620
Anal. Chem. 1983, 55, 620-625
CONCLUSIONS The methodology described here appears to provide a convenient, reliable means of generating an electrochemical data base appropriate for qualitative characterization of organic analytes. The variables chosen and the levels over which they were changed appeared to be appropriate for later pattern recognition analysis. The changes in all the experimental variables were large enough and reproducible enough to produce significant effects on both the Faradaic and capacitive responses of an analyte. The effect of each variable was unique, so that redundant information was not recorded. The information content appears to be large. Subsequent pattern recognition studies will be reported (34)to demonstrate applicability to computerized analyte classification.
ACKNOWLEDGMENT The contribution of the diphenyl ethers by F. D. Hess is gratefully acknowledged. Registry No.p-Nitrophenol, 100-02-7;l-chloro-2-nitrobenzene, 88-73-3;o-nitrophenol, 88-75-5;m-nitrophenol, 554-84-7;mnitrotoluene, 99-08-1;l-chloro-4-nitrobenzene, 100-00-5; p nitrotoluene, 99-99-0;2,6-dichloro-4-nitrophenol, 618-80-4; pnitroanisole, 100-17-4; a-chloro-m-nitrotoluene, 619-23-8; 5-(3,4dichlorophenoxy)-2-nitrobenzoicacid potassium salt, 84132-82-1; 5(2,6-dichlor~4-trifuoromethylphenoxy)-2-nitrobemoic acid ethyl 5-(4-trifluoromethylphenoxy)-2-nitrobenzoic ester, 84132-83-2; acid, 84132-84-3;5-(3,4-dichlorophenoxy)-2-nitrobenzoicacid 5-(4-trifluoromethylphenoxy)-2-nitromethyl ester, 84132-85-4; benzoic acid sodium salt, 67815-55-8; 4-(2,4-dichlorophenoxy)2-ethoxy-l-nitrobenzene,33076-43-6;2-ethoxy-l-nitro-4-(trifluoromethylphenoxy)benzene, 50594-37-1; 1-nitro-4-phenoxybenzene, 620-88-2;5-(3-chloro-4-trifluoromethylphenoxy)-2nitrobenzoic acid methyl ester, 84132-86-5.
(28) (29)
(30) (31) (32) (33) (34)
LITERATURE CITED (1) Zuman, P. “The Elucidation of Organic Electrode Processes”; Academlc Press: New York, 1969; p 123. (2) Wopschall, R. H.; Shah, 1. Anal. Chem. 1967, 39, 1514. (3) Peover, M. E.; Powell, J. S. J. Elecfroanal. Chem. 1969, 2 0 , 427.
Krygowshl, T. M.; Stencel, M.; Galus, 2.J. Elecfroanal. Chem. 1972, 39,395. Sybrandt, L. B.; Perone, S. P. Anal. Chem. 1972, 44, 2331. Thomas, Q. V.; DePalma, R. A.; Perone, S.P. Anal. Chem. 1977, 49, 1376. DePalma, R. A.; Perone, S.P. Anal. Chem. 1979, 5 1 , 829. Schachterle, S. D.; Perone, S. P. Anal. Chem. 1961, 53, 1672. Meltes, L.; Zuman, P.; Rupp, E.; Fennor, T.; Spritzer, L., “CRC Handbook In Organlc Electrochemistry”; CRC Press: Boca Raton, FL, 1978; VOI 1-4. Burgard, D. R.; Perone, S. P. Anal. Chem. 1978, 50, 1366. Damaskin, B. B.; Petril, 0. A.; Balrakov, V. V. “Adsorptlon of Organlc Compounds on Electrodes”; Plenum Press: New York, 1971; p 40. Amadelll, R.; Daghetti, A.; Vergano, L.; DeBattisti, A.; Trasatti, S. J. Elecfroanal. Chem. 1979, 100, 379. Reference 11, p 39. Gupta, S.;Sharma, S. Elecfrochlm. Acta 1965, 10, 151. Dutklewlcz, E.; Puzcz, A. J. Elecfroanal. Chem. 1979, 100, 947. Ylh, R. Y.; Swlthenbank, C., J. Agrlc. Food Chem. 1975, 2 3 , 592. Bockrls, J. O’M.; Reddy, A. K. N. “Modern Electrochemistry”; Plenum Press: New York, 1970; p 714. Valerlate, E. M. C.; Barradas, R. G. J. Elecfroanal. Chem. 1966, 12, 67. Barradas, R. G.; Kimmerle, F. M. J. Elecfroanal. Chem. 1965, 9, 483. Retajczyk, T. F.; Roe, D. E. J. Elecfroanal. Chem. 1968, 16, 21. Pllla, A. A.; Margules, G. S.J. Necfrochem. SOC. 1977, 124, 1697. Ichise, M.; Yamaglshi, H.; Olshl, H.; Koglma, T. J. Elecfroanal. Chem. 1980, 106, 35. Elkln, V. V.; Leutsner, B. I.; Abaterov, M. A.; Kuzmln, V. G. J. Elecfroanal. Chem. 1979, 96, 149. Ryan, M. D. J. Necfroanal. Chem. 1977, 79, 105. Zipper, J. J.; Perone, S. P. Anal. Chem. 1973, 45, 452. Hendrlx, C. D. CHEMTECH 1979, 9, 167. Box, G. E. P.; Hunter, W. G.; Hunter, J. S. “Statlstics for ExDerimenters”: Wilev: New York. 1978: ChaDter 12. Delahay, P. J. Phys.-Chem. 1966; 70, 2373.’ Pilla, A. A. In “Electrochemistry, Calculations, Slmulatlon and Instrumentation”; Mattson, Mark, MacDonald, Eds.; Marcel Dekker: New York, 1972. Fratoni, S.S.,Jr.; Perone, S. P. Anal. Chem. 1976, 48, 287. Mlaw, L. L.; Perone, S.P. Anal. Chem. 1979, 51, 1645. Delahay, P. “Double Layer and Electrode Klnetlcs”; Wiley-Intersclence: New York, 1965; Chapter 10. Reference 27, p 319. Byers, W. A.; Freiser, B. S.;Perone, S. P. Anal. Chem., following paper in this Issue.
RECEIVED for review June 1,1982.Accepted December 8,1982. The authors express gratitude for the support of the Office of Naval Research.
Structural and Activity Characterization of Organic Compounds by Electroanalysis and Pattern Recognition W. Arthur Byers,’ B. S. Frelser, and S. P. Perone*l Department of Chemlstry, Purdue University, West La fayette, Indlana 47907
