCLIC)
Nitro
c=o c-c-c 11. c - c - c
- CO
C (CYCLIC)
12. CH3
-C
13. C - C - C
Ether
(CIH) C P ( i , b ) , then Z(i) > 0. In addition, if P(i,a) or P(i,b) is equal to zero, then the ith fragment occurs in only one class. To reduce the number of fragments the features with abs (Z(i)) > 0.5 were selected as candidates for the reduced space. A comparison of the two lists gave 16 common fragments. Again two feature spaces were created, F16 and F16b, analogous to F46 and F46b. Cluster Analysis us. Pattern Recognition. The processes involved in cluster analysis can be divided into several parts. First, there is the calculation of a similarity measurement between the samples, such as Euclidean distance; then there is the establishment of intersample relationships based on these measurements, such as the nearest neighbor techniques. These relationships are not based on the categories (pharmacological activity) of the samples. Finally, the samples are classified by some criteria, such as nearest neighbor. On the other hand, pattern recognition techniques utilize the categories of the samples to develop the intersam(6) D. H . Foley, / E € € Trans. lnformation Theofy. IT-18, 618 (1972). (7) R. A . F i s h e r , A n n . Eugen.. 7 , 179 (1936). (8) N. J. Nilsson "Learning Machines," McGraw-Hill, New Y o r k , N . Y . ,
3702 (1971).
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C--_O-C
II
C--C--O
9.
10.
C
c=o c=o
t
-
0
ANALYTICAL CHEMISTRY, VOL. 46, NO. 9 , A U G U S T 1974
1965.
~~
Table 111. Percentage of U n k n o w n s Classified Correctly w i t h Cluster Analysis Techniques0
F46 F46B F16
F16B 'I
KNN
NLM
MST
SSP
92% (5)
86 (9)
88 88
(8) (8)
(6)
94 (4) 91 (6) 94 (4) 89 (7)
86 (9) 86 (9)
91
88 ( 8 ) 88 ( 8 ) 88 ( 8 )
92 (5) 89 (7)
HAR
86 94 89 89
(9)
(4) (7) (7)
I
/
T T T T
i
7
.
Note: Number misclassified in parentheses.
Table IV. L i n e a r Ordering of Shortest Spanning Path Algorithm for F16=
[66, 48, 44, 61, 41, 31, 32, 37, 47, 49, 40, 34, 52, 53, 50, 59, 36, 39, 45, 58, 60, 51, 54, 56, 57, 43, 29, 28, 27, 62, 65, 64, 35, 33, 421, [22, 19, 21, 30, 20, 46, 38, 24, 23, 17, 1, 26, 25, 2, 3, 6, 7, 9, 12, 16, 14, 15, 4, 8, 10, 11, 13, 18, 5.1 o
Note: Italicized samples are misclassified.
ple relationships which allows the separation of the samples. Cluster Analysis. Five cluster analysis techniques were used to determine the feasibility of using atom-centered fragments as features. The Euclidean distance between each sample was calculated creating the distance matrix for K-nearest neighbor, nonlinear mapping, and minimal spanning tree algorithms. Each technique and the method of the classification of the compounds are discussed briefly. Table I11 reports the percentage of the compounds classified correctly as well as the number misclassified for the four data sets, F46, F46b, F16, and F16b, using each technique. K-Nearest Neighbor (KNN) (9, 1 0 ) . The identification of an unknown is determined by examining the classification of its closest neighbors. These neighbors are determined by examining the distance matrix and finding the smallest distance involving the unknown. In this study, the closest neighbor ( k = 1) determines the identification of the unknown. Each sample is treated as an unknown and classified by its nearest neighbor. Nonlinear M a p (NLM) (11, 22). A nonlinear mapping of the distance matrix is created using a heuristic relaxation method. This technique allows the mapping of an ndimensional space (for the case of the F46 data a 46-dimensional space) onto a two-dimensional representation such that the closest distances between points are preserved in an optimum way. Clusters are determined visually, and the identification of a sample is based on its membership in a defined cluster, see Figure 4. Since the following two methods involve graphs, we define some basic terminology in graph-theory. A weighted g r a p h is composed of a set of points called nodes and a set of node-pairs called edges with a number called a weight assigned to each edge. The weights correspond t o the distances between points. A p a t h in a graph is a sequence of edges joining two nodes. A circuit is a closed path. A connected graph has paths between any pair of nodes. A ,spanning p a t h of a connected graph g is a path in g which contains all nodes of g. A tree is a connected graph with no circuits. A spanning tree of a connected graph g is a tree in g which contains all nodes of g. (9) E. Fix and J. L. Hodges Jr.. Univ. of California Project 21-49-004. Sept. 4. 1951. (10) T. M . Cover and P. E. Hart, I€€€ Trans. information Theory. IT- 13, 21 (1967). (11) C. L. Chang and R. C. T. Lee, I € € € Trans. Systems, Man. and Cybernetics. SMC-3. 197 (1973) (12) J. W Sarnrnon. Jr., / E € € Trans. Computers C-18, 401 (1969).
