- Email: [email protected]
Effects of solvent and substituent on the electronic absorption spectra of some substituted Schiff bases: A chemometrics study
Spectrochimica Acta Part A 91 (2012) 198–205
Contents lists available at SciVerse ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Effects of solvent and substituent on the electronic absorption spectra of some substituted Schiff bases: A chemometrics study Bahram Hemmateenejad a,b,∗ , Mahdieh Yazdani a , Hashem Sharghi a a b
Department of Chemistry, Shiraz University, Shiraz 71454, Iran Medicinal & Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
a r t i c l e
i n f o
Article history: Received 3 November 2011 Received in revised form 7 January 2012 Accepted 16 January 2012 Keywords: Schiff base Absorbance spectra Solvatchromism Solvent Chemometrics
a b s t r a c t A series of Schiff bases were studied for their delicate changes in absorption electronic spectra by changing substituents and solvents. UV/vis absorbance spectra of Schiff base derivatives of different substituents ranging from electron withdrawing to electron donating (Br, CF3 , Cl, CN, CO2 H, F, Me, NO2 , OH, OMe, H) were studied in different solvents (acetonitrile, chloroform, cyclohexane, dioxane, dimethylsulfoxide and methanol). Linear relationships were established to investigate the effect of solute structure and solvatochromic parameters of solvents on the absorbance spectra. Meaningful chemical factors and then regression models were provided utilizing factor analysis (FA) and multiple linear regression (MLR). It was found that the frequency of maximum absorbance was mainly controlled by the solvent’s dipolarity/polarizability. The max of the ortho-nitro derivative represented the largest dependency on solvents’ polarity/polarizability so that it can be used as a solvatochromic probe. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Schiff bases are of the general formula of R1 R2 C N-R3 , where R3 is an aryl or alkyl group that makes the Schiff base a stable imine. Many biologically important Schiff bases have been also reported in the literature possessing antibacterial, antifungal, antimicrobial, anticonvulsant, anti HIV, anti- inflammatory and antitumor [1]. In addition to biological and pharmacological applications, these compounds are potent for analytical applications. Therefore, they have been extensively studied for their photometric and thermodynamic properties and also proton transfer tatumeric equilibria [2,3]. These increasing applications show a need to concentrate more on their specific characteristics. Since many analytical applications of these compounds involve color changes (or spectrophotometric studies), we are interested on experimental and theoretical investigations on the color changes of these compounds. In general, when absorption spectrum of a chemical species is measured in solvents of different polarity it is usually found that the position, intensity and shape of absorption bands are usually affected by the solvents. These changes are a result of physical
∗ Corresponding author at: Department of Chemistry, Shiraz University, Shiraz 71454, Iran. Tel.: +98 711 613 7360; fax: +98 711 228 6008. E-mail address: [email protected] (B. Hemmateenejad). 1386-1425/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2012.01.023
intermolecular solute–solvent interaction forces which tend to alter the energy difference between ground and excited state of the absorbing species containing the chromophore. The term solvatochromism is used to describe the pronounced change in position (and sometimes intensity) of an UV/vis absorption band, accompanying a change in the polarity of the medium. A hypsochromic (or blue) shift, with increasing solvent polarity, is usually called negative solvatochromism. The corresponding bathochromic (or red) shift is termed positive solvatochromism [4,5]. We have recently synthesized some novel Schiff base compounds [6] and investigated their complex formation and acid–base equilibria by spectrophotometric methods [7]. In these studies we observed that the color of Schiff bases is changed considerably by changing in the substituents and also by dissolving them in different solvents. In fact, the Schiff base chromophore derivatives show substantial photoinduced effects and the electron–vibration interactions including anharmonic ones play the principal role [8,9]. In the present work, we aimed to study the effects of substituent and solvent on the synthesized Schiff bases through spectra-structure and spectra-solvent relationships. The effects of substituents have been investigated using QSPR (quantitative structure–property relationship) analysis utilizing molecular descriptors [10]. In addition, solvent effects were studied concerning solvent parameters (namely hydrogen bond donating ability (˛), hydrogen bond accepting ability (ˇ) and dipolarity/polarizability (* )). Finally multiple linear regression (MLR) and factor analysis (FA) were used to find the meaningful chemical factors and provide the regression models.
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
The longest wavelength of maximum absorbance (max ) was carefully determined using Savitsky–Golay second derivatives [11] of the calculated pure spectra. The wavelengths were converted to wavenumbers (max ) before being used in the regression analysis.
B A
R3
199
N HO
2.3. Procedures for data analysis
R2 R1 Ortho-substituted; R1= Br, CF3, Cl, CN, CO2H, Me, NO2, OH, OMe Meta-substituted; R2= Br, Cl, CN, CO2H, F, NO2, OMe Para-substituted; R3= Br, Cl, CN, CO2H, F, Me, NO2, OMe Fig. 1. The structure of Schiff bases used in this study.