Pattern recognitlon techniques were used to characterize organlc electrochemical data. Studies Included compounds of three structural classes all of which were reduced In the same potential range wlth four electrons. I t was found that both capacitive and Faradalc responses were useful In characterization of the compounds. Structural classlflcatlons could be made wlth 93.3% accuracy uslng both types of responses. Classlflcatlonsof herblcldal actlvlty could also be made for nltrodlphenyl ethers.
The classification of chemical structure by using electrochemical techniques is a challenging problem. Although many Present address: Westinghouse Electric Corp., 1310 Beulah Road, Pittsbur h, PA 15235. Present adtress: Chemistry & Materials Science Department, Lawrence Livermore National Laboratory, Livermore, CA 94550.
organics of biological and commercia1 importance are electroactive, they generally produce electrochemical responses which consist of only a small number of peaks or waves. This lack of fine-structure makes fingerprinting difficult. The task of qualitative electroanalysis is made even more formidable from both a theoretical and experimental standpoint by the complex dependence of an electrochemical response on many variables. Electrode material and solution composition affect the nature of the electrode-solution interface and the characteristics of electron transfer in ways that are still not completely understood. An empirical approach such as computerized pattern recognition would seem to be the best solution for qualitative identification. A computer using artificial intelligence methods can rapidly search large amounts of multivariate data for obscure relationships between structure and activity. Pattern recognition has already been used with success in determination of biological activity (I),and in spectral interpretation (2).
0003-2700/83/0355-0620$01.50/00 1963 American Chemlcal Society
ANALYTICAL CHEMISTRY, VOL. 55, NO. 4, APRIL 1983
Within the field of electrochemistry, several pattern recognition studies have been done which have implications for qualitative electroanalysis. Schachterle and Perone (3) used pattern recognition to show that analysis of cyclic linear sweep voltammograms and cyclic staircase voltammograms yielded information which could be used to accurately classify the mechanism of an electrode reaction. Since similar structures can be expected to react by similar mechanisms, this approach should also be helpful in qualitative analysis. Burgard and Perone (4), used staircase voltammetry to analyze 29 compounds belonging to four different electroactive group/skeleton combinations. The classes examined were aromatic-nitro, aliphatic-nitro, aromatic-aldehyde, and aromatic-aliphatic-ketone. These classes were almost completely separated on the basis of peak potential, but this feature alone cannot be considered sufficient for many identification problems. For this reason the voltammograms were examined for any shape information which might characterize a particular electroactive group or the skeleton to which it was attached. It was found that the change in peak shape with scan rate produced fair classification (70% correct) but that complete separation of the classes was not possible for the experimental conditions and compounds which were chosen. This result suggests that the information content of the electrochemical data base should be increased for more reliable structural classifications. More information about the structure of an electroactive molecule may be gained by examination of its surface activity. The strength and potential dependence of adsorption may indicate the presence of certain functional groups (5, 6 ) . r-electron interaction with the electrode has a characteristic influence on the adsorption behavior of organics (3, and specific interaction between the analyte and some other molecule in the double layer may be helpful in identification
(8,9). Another potential source of structural information is the change in an analyte’s electrochemical response caused by some chemical perturbation. Changes in solution conditions have been useful in the classification of electrode processes by traditional techniques (10) and for this reason would be expected to produce useful structural data as well. The purpose of this paper is to evaluate the utility of pattern recognition in qualitative electroanalysis by using a data base which contains specific information on both adsorption and solution-analyte interactions. The optimization of experimental conditions for several classification problems will also be discussed.