I
/
Figure 4. N o n l i n e a r mapping for F16
Table V. R e s u l t s of M i n i m a l Spanning T r e e Algorithm for F16fk
Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster a
I: 48, 66 2: 28, 29 3: 34, 37, 40, 47, 49, 52, 53 4: 36, 39, 43, 45, 50, 58, 59, 60 5: 27, 62
6: 1-3, 6, 7, 9, 12, 14-17, 25, 26 7: 23, 24, 38 8: 31, 32, 41, 44, 61 9: 33, 35 10: 42, 63 11: 64, 65 12: 4, 5, 8, 10, 11, 13, 18 13: 19, 20, 21, 22, 30, 46 14: 51, 54, 55, 56, 57
Note Italicized samples are misclassified
The weight of a graph g is defined to be the sum of the weights of its constituent edges. Shortest Spanning P a t h (SSP) (13, 1 4 ) . A shortest ( m i n i m a l ) spanning p a t h of a graph g is a spanning path whose weight is minimum among all spanning paths of g. To form this path, the algorithm begins with an ordered list of two samples, then iteratively places a new sample on the list so that the resulting ordered list has the minimum sum of distances between the points. The classification of samples is determined by examining the ordered list for clusters. For the substructural features, two major cluster emerged, one for sedatives and one for tranquilizers. The percentage correct was determined by examining the homogeneity of the clusters. see Table IV. Minimal Spanning Tree (MST) (15).A m i n i m a l .\panning tree is a spanning tree whose weight is minimum among all spanning trees. The computer program creates this minimal spanning tree and then proceeds to create clusters by breaking edges with weights that are greater than twice the average weight and which do not isolate a single point as a cluster. Consequently, the classification of a sample is based on its membership in a defined cluster. see Table V. (13) J . R . Slagle. C. L. Chang. and S. R. Heller. Comm. ACM. submitted for publication. ( 1 4 ) S. R . Heiler. C. L. Chang. and K. C. Chu. Ana/ Chem.. 4 6 , 951 (1 974). (15) C. T . Zahn, / € E € T r a m on Computers, C-20, 68 (1970).
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Table VI. Results of Hierarchial Clustering Algorithm for F45Ba
Cluster 3: 1-18, 25, 26 Cluster 5: 23, 24 Cluster 7: 20, 21 Cluster 9: 33, 35, 38, 46 Cluster 11: 22, 30 Cluster 13: 48, 66 Cluster 15: 28, 29, 42, 63 Cluster 16: 19, 64, 65 Cluster 18: 27, 31, 32, 34, 36, 37, 39, 40, 41, 43-45, 47, 49-53, 56-62 Cluster 19: 54, 55
Feat.
Feat. a
Feat. 4350 h
l-6
Feat. 620
a
Note: Italicized samples are misclassified.
Table VII. Percentage of Unknowns Classified Correctly by Discriminant Functions F16
LMA: FRD: C o m p o u n d s Misclassified:
85 % 86 %
LMA: 27, 33, 35, 38, 42, 46, 48, 63, 64, and 65 FRD: 22, 26, 29, 35, 38, 46, 62, 63, and 66
Figure 5.