2. Experimental 2.1. Reagents A series of laboratory synthesized Schiff bases [6], containing different substituents ranging from electron withdrawing to electron donating (Br, CF3 , Cl, CN, CO2 H, F, Me, NO2 , OH, OMe, H) in ortho, meta and para positions of one of the aromatic ring (ring A) were used for this study (see Fig. 1). Furthermore, extra pure organic solvents, provided from Merck chemical company, were used as received. The compounds powders were highly stable so that the solutions prepared from each compound about one year later showed about 5% reduction in absorbance. 2.2. Procedure for determination of max A solution of 0.001 M of each compound was prepared in each of the studied solvents and then 10.0 L aliquots of the solution was injected into the 10 mm quartz cell that was initially filled with 2.0 mL of the same solvent. After each injection, the electronic absorption spectrum was measured by a Hewlett Packard diode array spectrophotometer (model HP8452A) with spectral resolution of 1.6 nm. In each titration, 24 injections were made to obtain different concentrations of a solute in a specified solvent. By this manner, a data matrix was obtained for each molecule, the rows and column of which were solutions of different concentrations and different wavelengths, respectively (see Supplementary materials Fig. S1 for schematic representation of data array for each solute in each solvent). Consequently for each molecule, six data matrices consisting of their spectra of different concentrations in the six aforementioned solvents were obtained. Furthermore, because ortho-NO2 substituent shows drastic shifts by changing solvents, its spectrum was recorded in 6 extra solvents (i.e., DMF, ethyl acetate, n-heptane, n-hexane, THF, water) in order to be able to see the corresponding shifts more tangible. A total of 25 molecules were studied in 6 solvents and hence 150 data matrices were obtained. These data matrices were used to calculate the pure absorbance spectra (molar absorptivity) of the studied solutes in different solvents, and then wavelength of maximum absorbance. The pure absorbance spectrum was determined by least square regression of the recorded absorbance spectra against solute concentration according to the Beer–Lambert law: A = cs
(1)
−1 sˆ = c+ A = (c c) c A
(2)
where A is the collected absorbance data matrix of the individual solutes in a solvent, c is the vector of the concentrations of solute and s is the molar absorptivity spectrum. The superscripts (+), ( ) and (−1) denote pseudo-inverse, transpose and inverse of a matrix, respectively. The superscript (ˆ) denotes the calculated values.
2.3.1. Software Molecular modeling and geometry optimization were performed by Hyperchem (version 7.0). Electronic descriptors were obtained from this software. Furthermore, Dragon software (version 2.1) was used for calculation of 18 different sets of descriptors. SPSS software (version 12, SPSS Inc.) was also used for MLR analysis. Other calculations like FA and calculations of molar absorbtivity coefficients were performed in MATLAB (version 7.0, MathWorks, Inc.) environment. All computations were performed on a Pentium IV personal computer (CPU 1.70 GHz) with windows XP operating system. 2.3.2. Data set The response data analyzed in this work was the wavenumber of maximum absorption (max ), which was obtained for 25 solutes in 6 solvents. The data were arranged in a (25 × 6) data matrix (see Fig. S2 of supplementary materials). For each type of substitution position (i.e., ortho, meta and para) a separate sub-matrix was extracted and used for further analysis. 2.3.3. MLR modeling for calculation of substituent effect Each column of the matrix shown in Fig. S2 represents the variations in the max of solute over changing in the substitution in a specified solvent. The aim is to relate these changes to a meaningful molecular descriptor. Therefore, for each of the 6 columns (in each data set individually), a separate regression model was obtained using molecular descriptors calculated by Dragon and Hyperchem software. Since the number of descriptors was much higher than the number of molecules, we used two-step stepwise regression to select the most relevant subset of descriptors for each solvent and to obtain meaningful models having as lower number of variables as possible [12–14]. Firstly, all descriptors were divided into different groups (according to the classification made in Dragon software) and then a separate stepwise MLR was developed for each group of descriptors. After evaluating the models by cross-validation and selecting the best model of each group, the variables appeared in the models were used as input of another stepwise selection model. 2.3.4. MLR modeling for calculation of solvent effect Each row of the data matrix shown in Fig. S2 represents the changes in the max of an individual solute in different solvents. The aim is to relate these changes to solvatochromic parameters of the solvents [15]. Table 1 lists the solvatochromic parameters (hydrogen bond donor ˛, hydrogen bond acceptor ˇ, and dipolarity/polarizability * ) for the employed solvents.
Table 1 Solvatochromic parametes of the solvent used. Solvents
␣
ˇ
*
Acetonitrile CHCl3 Cyclohexane Dioxane DMSO Methanol
0.19 0.44 0 0 0 0.93
0.31 0 0 0.37 0.76 0.62
0.75 0.58 0 0.55 1 0.6
200
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
Fig. 2. The calculated pure spectra of all Schiff bases used in this study in DMSO and chloroform solvents.
Thus, for each row (in each data set individually), a regression model was obtained by stepwise selection of the solvatochromic parameters.
3. Results and discussion
2.3.5. Factor analysis procedure Factor analysis (FA) and/or principal component analysis (PCA) are multivariate techniques for reducing matrices of data to their lowest dimensionality by the use of orthogonal factor space and transformations that yield predictions and/or recognizable factors. Scientist do not always distinguish between FA and PCA, but for chemists factors often have a physical significant, therefore FA is a procedure to relate the abstract factors or principal components (PCs) to chemical factors (e.g., solute or solvent properties) [11,16]. Instead of analysis of the behavior of each solute in different solvent or analysis the effect of the structure of a list of solutes in one solvent by MLR, FA provide a bilinear decomposition of the wavenumber data matrix of all solutes in all solvents to develop multi-linear models for all solutes or all solvents [16]. The wavenumber data matrices for each set of ortho, meta and para substituents were firstly subjected to PCA using singular value decomposition (SVD). The numbers of principal factors were determined utilizing different criteria such as indicator function, scree plot, PRESS and so on. Target transformation factor analysis (TFA) was employed to separate the solvent and solute effects and then to obtain regression equations for all solute and all solvents, simultaneously. All the solvent and solute parameters used in MLR analysis were tested one-by-one as target. The score matrix (which was the row matrix) was correlated with the molecular descriptors and the loading matrix (which was the column matrix) was correlated with the solvatochromic parameters.