621
Table I. Compounds Used and Class Designations class compound
1 1 1
1 1 1 1 1 1 1
2A 2A 2A 2A
4-nitrophenol 1-chloro-2-nitrobenzene 3-nitrophenol nitrobenzene 4-nitrotoluene 2,6-dichloro-4-nitrophenol 4-nitroanisole 2-chloro-6-nitrophenol oi -chloro-3-nitrotoluene 1-chloro-4-nitrobenzene methyl 2-nitrobenzoate 4-methyl-2-nitrophenol 5-chloro-2-nitro-l-(trifluoromethy1)benzene 3-meth yl-4-nitrophenol 2-chloro-5-nitro-l-(trifluorome thyl) benzene 4-methyl-3-nitrophenol 5-chloro-2-nitrobenzoicacid 3-nitrotoluene 2-nitrophenol ethyl 54 2-chloro-4[ trifluoromethyl]phenoxy)-2-nitrobenzoate methvl 5-~3.4-dichloroahenoxv~-2-nitrobenzoate 2-chl&o-i-( 3-ethoxy-4:nitropl&oxy)-4trifluoromethylbenzene methyl 5-(2-chloro-4[ trifluoromethyl]phenoxy)-2-nitrobenzoate
2B 2B 2B 2B 2B 3 3 3 3
3 3
potassium 5-(3,4dichlorophenoxy )-%nitrobenzoate 5 4 2-chloro-4[ trifluoromethyl]phenoxy)-2-nitrobenzoicacid sodium 54 2-chloro-4[ trifluoromethyl ]phenoxy)-2-nitrobenzoate 1,3-dichloro-4-(4-nitrophenoxy )benzene 4-phenoxy-1-nitrobenzene Acid Orange 8 Brilliant Crystal Scarlet Xylidine Ponceau 2R Bordeaux R Orange G Erichrome Blue Black B Sudan I New Coccine Acid Red 4 Chromotrope 2R Crocein Orange G Plasmocorinth B Amaranth Acid Red 88 Acid Red 8 Acid Alizarin N Mordant Blue 79
EXPERIMENTAL SECTION Forty-five (45) compounds representing three major structural classes were examined. All compounds were reduced between -0.3 and -0.8 V vs. SCE (pH 8) with four electrons. Large variations of reduction potential occurred within each class. The compounds and their class designations are listed in Table I. Class 1consisted of 19 nitroaromatics containing a single benzene ring. Class 2 contains nine nitrodiphenyl ethers which were further subdivided as strong herbicides (class 2A) and those compounds which showed little or no herbicidal activity (class 2B). The criterion for the herbicidal class assignments was the Orr test (11). Class 3 consisted of 17 azo compounds which had hydroxy substituents ortho to the azo linkage. Most of the azo compounds contained at least one sulfonic acid group and naphthalene ring. The nitrodiphenyl ethers were obtained from the Department of Botany and Plant Pathology, Purdue University. Purity was 95% or better. The azo compounds and most of the nitroaromatics were purchased from the Aldrich Chemical Co. in the purest form available. Experimental conditions were varied in a fractional factorial design so that the effect of each of seven variables (12)could be independently determined. Sixteen different cyclic staircase voltammograms and cyclic differential capacity curves were ob-
tained for every compound using instrumentation and techniques which have been described earlier (12). The effect that each variable had on the Faradaic or capacitive response of an analyte was determined by calculation of a variable effect curve. This curve was constructed by summing pointby-point all responses obtained for those experiments where the variable of interest was at its high level and then subtracting out all responses where the variable was at its low level. Since the experiments were arranged according to a fractional factorial design (13), the effects of all other variables except the one of interest were randomized over each level and did not contribute to the effect curve. Features for use in pattern recognition were extracted from each separate cyclic staircase voltammogram, cyclic differential capacity curve, and variable effect curve by the use of the fast Fourier transform. In each case, the cathodic and anodic portions of each curve were stored as one 256-point continuous response vs. time. The curve was scaled to cover a range of 0.0 to 1.0 and then rotated and translated in such a way that high frequency artifacts in the transform would be avoided (14). The transform was done with SUBROUTINE FORT (Purdue University Computer Center), and the first 64 coefficients were saved. Because
622
ANALYTICAL CHEMISTRY, VOL. 55, NO. 4, APRIL 1983
EXP. 1 , 2 ,
,. 16
Y A R . 1, 2 ,
.. 6
FXP. 1, 2 ,
.