Hierarchical clustering tree
Figure 6.
Fisher discriminant for unknowns 27 and 48
Hierarchical Clustering Algorithm (HAR) (16). This algorithm creates clusters by splitting samples into two clusters which represent the presence or absence of a certain value of a certain feature. The value of the feature selected for this splitting is determined by calculating a Fisher ratio for each discrete value of each feature, where the classes to be separated represent the presence and absence of a value of the feature. Then the value of the feature with the highest Fisher ratio is used as the criteria (16) R C T Lee, / € € E Trans on System, Man and C y b e r n e t m , submitted for publication 1186
for the formation of the clusters. This process is continued on the largest resultant cluster until a prespecified number of splittings occur or until no clusters can be formed containing two or more samples. Finally, the classification of the unknowns is determined by examining the homogeneity of each cluster, see Table VI and Figure 5 . Pattern Recognition Techniques. Two types of discriminant functions were utilized in examining the data, the learning machine (LMA) and the Fisher discriminant (FRD). Table VI1 reports the results of classifying pairs of unknowns, where each set of unknowns consists of a sedative and tranquilizer taken from the original 66 drugs, using a discriminant function created from the remaining 64 compounds. To show that the classification of unknowns does not depend on the specific pair of points selected, an unknown which was misclassified remains misclassified when paired with another compound, and a compound which was classified correctly remains correctly classified when paired with another compound. Learning Machine (LMA) (8, 17, 18). The learning machine method is an error correcting procedure which attempts to create a linear decision surface (a plane in two-dimensions and a hyperplane in higher dimensional space) which can separate a set of points, called the training set, into their appropriate classes. The percentage of correctly classified samples in the training set is called the recognition rate. In this study the recognition rate was 98-100%. In order to maintain a high sample-to-feature ratio, the F16 pattern space was used (samples/features > 4). Once the linear decision surface is created, it is used to predict the classification of unknowns. The percentage of correctly identified unknowns is called the prediction rate, which is given in Table VII. Fisher Discriminant (FRD) ( 4 , 19). When two classes of points are used, the FRD method computes an optimal plane onto which n-dimensional points may be projected such that the difference in the interclass means of the projected points is maximized while the intraclass variance of the projected points is minimized. Unknowns are projected onto the FRD, and the classification is determined by examining its neighbors, see Figure 6. (17) T. L. lsenhour and P. C. Jurs.Anal. Chem., 43 (lo),20A (1971). (18) C. L. Chang. / E € € Trans. on Computers, C-20,222 (1971). (19) J. W . Sammon Jr., / € E € Trans. on Computers, C-19,126 (1970).
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DISCUSSION One of the principal objectives of this study was to evaluate the feasibility of using pattern recognition and cluster analysis techniques to correlate the chemical structure of the drugs (via the augmented atom fragments) to the pharmacological activity. In the case of sedatives and tranquilizers, the cluster analysis techniques (KNN, NLM, SSP, MST, and HAR) were able to classify 86 to 94% of the compounds correctly, while the discriminant functions (LMA and FRD) classified 84.9 to 8690 correctly. These results are very encouraging. This degree of success indicates that these techniques can be applied successfully to certain SAR problems. In addition, a n interesting result of using PR and CA techniques can be found by examining the compounds misclassified most frequently. For the cluster analysis techniques, these compounds were 27, 28, 29, 33, 35, 38, 42, 46, 63, 64, and 65, and for the discriminant functions 27, 33, 35, 38, 42, 46, 48, 63, 64, and 65. Although the compounds 33, 35, 38, 46, 48, 64, and 65 were classified by PR techniques as sedatives, they are used clinically as tranquilizers. However, Cutting indicates that these compounds gave many biological responses more similar to sedatives such as phenobarbital than to the classical tranquilizers-i. e. chlorpromazine or reserpine (20). (20) W. C. Cutting, "Handbook of Pharmacology," 5th ed., AppletonCentury-Crofts, New York, N.Y.. 1972,pp 549-562, 570-589.