For each solute in each solvent, a data matrix was provided from the digitized absorbance data as a function of concentration. A selection of the absorbance spectra are given in Fig. S3 of the supplementary materials. This data matrix was then regressed over the solute concentration to obtain pure spectrum of that solute in the considered solvent. It is obvious that these spectra are independent of solute concentration in the studied concentration ranges. As an example the calculated pure spectra of each solute in two solvents of high and low polarities (i.e., chloroform and DMSO) are represented in Fig. 2. Obviously, the shape of pure spectra is highly dependent upon substitution pattern as well as the solvent. Whilst most molecules represent a collection of overlapped peaks, some of them represent distinct absorbance peak. This behavior is not only dependent on type and characteristics of substituents but also it dependents on the substitution position. Due to the highly spectral overlapping of the peaks, it was very difficult to detemine the max of each peak accurately. Actually, in solvatochromic studies the max of the longest wavelength is usually studied. As it is observed from the shown pure spectra in Fig. 2, the peaks of the longest wavelength in almost all cases are also overlapped with the peaks of the shorter wavelengths. There are some different approaches for accurate determination of peak position, among which first and second derivatives are more popular [11]. In the first derivative method, which is more useful for non-overlapping or low-overlapping peaks, the derivative approches zero at the max and thus the wavelengths at zero crossing are considered the max . This method does not give highly
3.1. Pure spectra and max
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
201
Table 2 max of the longest wavelength for the ortho, meta and para substituted derivatives in different solvents (nm). No
Substituent
Acetonitrile
CHCl3
Cyclohexane
Dioxane
DMSO
Methanol
O01 O02 O03 O04 O05 O06 O07 O08 O09 O10 M01 M02 M03 M04 M05 M06 M07 P01 P02 P03 P04 P05 P06 P07 P08
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-OH o-OMe o-H m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2 p-OMe
347 344 347 351 337 342 402 355 353 340 342 342 342 340 342 344 346 344 344 351 348 340 344 365 352
351 347 351 353 338 344 398 368 368 344 348 348 348 346 346 348 350 350 350 354 352 344 346 368 354
352 350 352 354 NS 346 378 NS 351 347 350 350 NS NS 348 NS 352 353 353 355 NS 349 348 NS 354
351 347 351 353 338 344 398 363 370 344 348 348 348 346 346 348 350 350 350 354 352 344 346 368 354
334 340 333 336 334 338 419 366 364 337 336 335 331 334 334 332 344 341 341 334 335 340 344 391 355
349 345 349 352 337 343 404 358 352 341 344 344 344 341 344 346 348 346 346 352 349 342 344 367 352
NS: The compound is not soluble in that solvent.
accurate results for peaks of serious overlapping. In this case, second derivative method is more useful. If two overlapped peaks represents a single absorbance maxima, their second derivatives produces two peaks maxima or minima at the corresponding max . We used the Savitsky–Golay method for derivative calculations [17,18]. The estimated max values of the longest wavelength are represented in Table 2. By scanning this Table, even vertically or horizontally, the variations of max by changing substituents and solvents are evident. These changes can be explained by selecting suitable molecular descriptors or solvent parameters. Besides, the molar absorptivity of the solutes at the calculated max was also calculated and the results are given in Table S1. Dependency of molar absorption on the substituent and solvent is also evident from Table S1 and Fig. 2. However, it was difficult to explain it using mathematical models. Before bringing out the quantitative results of MLR and FA in the substituent effect, a discussion about the qualitative results seems to be suitable. It is seen from Table 2 that the maximum absorption wavelength for ortho and para substituents shifts to longer wavelengths for CN, CO2 H, NO2 , OH and OMe groups with respect to other substituents whereas this phenomenon does not observe for meta substituents. This behavior can be simply attributed to the resonance of the mentioned substituent with the aromatic rings of the Schiff bases. In the molecular-orbital treatment, electrons are considered to be further delocalized by conjugation. The effect of this delocalization is to lower the energy level of the * orbital and give it less antibonding character. Absorption maxima are therefore shifted to longer wavelengths as a consequence of conjugation [4]. Thus, this provided data of UV/vis absorbance confirm the fact that meta substituents in an aromatic ring does not participate in resonance in contrast to ortho and para substituents. (It should be noted that for clarification in this text and * symbols has been used for molecular orbitals and * for the polarity of the solvent). 3.2. MLR results According to the solvatochromism point of view [4,5], max values were converted to max values and used for statistical analysis. As it was mentioned before, for each set of ortho, meta
and para compounds separate models were obtained. Firstly the effects of molecular structure were investigated. The final obtained QSPR models in each solvent are summarized in Table 3. It is observed that for all molecules mono-parametric equations have been obtained and the correlation coefficient of multiple determination is varied between 0.64 and 0.97. The variables selected for QSPR models are different for different substitution position. This suggest that the effect of substituent on max is dependent on its position. In addition, the selected variables are more or less different in different solvent. This means that the effect of solute on max could be also dependent on the type of solvent. To study the solvent effects by MLR, individual equations were derived for each solute by regressing max against solvatochromic parameters of the solvent. The results are represented in Table 4. Obviously, the solvent dipolarity/polarizability is the only solvatochromic parameter that has been appeared in all equations. Unlike substituent effect, the MLR equations of the solvent have high statistical quality and for limited number of solutes R2 is less than 0.80. In these equations, * has positive coefficient for all substituents, expect o-NO2 . The positive coefficients explain that by increasing in the solvent polarity, the energy need to excite the electrons to higher level orbital is increased. Consequently, blue spectral shift was observed by increasing in solvent polarity. The negative coefficient of o-NO2 will be discussed later. 