16
VRR
1, 2 ,
.
7
Flgure 1. Feature sets.
of computer memory limitations, only the fmt 45 coefficients were used in later pattern recognition studies. Figure 1 shows the breakdown of the data and the nomenclature used in referring to the different feature sets. Separate pattern recognition analyses were carried out by using the feature sets listed at the bottom of the diagram, each of which consisted of 45 Fourier coefficients. The Faradaic factorial features were Fourier transform coefficients derived from individual staircase voltammograms in which the current had been sampled at the end of the step or after 30% of the step time had elapsed. There were 16 voltammograms for each compound, so 16 separate pattern recognition analyses were carried out with these features to determine which combination of experimental conditions produced voltammograms containing the most structural information. Faradaic Variable effect curve features were Fourier transform coefficients derived from Faradaic variable effect curves. Since there were seven variables in this study, seven Faradaic variable effect curves were calculated, each representing the average change in a staircase voltammogram when a variable was changed from its high to its low level. Seven separate pattern recognition analyses were carried out to determine if altering the level of any variable produced changes in a staircase voltammogram which were indicative of structure. In the same manner, capacitive factorial features and capacitive variable effect curve features were derived from the 16 differential capacity curves and six capacitive variable effect curves which were available for each compound. Only six capacitive variable effect curves were calculated since the sampling time variable applied only to measurements of Faradaic current. Pattern recognition analyses were carried out on the various feature subsets using the k nearest neighbor (kNN) classifier with k = 1. Euclidian distance was used as the measure of nearness. Since the number of compounds in each class was small, the leave-one-out (LOO) procedure (15) was used to estimate the predictive information that was contained in each feature subset. The optimum feature combinations and weighting factors for each feature within each subset were determined by a feedback learning algorithm which used the nearest neighbor distance error ("DE) as a performance criterion (16). The feedback learning algorithm began by testing each of the 45 features in a subset to see which gave the lowest error alone. The best single feature was then combined sequentially with each of the remaining 44 features to determine the best feature pair. Each feature was assigned a weight factor to minimize the "DE, and then the best three-feature combination was sought. This process was continued until neither adding another feature nor adjusting feature weights could reduce the "DE.
RESULTS AND DISCUSSION Faradaic Factorial Features. A pattern recognition analysis of the Faradaic factorial features gave classification accuracies for the 16 permutations of experimental variables. The most striking outcome of the analysis was the wide range of classification accuracies obtained, which varied between 91.1% and 68.9% over all classes. Within class 2 the classification accuracy varied between 33.3 and 100%. Obviously, the choice of experimental conditions has a strong effect on classification accuracy. The best conditions were for experiment no. 2 and 6, the worst was for no. 15. (See ref 12 for specific conditions.) Some of the variability is undoubtedly due to experimental error, Confidence limits for the classification accuracies could have been calculated if the entire data set had been replicated,
Table 11. Average Classification Accuracy at Each Variable Level for Faradaic Factorial Features, Classification of Structure average classification accuracy variable high level low level effect 76 ethanol 81.4 83.6 -2.2 79.7 85.3 -5.6 PH surfactant 80.6 84.4 -3.8 no. of cycles 81.7 83.3 -1.6 85.3 scan rate 79.7 5.6 drop hang time 80.8 84.2 -3.3 sampling time 81.1 2.8 83.9 but this was not done because of the large amount of time and effort involved. A rough estimate of the error was made by comparing the results for two experiments which were run under the same conditons (12). Here, the total classification accuracy varied by 4.5% for the Faradaic factorial features. Thus, it is reasonable to assume that the large variations in classificationaccuracy with changes in experimental conditions were due to more than just random error. To see if it would be advantageous to hold any variable level consistently high or low in future studies, we calculated the average classification accuracy for each level of each variable. Subtraction of one average from the other gave the effect of that variable on total classification accuracy. The results for all seven variables are listed in Table 11. The two variables which had the greatest effect on the total classification accuracy were pH and scan rate. The best structural classifications were made when the pH was at 8.0 rather