One of the advantages of using augmented atoms as features is that PR and CA techniques can detect the important structural differences between the classes of compounds. Of the 16 fragments in the files F16 and F16b, ten are most common to tranquilizers and six, to sedatives. The common substructural characteristics of the tranquilizers studied are the presence of aromatic sulfides and amines, tri-substituted aliphatic amines, methyl amines, and the C-C-N grouping, while the sedatives were characterized by amides, amidines, and urea linkages, isolated double bonds, and tetra-substituted carbon atoms. This structural information may be useful for further classifications of additional sedatives and tranquilizers as well as in developing new drugs. Further work is under way to examine the application of PR and CA techniques to other pharmacological activities as well as other SAR problems.
ACKNOWLEDGMENT The author would like to thank R. C. T. Lee, C. L. Chang, K. L. Ting, and M. Shapiro for the use of their programs and their helpful and informative discussions. Received for review October 31, 1973. Accepted March 29, 1974.
Investigation of Metallic Copper-Chloride Interaction in a Hydrogen-Air Flame David F. Tomkins and C. W. Frank Department of Chemistry, University of lowa, Iowa City, lowa 52242
A probable flame mechanism for the interaction of chlorine atoms with copper in the Tomkins burner assembly was developed. This mechanism proposed the direct chlorine atom interaction with metallic copper l o produce volatile copper(l) chloride at the tube surface. The copper(1) chloride was then decomposed to atomic copper in the hydrogen-air flame. The Inhibitive effect of sodium on the copper deflection was also studied as a function of various experimental parameters. This study revealed that sodium when present in the flame changed the flame equilibria by combining with chlorine atoms to form sodium chloride. The interaction of chlorine atoms with the copper tube surface is thus reduced.
A positive Beilstein test (green coloration of a flame) is produced when a trace of organic halide is introduced into a nonluminous flame on a copper wire ( I ) . Although it is a generally accepted fact that only a negative Beilstein test for the presence of halogen in an organic compound may be taken as being conclusive, only a few halogen-free compounds containing nitrogen, or nitrogen together with sulfur give a positive test (2-4). Previous investigators have drawn attention to the fact that pure volatile copper com(1) F. K. Beilstein. Ber., 5, 620 (1872). (2) M. Jurecek and F. Muzik, Chem. Listy, 44, 165 (1950). (3) J. Van Alphen. Red. Trav. Chim. Pays-Bas, 52, 567 (1933). (4)H. Milrath. Chem. Zfg., 33, 1249 (1935).
pounds and products such as lard, butter, and suet even without copper give a green coloration in the flame (2, 3 ) . Generally, it may be stated that substances which give a positive test form volatile compounds with copper by either direct sample combination or the combination of sample pyrolysis products with copper(I1) oxide. A positive Beilstein test for the presence of organic halides was conclusive when the sample was introduced into the flame on a strong platinum wire underneath a hot copper gauze (2). A modification of the Beilstein test for the halogens has been utilized by Van der Smissen for the determination of halogen-containing compounds in air (5, 6). Although the burner emitted the spectra of Cu, CuO, CuH, and CuOH, the spectrum of CuCl was not observed (7). Gilbert used an indium-coated copper tube burner for the determination of chloride ion uia the InCl emission band (7, 8). A proposed flame mechanism included the conversion of sample chloride to HCl in the fuel rich flame, HCl contact with indium metal, and the formation of InCl which was excited in a secondary flame. In a previous paper (9), it was shown that chloride in contact with hot metallic copper increased the atomic copper content of the flame above the burner assembly in pro(5) Dragerwerk, German Patent, 1,095,551 (December 1960). (6)C. E. Van der Smissen, US. Patent 3,025,141 (March, 1962). (7) P. T. Gilbert, Anal. Chem., 38, 1920 (1966). (8) P. T. Gilbert. US. Patent 3,504,976 (April, 1970). (9) D. F. Tomkins and C. W. Frank, Anal. Chem., 44, 1451 (1972).
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