3.3. Models obtained by FA By MLR analysis, QSPR models were generated for a list of solutes in individual solvents and also solvatochromic models were created for individual solutes. Actually, MLR can analyze only a vector of response (e.g., each column or row of the wavenumber data matrix) at each time. One main drawback of MLR analysis for multiresponse data is the inconsistency between the obtained equations for different situations, such as different QSPR models obtained here in different solvents. This makes the interpretation of the results difficult. However, by FA it is possible to analyze a multiresponse data and therefore generate a unique model for the set of responses at all conditions (e.g., providing QSPR models having the same variables in all solvents). Note that in this case we have
202
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
Table 3 MLR models obtained for investigating the effect of molecular structure on the frequency of absorption of the longest wavelength. Subset
Solvent
Equation
R2
ortho
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 32096.68 (±365.83) − 9292.25 (±1386.98) E2v max = 30262.15 (±409.34) − 26391.25 (±8821.98) R8p+ max = 31439.38 (±718.35) − 8408.61 (±2431.74) E2v max = 31358.42 (±671.01) − 8621.33 (±2271.48) E2v max = 31358.42 (±671.01) − 8621.33 (±2271.48) E2v max = 35982.89 (±2006.66) − 10836.74 (±2928.31) Mv
0.65 0.64 0.71 0.74 0.74 0.77
meta
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 30451.28 (±174.98) + 8830.99 (±2181.38) Mor22v max = 18494.02 (±1964.03) + 14083.92 (±2585.49) SIC2 max = 29226.78 (±48.66) + 3297.89 (±652.89) Mor22v max = 28874.43 (±37.84) + 1969.50 (±507.67) Mor22v max = 28874.43 (±37.84) + 1969.50 (±507.67) Mor22v max = 22695.44 (±2963.86) + 7752.58 (±3909.61) SIC2
0.81 0.86 0.84 0.75 0.75 0.66
para
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 27954.55 (±147.62) − 3010.31 (±93.41) RDF120v max = 29222.34 (±92.64) − 1394.27 (±288.65) RDF120v max = 30667.99 (±431.55) − 9174.11 (±2096.22) Mor30p max = 35681.54 (±2104.14) − 3388.74 (±1002.68) GATS4p max = 35681.54 (±2104.14) − 3388.74 (±1002.68) GATS4p max = 28650.44 (±119.81) − 1809.37 (±556.78) Mor30p
0.97 0.82 0.79 0.69 0.69 0.78
three data matrices; (i) data matrix of response variables, D, (ii) a data matrix of molecular descriptors, (X) and (iii) a data matrix of solvent properties (Y). FA is performed on D and then the score and loading matrices of D are related to X and Y, respectively. Consider the arrangement of the wavenumber data in a data matrix D as shown in Fig. S2 so that the rows are related to the solute structure and the columns are related to solvent property. Application of PCA on this data matrix results in score (T) and loading (P ) matrices spanning the row and column spaces of the original data matrix, respectively. The non-modeled data by PCA model are included in matrix E, which has the same dimension as D: D = TP + E
(3)
The first step in FA is to determine the number of chemical factor or chemical dimensionality of the response data matrix D (or the number of columns in T and P). For all subsets of molecules, data matrices were found to be explained by two chemical factors.
The matrices T and P are pure mathematical factors and the next step is to transform these factors to chemically meaningful factors using solute descriptors (X) and solvent properties (Y) as suitable targets for T and P, respectively. This was done via target factor analysis (TFA). Apparent error in target (AET) was used to select the most suited targets [16,19]. The targets, which represented the lowest AET for investigating the solute structure, are listed in Tables S2–S4 of supplementary materials. Unity vector, which is used to account the effect of offset or intercept, was found as one of the significant target for all subsets. Beside the unity vector, the molecular descriptors, which represented the least AET were E2v, Mor22v and Mor30p for ortho, meta and para subsets, respectively. This means that for the meta substituted derivatives, the descriptors E2v is the most significant parameters in all solvents and hence in all solvents the effect of molecular structure for ortho-substituted derivatives can be written as max = bx0 + bx1 E2v. Similarly, the respective QSPR models for meta and para subsets can be written as max = bx0 + bx1
Table 4 MLR models for investigating the efect of solvent on max for different solutes.a Subset
Substituent
Equation
ortho
R2
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-H
*
max = 26665.85 (±303.73) + 3178.93 (±424.36) max = 28477.48 (±101.58) + 807.61 (±155.47) * max = 26547.28 (±338.55) + 3375.12 (±473.02) * max = 26481.97 (±438.24) + 3134.59 (±612.29) * max = 29172.10 (±93.44) + 746.67 (±130.56) * max = 28801.94 (±100.24) + 634.97 (±153.42) * max = 26475.35 (±170.51) − 2486.03 (±260.98) * max = 28733.99 (±113.45) + 851.98 (±173.64) *
0.95 0.87 0.94 0.90 0.92 0.81 0.98 0.86
meta
m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe
max = 28358.21 (±225.74) + 1139.33 (±345.50) * max = 28333.16 (±246.94) + 1208.05 (±377.95) * max = 26981.35 (±269.27) + 3185.50 (±376.22) * max = 27849.47 (±411.43) + 2078.60 (±574.84) * max = 28497.74 (±236.03) + 1092.61 (±361.25) * max = 26999.01 (±274.49) + 3036.83 (±383.51) * max = 28322.22 (±102.33) + 668.31 (±156.61) *
0.73 0.72 0.96 0.81 0.70 0.95 0.82
para
p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2
max = 28208.47 (±160.46) + 1010.82 (±245.59) * max = 28208.47 (±160.46) + 1010.82 (±245.59) * max = 26110.21 (±480.37) + 3673.95 (±671.16) * max = 26651.17 (±336.42) + 3104.03 (±470.02) * max = 28665.51 (±85.09) + 822.71 (±130.23) * max = 28745.68 (±63.38) + 366.18 (±97.01) * max = 26530.46 (±105.42) + 1157.74 (±168.74) *
0.81 0.81 0.91 0.94 0.91 0.78 0.96
a
o-OH, o-OMe and p-OMe did not show significant relation with any of the solvatochromic parameters.