than 5.1, and when the scan rate was 1.0 V/s rather than 0.25 VIS. The effect of pH on classification is most likely related to the presence of solvent reduction in the voltammograms obtained at the lower pH. Although the solvent reduction was largely eliminated through a blank subtraction, a good deal of error was involved in this process. Relatively small random changes in pH between the blank and the solution containing the analyte would cause measurable shifts in the solvent reduction. For this reason the base lines of voltammograms taken at pH 5 tended to fluctuate and degrade classification. The better classifications obtained at the higher scan rate could be due to several causes. Increasing the scan rate would cause the differences between systems having different electrode kinetics to be accentuated. The contribution of adsorbed species to the total current would be greater at higher scan rates allowing for better distinction between molecules of high and low surface activity. Finally, the signal to noise ratio would be improved for all systems a t higher scan rates. Faradaic Variable Effect Curve Features. Previous work in this laboratory indicated that the changes in the shape of a voltammogram associated with scan rate variation contained structural information ( 4 ) . In this study one of our goals was to confirm this finding and to see if changes in any other variable would also be helpful in structural classification. This goal can be achieved through the pattern recognition analysis of features derived from variable effect curves. A Faradaic variable effect curve represents the average change in a staircase voltammogram when a variable is changed from one level to another. If a variable has no effect on a voltammogram, the entire effect curve should contain nothing more than random noise and be of no use in classification. Previous work (12) has shown that all of the variables listed in Table I1 produce statistically significant and reproducible changes in voltammetric wave shapes for a subset of the compounds considered in this study. Here we will consider whether or not any of these changes are good structural indicators.
ANALYTICAL CHEMISTRY, VOL. 55, NO. 4, APRIL 1983
Table 111. Structural Classification by Using Faradaic Variable Effect Curve Features
variable effect curve % ethanol
PH surfactant no. of cycles scan rate drop hang time sampling time
no. of feaclassification accuracy total class 1 class 2 class 3 tures
66.7 84.4 84.4 93.3 89.0 84.4 75.6
78.9 94.7 78.9 94.7 94.7 78.9 63.2
44.4 44.4 66.7 88.9 66.7 66.7 88.9
64.7 94.1 100 94.1 94.1 100 82.3
2 5 4 5 6 5 4
Table IV. Average Classification Accuracy at Each Variable Level for Capacitive Factorial Features, Classification of Structure average classification accuracy high level low level
variable % ethanol PH sur factant no. of cycles scan rate drop hang time
87.4 84.9 87.1 84.3 83.7 85.1
81.1 83.6 81.4 84.2 84.7 83.3
N
'0:G
Oa'5
-VOLTAGE
l.'O
1.5
I
2.0
V5 S . C , E .
Figure 2. Second cycle staircase voltammograms of (a) Crocein Orange G and (b) 4-methyC3-nitrophenol showlng redox systems which appeared near -0.1 V vs. SCE with conditions of experlment 15 (ref 12).
Faradaic variable effect curves were calculated for each of the seven experimental variables. These curves were then analyzed in the same manner as the raw voltammograms. Fourier coefficients were extracted from the effect curves for each compound, and then an iterative feedback learning program was used to select and weight the coefficients to give the best possible classification accuracy. The results are listed in Table 111. The high classification accuracy obtained in the pattern recognition analysis of the scan rate effect curves confirms that changes in the shape of a voltammogram with scan rate are indeed useful in determination of structure. A total of 89.0% of the compounds examined were correctly classified by using scan rate effect curve features. Even better classifications were made by wing the "number of cycles" effect. A total of 93.3% of all compounds were correctly classified. This represents the highest total classification accuracy obtained for any set of Faradaic features. The electrochemical generation of new electroactive species is a likely source of the structural information contained within the "number of cycles" effect curves. Both the nitro and the azo compounds undergo chemical reactions after electron transfer to create new redox systems which are found near -0.05 V vs. SCE. Figure 2 shows voltammograms from each group. Another possible source of structural information is the change in the double layer between one scan and the next. Examination of differential capacity curves showed that there were often large changes in adsorption between cycles which would also be reflected in voltammetric responses. Capacitive Factorial Features. The cyclic differential capacity curves were analyzed in the same manner as were the cyclic staircase voltammograms, obtaining classification
G ,
'0.G
,
0.5 -VOLTAGE
,
1.G V5
effect
6.3 1.3
6.7 0.1 -1.0 1.8
IR1
I
,
623
I
1.6
1
2.0
5.C.E.