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
203
Table 5 Regression coefficients of the QSPR model of (max = bx0 + bx1 x)a obtained by TFA for accounting the effect of molecular structure. Subset
Solvent
bx0
bx1
ortho
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
28845 31838 32071 31906 31906 31117
3142 −9443 −10564 −10492 −10492 −8362
meta
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
30638 29329 29161 28849 28849 28683
13686 2353 2169 1526 1526 1509
para
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
20239 30683 30930 30927 30927 30545
44495 −8772 −10458 −11542 −11542 −10586
a
x is E2v, Mor22v and Mor30p for ortho, meta and para subsets, respectively.
Mor22v and max = bx0 + bx1 Mor30p. The model constants, which were easily calculated by target transformation [20] are listed in Table 5. The plot of predicted max by the suggested model against the experimental value for all solutes in all solvents resulted in an stright line with squared correlation coefficient of 0.99. To investigate the solvent effect by TFA, the solvatochromic parameters were projected on the loading space. The AET calculated for each solvatochromic parameters are given in Tables S5–S7 of supplementary materials. Obviously, unity vector and solvent polarity/polarizability can be selected as meaningful factors. Thus, similar to MLR analysis, the solvatochromic parameter * was considered as the most significant solvent property effecting the shift in the longest max . According to these observations and in the similar manner as MLR, the effect of solvent on the max of all studied compounds can be described by the equation of the form of max = by0 + by1 * . The calculated regression coefficients by TFA are listed in Table 6. Table 6 Regression coefficients of the solvatochromic model of (max = by0 + by1 * ) obtained by TFA for accounting the effect of solvent. Subset
Substituent
by0
Ortho
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-H
26508 28100 26372 26266 29126 28398 26759 28412
by1 3405 1322 3626 3444 813.2 1186 −2887 1290
Meta
m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe
27597 27455 26880 27803 27545 26822 28070
2172 2401 3331 2146 2393 3290 1006
para
p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2
27872 27872 25785 26450 28834 28841 26610
1460 1460 4141 3392 584 233 1176
Fig. 3. (a) The recorded spectra of o-NO2 derivative in 12 solvents and (b) changes in max of this molecules against * scale of solvents.
A comparison between the results of MLR and FA would be interesting. Concerning with effect of solute structure, FA resulted a unique model for all solvents whereas MLR did not. However, one can find some consistency between the models produced by two methods. For example, MLR selected E2v as input variable for the QSPR models of the ortho-substituted in most of the solvents and FA detected this descriptor as the most important one. Similarly, for the meta-substituted molecules, the descriptor Mor22v, which selected by MLR in many solvents, was also identified as the most important one. Moreover, both MLR and FA identified * as the most important solvent parameter. A comparision between the regression coefficients of the solvatochoromic model calculated by MLR and FA suggest that although there is some differences between two calculated values they are almost similar. And another interesting point is that both methods calculated negative coefficients of * for o-NO2 . 3.4. Comments on the selected solvatochromic parameter MLR and FA results showed that solvent dipolarity/polarizability is the most important factor which affects the maximum absorption wavelengths by changing the solvents. There are, however, some points to be precisely delineated. The regression coefficient of * is one of the important points to pay attention. In all cases the regression coefficients are positive except for o-NO2 substituent. As it was noted previously, the positive coefficient confirms that n → * electron transition is responsible for such an absorbance peak. This is the characteristics of the C N functional group in the molecule. The o-NO2 substituent, however, does not obey this trend. To be sure about the results obtained for this molecule, the pure spectrum of this molecule was investigated in 6 extra solvents. Fig. 3 shows the absorbance spectra of o-NO2 in all studied solvents and also the changes in and max of maximum absorption of this molecule over * . The wavelengths of maximum absorbance for o-NO2 in the set of 12 solvents are listed in Table S8 of supplementary materials. Again a negative relationship with a high degree of linearity (R2 = 0.92) is observed between max and * . It can be concluded that unlike other
204
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
derivative, the transition responsible for the longest wavelength absorbance maxima of o-NO2 is → * . The studied Schiff base molecules are potent to have two major transitions: n → * and → * . Among these electronic transitions, n → * needs lower energy and will be located in the longest wavelength region of the spectrum. However, some substituents having nonbonding electrons on N, O or halogen atoms, exert a significant hypsocromic shift on this transition and on the other hand exert a weaker bathochromic shift on → * transition. The bathochromic shift is due to the mentioned resonance effect and the hypsochromic shift is due to the induction effect of the N, O and halogen atoms. These atoms pull the nonbonding electrons of N in C N group (in the Schiff bases) toward themselves and the outcome results is that the nonbonding electrons of N in the C N group will be held more strongly with respect to the situation in which no induction effect exists. Thus, in some cases it is possible that n → * transition falls under the → * transition [4,21]. NO2 group in the ortho position of the aromatic ring, with a nitrogen and two oxygen atoms and also nonbonding electrons, is very potent to exert these shifts and exchange the positions of the transitions in the corresponding spectrum. In addition to the above behavior of o-NO2 derivative, it also represented the largest change in max in solvents of lowest to highest polarity (i.e., 43 nm shift in max from n-hexane to DMSO). Thus, it can be used as a new probe of solvent polarity/polarizability in solvatochromism studies. 