Figure 3. Blank-subtract differential capacity curves for (a) a nitrodiphenyl ether and (b) a single-ring nitroaromatic when both ethanol and surfactant were present. (Experiment no. 5, see ref 12.)
accuracies for the 16 permutations of experimental variables. The capacitive factorial features appear to perform just as well as or better than Faradaic factorial features in classification of structure. The maximum classification accuracy was 93.3% for experiment no. 1 (see ref 12). This compares to 91.1% obtained with Faradaic features. The average total classification accuracy for all experiments was also higher. The average for the capacitive factorial features was 84.4%, while the average for the Faradaic factorial features was 82.1%. To see if classifications using capacitance data were consistently better at one level or another for the variables in Table 11,we calculated the average classification accuracies for all the variable levels. Table IV lists the results. It is interesting to note that superior classifications were obtained when ethanol or sodium lauryl sulfate was present in solution. It seems that the presence of a surface active agent improved the classification of structure by increasing the differences between the responses of weakly and strongly adsorbed analytes. This was particularly true for the nitrodiphenyl ethers, where classifications were on the average 9% better when sodium lauryl sulfate was in solution. Figure 3 compares the response of a nitrodiphenyl ether with that of another nitroaromatic when sodium lauryl sulfate was present. The nitrodiphenyl ethers had a positive differential capacity peak near -1.2 V indicating that they were strongly adsorbed a t more anodic potentials. The nitroaromatics from class 1 had a negative peak in this region. The negative peak occurred because neither the surfactant (which caused a peak in the blank) nor the nitroaromatics were adsorbed on the electrode. Capacitive Variable Effect Curve Features. Relatively poor results were obtained when capacitive variable effect curve features were used for classification of structure. The classification accuracies obtained for the variable effect curves
624
ANALYTICAL CHEMISTRY, VOL. 55, NO. 4, APRIL 1983
Table V. Structural Classification by Using Capacitive Variable Effect Curve Features
variable effect curve % ethanol PH surfactant no. of cycles scan rate drop hangtime
classification accuracy no* of class class class featotal 1 2 3 tures
62.2 71.1 68.9 75.6 71.1 60.0
52.6 68.4 68.4 68.4 57.9 57.9
55.6 66.7 33.3 71.8 55.6 66.7
76.5 76.5 88.2 82.3 94.1 58.8
4 4 3 3 1 1
Table VI. Herbicide Analysis. Average MJEC at Each Variable Level by Using Faradaic Factorial Features variable % ethanol PH surfactant no. of cycles scan rate drop hang time sampling time
average MJEC high level low level
0.1047 0.1880 0.1872 0.0732 0.0732 0.1155 0.1050
0.0848 0.0015 0.0023 0.1164 0.1164 0.0740 0.0845
effect
0.0199 0.1865 0.1849 -0.0432 -0.0432 0.0415 0.0205
are given in Table V. (The effect of sampling time is not included because it is not applicable to the capacitance measurement (12).) The low signal to noise ratios in many of the effect curves are no doubt responsible for the poor classification accuracies. An earlier study showed that the differential capacity variable effects were too small to be distinguished from the noise for some weakly adsorbed compounds (12). Determination of Herbicidal Activity. Some nitrodiphenyl ethers are potent herbicides. It has been proposed that the mechanism of herbicidal action within the class involves the formation of nitrodiphenyl ether free radicals which initiate destructive chain reactions with the polyunsaturated fatty acid moieties of phospholipid molecules making up cellular membranes (11). Since the first step in the reduction of most nitroaromatics at the mercury electrode also involves the formation of radical species (18,the electrochemical behavior and the herbicidal activity of the nitrodiphenyl ethers may be related. If this is true, then it might be possible to predict the herbicidal activity of a nitrodiphenyl ether from its voltammetric behavior using computerized pattern recognition. The nitrodiphenyl ethers examined in this work can be divided into those which are strong herbicides (class 2A) and those which are weak herbicides or show no herbicidal action at all (class 2B). We have used computerized pattern recognition to search for electrochemical features which can distinguish between these classes. The features which described the nitrodiphenyl ethers were divided into the same subsets which were used in the struc-