3.5. Comments on the selected descriptors (a discussion about substituent effect) 3.5.1. Para substituted derivatives Initial regression results (MLR modeling) showed that by changing substituents in para molecules three descriptors [10] (RDF120v, Mor30P, GATS4p) showed relationship with max . FA results, however, revealed that Mor30p (a 3D-MoRSE descriptor) is the most significant one. 3D-MoRSE descriptors (three dimension molecule representation of structures based on electron diffraction) are molecular descriptors calculated by summing atomic weights viewed by a different angular scattering function. A typical MoRSE descriptor is denoted by M or sw where s and w take the values 1 ≤ s ≤ 32 and w ∈ {u, m, v, e, p} where u is unweighted, m is weighted by mass, v is weighted by van der Waals volume, e is weighted by electronegativity, p is weighted by polarizability. Thus, Mor30p is weighted by polarizability. The term polarizability describes a molecular property having to do with deformability of a bond. Polarizability is the relative tendency of a charge distribution, like the electron cloud of an atom or molecule, to be distorted from its normal shape by an external electric field, which may be caused by the presence of a nearby ion or dipole [21]. The main factor determining the polarizability is ground state dipole moments. A molecular polarizability effect occurring by an intramolecular electron displacement (sometimes called the “conjugative mechanism” and, previously, the “tautomeric mechanism”) characterized by the substitution of one electron pair for another within the same atomic octet of electrons. It can be indicated by curved arrows symbolizing the displacement of electron pairs, as in
which represents the hypothetical electron shift
This implies the fact that para substituents exert their influence on the chromophore directly by resonance. During resonance there is an electronic movement in the molecule. The double bond become a single bond and vice versa. There will be positive and negative charges on electropositive and electronegative atoms for a moment and the whole process lead to polarizability. A point is important to mention here: GATS4p and RDF120v are the descriptors which were selected in the first step of MLR but did not enter the final model (Table 5). GATS4p (a 2D-autocorrelation descriptor) is weighted by atomic polarizability and therefore confirm the effect of Mor30p descriptor. RDF120v is among RDF descriptors and is weighted by atomic van der Waals volumes. Appearing this descriptor, even in the first step of regression, conceptualize that the effect of para position could also be in the form of spatial induction if the volume of the substituents becomes a predominant factor. This point is important since the selected descriptors in meta and ortho case are weighted by atomic van der waals volumes, as will be discussed afterward. 3.5.2. Meta substituted derivatives MLR and FA results showed that Mor22v (a 3D-Morse descriptor weighted by atomic van der Waals volumes) is the most significant descriptor for meta-substituted derivatives. Substituents in meta position exert their influence on the reaction center by field effect not resonance. Selection of this descriptor validate that the influence of meta-position is feasible in the course of a spatial induction (field effect). 3.5.3. Ortho substituted derivatives For this subset, E2v as one of the WHIM descriptors was selected. WHIM descriptors are 3D molecular descriptors obtained as statistical indices of all the atoms projected onto the three principal components calculated from weighted covariance matrices of the atom coordinated. WHIM approach can be viewed as a generalization searching for the principal axes with respect to a defined atomic property (the weighting scheme). The following weighting schemes are used for computing the weighted covariance matrix, Sw : unweighted (u), atomic masses (wi = mi ), atomic van der Waals volumes (wi = vi ), atomic Sanderson electronegativities (wi = ei ), atomic polarizabilities (wi = pi ) and atomic electro topological states (wi = si ). E2v is the second component derived from the van der Waals volumes weighted covariance matrix. Two points are important to mention here: Firstly, E2v is weighted by atomic van der Waals volume. Secondly, the second PC of the aforementioned matrix shows a good relation with the wave numbers. Take into consideration the fact that the first PC usually contains the most variance, it is concluded that some other factors affect the results in this position. Steric hindrance is one of the aspects that can be discussed here. The factors that cause the steric hindrance were considered to be the bulkiness of the ortho substituents. Steric effects arise from the fact that each atom within a molecule occupies a certain amount of space. If atoms are brought too close together, there is an associated cost in energy due to overlapping electron clouds (Pauli or Born repulsion), and this may affect the molecule’s preferred shape (conformation) and reactivity. Taking into account the existence of spatial induction, like para position, together with steric hindrance it is not very astonishing that a descriptor weighted by van dar Waals volume be more important than the one weighted by polarizability. Meanwhile resonance effect in
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
this position was also noticeable by the selection of R8p+ (A GETAWAY descriptor weighted by atomic polarizability) in the first step of performing MLR (Table 3). E2v shows 0.76% correlation with R8p+. 4. Conclusion A series of ortho, meta and para substituted Schiff bases were studied for the effects of solvent and substituent on electronic absorption spectra using chemometrics tools. Whilst MLR analysis resulted in different QSPR models in different solvents, FA produced a unique model for each subset of substituted molecules in all solvents. The study of solvent effect by both methods showed that diploarity/polarzability of the solvent was the most important factor. Hypsochromic shift was observed for all substituents (due to n → * electronic transition) except o-NO2 which showed bathochromic shift (due to → * transition). For the substituent effect descriptors related to polarizability or van der Walls volume were found to be important, emphasizing the main effect of polarity/polarizability interactions between solute and solvent on the electronic transition of the studied Schiff basses. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.saa.2012.01.023.