tural classification study. Analysis of the Faradaic and capacitive factorial features demonstrated that 100% classification accuracy could be achieved for over half of the experimental conditions examined using no more than two features in each case. The lowest accuracy obtained was 77.8%. Although this result confirmed the hypothesis that voltammetric responses could be used to predict herbicidal activity, the fact that 100% classification accuracy was achieved so often made it difficult to draw any conclusions about the best experimental conditions for herbicidal analysis. For this reason another figure of merit for the performance of the pattern classifier, the modified Jurs criterion (MJEC) was used for selection of features and for evaluating experimental conditions. This figure of merit has been discussed briefly in an earlier work (16) and is calculated by the equation below:
MJEC =
2 [R + R tanh (DDS/WDS)I2
i=l
(1)
where n is the number of patterns, DDS is the distance to the kNN decision surface, WDS is the width of the decision surface, and R is used to weight correctly and incorrectly classified patterns. In the herbicide analysis, correctly classified patterns were given a weight of 0.1.Incorrectly classified patterns were given a weight of 1.0. A low MJEC value will indicate good class separation. Decision surfaces are usually not considered explicitly when using the kNN classifier, but their positions can be calculated if needed (18, 19). The results of the above analysis indicated that the best experimental conditions for herbicidal classification were no. 7,11,14,and 15 for Faradaic factorial features, and no. 4,5, 7,9,and 10 for capacitive factorial features. (See ref 12 for specific conditions.) As was found before in the classification of structure, capacitive factorial features performed somewhat better than Faradaic factorial features. The mean MJEC was 0.125 for the Faradaic factorial features, but it was 0.049 for the capacitive factorial features. The ability of capacitive factorial features to distinguish between classes 2A and 2B suggests that the hydrophobicity and/or the steric configuration of a nitrodiphenyl ether may play a very important role in determining its herbicidal activity. It appeared that classifications of herbicidal activity using Faradaic factorial features could be improved considerably by working at high pH and by not using surfactant. Large increases in the modified Jurs error criterion were observed when either nitric acid or sodium lauryl sulfate was added to the solution being analyzed. This effect is shown in Table VI. Table VI1 summarizes the results of the herbicide analysis using variable effect curve features. The modified Jurs error criterion has been listed as well as the total classification accuracy. Overall, features derived from variable effect curves did not do as well in the classification of herbicidal activity as did the features which were calculated directly from
Table VII. Results of Herbicide Analysis by Using Variable Effect Curve Features capacitive variable effect curve features Faradaic variable effect curve features no. of no. af variable MJEC % accuracy features MJEC % accuracy features % ethanol 0.0010 100 2 0.0027 100 2 PH 0.6939 77.8 2 0.4635 17.8 1 surfactant 0.0046 100 2 0.0039 100 2 no. of cycles 0.0015 100 2 0.6117 77.8 2 scan rate 0.0026 100 2 0.4075 77.8 1 drop hang time 0.5027 88.9 1 0.5009 88.9 1 sampling time 0.6877 77.8 1
ANALYTICAL CHEMISTRY, VOL. 55, NO. 4, APRIL 1983
Table VIII. Overall Summary of Strnctural Characterization Studies parametersa Ffact
best overall % classfctn no. of features best classfctn by classes most useful factors
Cfact
Fve
Cve
91.1
93.3
93.3
75.6
8
6
5
3
89.4 100 82.3 high pHand scan rate
100 77.8 94.1 high % EtOHand surfactant
68.4 77.8 82.3 no. o no. o cyc s cyc s 94.7 88.9 94.1
a Ff,, = Faradaic factorial; CfaCt= capacitive factorial; Fve= Faradaic variable effects; Cve = capacitive variable effects.
staircase voltammograms or differential capacity curves. Capacity effect curve features were particularly poor.