205
References [1] C.M. da Silva, D.L. da Silva, L.V. Modolo, R.B. Alves, M.A. de Resende, C.V.B. Martins, Â. de Fátima, J. Adv. Res. 2 (2011) 1–8. [2] K.C. Gupta, A.K. Sutar, Coord. Chem. Rev. 252 (2008) 1420–1450. [3] S. Kumar, D.N. Dhar, P. Saxena, J. Sci. Ind. Res. 68 (2009) 181–187. [4] D.L. Pavia, G.M. Lampman, G.S. Kriz, Introduction to Spectroscopy. A Guide for Students of Organic Chemistry, second ed., Saunders College Publishing, Philadelphia, 1996. [5] C. Reichardt (Ed.), Solvents and Solvent Effects in Organic Chemistry, third ed., Wiley-VCH, Germany, 2003. [6] H. Naemi, Ph.D Thesis, Shiraz University, Shiraz, Iran, 1999. [7] B. Hemmateenejad, L. Emami, H. Sharghi, Spectrochim. Acta Part A Molecular and Biomolecular Spectroscopy 75 (2010) 340–346. [8] C. Andraud, I.V. Kityk, H. Kutawara, G. Lemercier, K. Stromberger, M. Alexandre, W. Gruhn, K.S. Pak, Mater. Lett. 57 (2003) 1489–1495. [9] G. Lemercier, M. Alexandre, C. Andraud, I.V. Kityk, Chem. Phys. 298 (2004) 299–306. [10] R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Germany, 2000. [11] R.G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons Inc., England, 2003. [12] B. Hemmateenejad, M. Yazdani, Anal. Chim. Acta 634 (2009) 27–35. [13] B. Hemmateenejad, M. Elyasi, Anal. Chim. Acta 646 (2009) 30–38. [14] B. Hemmateenejad, R. Sabet, A. Fassihi, Chem. Biol. Drug Des. 74 (2009) 405–415. [15] M.J. Kamlet, J.L.M. Abboud, M.H. Abraham, R.W. Taft, J. Org. Chem. 48 (1983) 2877–2887. [16] E.R. Malinowski, Factor Analysis in Chemistry, third ed., Wiley-Intersciences, New York, 2002. [17] K. Kitamura, K. Hozumi, Anal. Chim. Acta 201 (1987) 301–304. [18] A. Savitzky, M.J.E. Golay, Anal. Chem. 36 (1964) 1627–1639. [19] M. McCue, E.R. Malinowski, Anal. Chim. Acta 133 (1981) 125–136. [20] E.R. Malinowski, M. McCue, Anal. Chem. 49 (1977) 284–287. [21] D.A. Skoog, F.J. Holler, T.A. Nieman, Principles of Instrumental Analysis, fifth ed., Harcourt Brace & Company, Philadelphia, 1998.
Contents lists available at SciVerse ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Effects of solvent and substituent on the electronic absorption spectra of some substituted Schiff bases: A chemometrics study Bahram Hemmateenejad a,b,∗ , Mahdieh Yazdani a , Hashem Sharghi a a b
Department of Chemistry, Shiraz University, Shiraz 71454, Iran Medicinal & Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
a r t i c l e
i n f o
Article history: Received 3 November 2011 Received in revised form 7 January 2012 Accepted 16 January 2012 Keywords: Schiff base Absorbance spectra Solvatchromism Solvent Chemometrics
a b s t r a c t A series of Schiff bases were studied for their delicate changes in absorption electronic spectra by changing substituents and solvents. UV/vis absorbance spectra of Schiff base derivatives of different substituents ranging from electron withdrawing to electron donating (Br, CF3 , Cl, CN, CO2 H, F, Me, NO2 , OH, OMe, H) were studied in different solvents (acetonitrile, chloroform, cyclohexane, dioxane, dimethylsulfoxide and methanol). Linear relationships were established to investigate the effect of solute structure and solvatochromic parameters of solvents on the absorbance spectra. Meaningful chemical factors and then regression models were provided utilizing factor analysis (FA) and multiple linear regression (MLR). It was found that the frequency of maximum absorbance was mainly controlled by the solvent’s dipolarity/polarizability. The max of the ortho-nitro derivative represented the largest dependency on solvents’ polarity/polarizability so that it can be used as a solvatochromic probe. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Schiff bases are of the general formula of R1 R2 C N-R3 , where R3 is an aryl or alkyl group that makes the Schiff base a stable imine. Many biologically important Schiff bases have been also reported in the literature possessing antibacterial, antifungal, antimicrobial, anticonvulsant, anti HIV, anti- inflammatory and antitumor [1]. In addition to biological and pharmacological applications, these compounds are potent for analytical applications. Therefore, they have been extensively studied for their photometric and thermodynamic properties and also proton transfer tatumeric equilibria [2,3]. These increasing applications show a need to concentrate more on their specific characteristics. Since many analytical applications of these compounds involve color changes (or spectrophotometric studies), we are interested on experimental and theoretical investigations on the color changes of these compounds. In general, when absorption spectrum of a chemical species is measured in solvents of different polarity it is usually found that the position, intensity and shape of absorption bands are usually affected by the solvents. These changes are a result of physical
∗ Corresponding author at: Department of Chemistry, Shiraz University, Shiraz 71454, Iran. Tel.: +98 711 613 7360; fax: +98 711 228 6008. E-mail address: [email protected] (B. Hemmateenejad). 1386-1425/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2012.01.023