CONCLUSIONS Computerized pattern recognition appears to be very useful in qualitative electroanalysis when the proper experimental conditions are chosen, as summarized in Table VIII. It was possible to distinguish between three structural classes with similar electrochemical characteristics. Even though nitrodiphenyl ethers, the nitroaromatics, and the azo compounds were reduced in the same potential region with the same number of electrons, 93.3 % classification accuracy was achieved. Pattern recognition appears to be useful in predicting herbicidal activity from electrochemical behavior. Herbicidally active and inactive nitrodiphenyl ethers were distinguished from each other with a classification accuracy of 100% over a wide range of experimental conditions. Although the small number of herbicides studied precludes too much optimism, it might be possible to use the methods described here to screen newly synthesized compounds within the same general structural class for herbicidal activity. Another important area of application would be identification of herbicides within the environment. Perhaps the most significant result of the work reported here, however, is the demonstration that the information content of voltammetric data can be enhanced considerably by the systematic adjustment of experimental conditions. Moreover, it is clear that the interpretation objectives will dictate the choice of experimental conditions, and a welldesigned study as described here is capable of specifying the most favorable choices. It is also interesting to observe that the most useful experimental variations are not necessarily those which are traditionally valued most highly in voltammetric studies-such as the enhancement of surface interac-
625
tions. Thus, we hope the work described here will direct attention to the surprising amount of useful qualitative information available in voltammetry and to the effective tools for information enhancement and interpretation. Registry No. 4-Nitrophenol, 100-02-7;l-chloro-2-nitrobenzene, 88-73-3;3-nitrophenol, 554-84-7; nitrobenzene, 98-95-3; 4-nitro618-80-4; 4-nitrotoluene, 99-99-0; 2,6-dichloro-4-nitrophenol, anisole, 100-17-4; 2-chloro-6-nitropheno1, 603-86-1; a-chloro-3100-00-5;methyl nitrotoluene, 619-23-8; l-chloro-4-nitrobenzene, 2-nitrobenzoate, 606-27-9; 4-methyl-2-nitropheno1,119-33-5;5chloro-2-nitro-l-(trifluoromethyl)benzene, 118-83-2;3-methyl-4nitrophenol, 2581-34-2; 2-chloro-5-nitro-l-(trifluoromethyl)benzene, 777-37-7; 4-methyl-3-nitrophenol,2042-14-0; 5-chloro2-nitrobenzoic acid, 2516-95-2; 3-nitrotoluene,99-08-1; 2-nitrophenol, 88-75-5; ethyl 5-(2-chloro-4-[trifluoromethyl]phenoxy)2-nitrobenzoate, 77207-01-3; methyl 5-(3,4-dichlorophenoxy)-2nitrobenzoate, 84132-85-4; 2-chloro-l-(3-ethoxy-4-nitrophenoxy)-4-trifluoromethylbenzene,42874-03-3; methyl 5-(2-chloro4-[trifluoromethyl]phenoxy)-2-nitrobenzoate, 50594-67-7; potas84132-82-1;5-(2sium 5-(3,4-dichlorophenoxy)-2-nitrobenzoate, chloro-4-[trifluoromethyl]phenoxy)-2-nitrobenzoic acid, 5059466-6; sodium 5-(2-chloro-4-[trifluoromethyl]phenoxy)-2-nitrobenzoate, 62476-59-9; 1,3-dichloro-4-(4-nitrophenoxy)benzene, 1836-75-5; 4-phenoxy-l-nitrobenzene,620-88-2; acid orange 8, 5850-86-2; brilliant crystal scarlet, 2766-77-0; xylidine ponceau 2R, 3761-53-3;bordeaux R, 5858-33-3; orange G, 81604-88-8;erichrome blue black B, 3564-14-5; Sudan I, 842-07-9;new coccine, 2611-82-7; acid red 4, 5858-39-9; chromotrope 2R, 4197-07-3; crocein orange G, 1934-20-9;plasmocorinth B, 1058-92-0;amaranth, 915-67-3; acid red 88, 1658-56-6;acid red 8,4787-93-3; acid alizarin N, 2092-55-9; mordant blue 79, 3567-69-9.
LITERATURE CITED (1) Chu, K. C.; Feldmann, R. J.; Shapiro, M. G.; Hazard, G. F.; Geran, R. I. J . Med. Chem. 1975, 18, 539. (2) Abe, H.; Jurs, P. C. Anal. Chem. 1975, 4 7 , 1829. (3) Schachterle, S. D.; Perone, S. P. Anal. Chem. 1981, 5 3 , 1872. (4) Burgard, D. R.; Perone, S. P. Anal. Chem. 1978, 50, 1388. (5) Damaskin, B. B.; Petrli, 0. A.; Batrakov, V. V. “Adsorption of Organic Compounds on Electrodes”; Plenum Press: New York, 1971; p 40. (8) Amadeili, R.; Daghettl, A.; Vergomo, L.; DeBattisti, A.; Trasatti, S. J. Elecfroanal. Chem. 1979, 100, 379. (7) Reference 5, p 39. (8) Gupta, S.; Sharama, S. Elecfrochim. Acta 1985, 10, 151. (9) Dutkiewicz, E:,; Puacz, A. J. Elecfroanal. Chem. 1979, 100, 947. (IO) Zuman, P. The Elucidation of Organic Electrode Processes”; Academic Press: New York, 1969; Chapter 2. (11) Orr, G. Ph.D. Thesis, Purdue University, 1981. (12) Byers, W. A.; Perone, S. P, Anal. Chem., preceding paper in this lsSUB.
(13) Hendrix, C. D. CHEMTECH 1979, 9 , 167. (14) Hayes, J. W.; Clover, 0. E.; Smith, D. E. Anal. Chem. 1973, 45, 277. (15) Thomas, Q. V.; DePaima. R. D.; Perone, S. P. Anal. Chem. 1977, 49, 1378. (16) Byers, W. A.; Perone, S. P. Anal. Chem. 1980, 5 2 , 2173. (17) Kastening, B.: Holleck, L. J. Electroanal. Chem. 1970, 2 7 , 355. (18) Byers, W. A. Ph.D. Thesis, Purdue University, West Lafayette, IN, 1981, SUBROUTINE HYPE. (19) Batchefor, B. G. ”Practical Approach to Pattern Classification”; Plenum Press: London, 1974.
RECEIVED for review June 1,1982. Accepted December 8,1982.