intermolecular solute–solvent interaction forces which tend to alter the energy difference between ground and excited state of the absorbing species containing the chromophore. The term solvatochromism is used to describe the pronounced change in position (and sometimes intensity) of an UV/vis absorption band, accompanying a change in the polarity of the medium. A hypsochromic (or blue) shift, with increasing solvent polarity, is usually called negative solvatochromism. The corresponding bathochromic (or red) shift is termed positive solvatochromism [4,5]. We have recently synthesized some novel Schiff base compounds [6] and investigated their complex formation and acid–base equilibria by spectrophotometric methods [7]. In these studies we observed that the color of Schiff bases is changed considerably by changing in the substituents and also by dissolving them in different solvents. In fact, the Schiff base chromophore derivatives show substantial photoinduced effects and the electron–vibration interactions including anharmonic ones play the principal role [8,9]. In the present work, we aimed to study the effects of substituent and solvent on the synthesized Schiff bases through spectra-structure and spectra-solvent relationships. The effects of substituents have been investigated using QSPR (quantitative structure–property relationship) analysis utilizing molecular descriptors [10]. In addition, solvent effects were studied concerning solvent parameters (namely hydrogen bond donating ability (˛), hydrogen bond accepting ability (ˇ) and dipolarity/polarizability (* )). Finally multiple linear regression (MLR) and factor analysis (FA) were used to find the meaningful chemical factors and provide the regression models.
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
The longest wavelength of maximum absorbance (max ) was carefully determined using Savitsky–Golay second derivatives [11] of the calculated pure spectra. The wavelengths were converted to wavenumbers (max ) before being used in the regression analysis.
B A
R3
199
N HO
2.3. Procedures for data analysis
R2 R1 Ortho-substituted; R1= Br, CF3, Cl, CN, CO2H, Me, NO2, OH, OMe Meta-substituted; R2= Br, Cl, CN, CO2H, F, NO2, OMe Para-substituted; R3= Br, Cl, CN, CO2H, F, Me, NO2, OMe Fig. 1. The structure of Schiff bases used in this study.
2. Experimental 2.1. Reagents A series of laboratory synthesized Schiff bases [6], containing different substituents ranging from electron withdrawing to electron donating (Br, CF3 , Cl, CN, CO2 H, F, Me, NO2 , OH, OMe, H) in ortho, meta and para positions of one of the aromatic ring (ring A) were used for this study (see Fig. 1). Furthermore, extra pure organic solvents, provided from Merck chemical company, were used as received. The compounds powders were highly stable so that the solutions prepared from each compound about one year later showed about 5% reduction in absorbance. 2.2. Procedure for determination of max A solution of 0.001 M of each compound was prepared in each of the studied solvents and then 10.0 L aliquots of the solution was injected into the 10 mm quartz cell that was initially filled with 2.0 mL of the same solvent. After each injection, the electronic absorption spectrum was measured by a Hewlett Packard diode array spectrophotometer (model HP8452A) with spectral resolution of 1.6 nm. In each titration, 24 injections were made to obtain different concentrations of a solute in a specified solvent. By this manner, a data matrix was obtained for each molecule, the rows and column of which were solutions of different concentrations and different wavelengths, respectively (see Supplementary materials Fig. S1 for schematic representation of data array for each solute in each solvent). Consequently for each molecule, six data matrices consisting of their spectra of different concentrations in the six aforementioned solvents were obtained. Furthermore, because ortho-NO2 substituent shows drastic shifts by changing solvents, its spectrum was recorded in 6 extra solvents (i.e., DMF, ethyl acetate, n-heptane, n-hexane, THF, water) in order to be able to see the corresponding shifts more tangible. A total of 25 molecules were studied in 6 solvents and hence 150 data matrices were obtained. These data matrices were used to calculate the pure absorbance spectra (molar absorptivity) of the studied solutes in different solvents, and then wavelength of maximum absorbance. The pure absorbance spectrum was determined by least square regression of the recorded absorbance spectra against solute concentration according to the Beer–Lambert law: A = cs
(1)
−1 sˆ = c+ A = (c c) c A
(2)
where A is the collected absorbance data matrix of the individual solutes in a solvent, c is the vector of the concentrations of solute and s is the molar absorptivity spectrum. The superscripts (+), ( ) and (−1) denote pseudo-inverse, transpose and inverse of a matrix, respectively. The superscript (ˆ) denotes the calculated values.
2.3.1. Software Molecular modeling and geometry optimization were performed by Hyperchem (version 7.0). Electronic descriptors were obtained from this software. Furthermore, Dragon software (version 2.1) was used for calculation of 18 different sets of descriptors. SPSS software (version 12, SPSS Inc.) was also used for MLR analysis. Other calculations like FA and calculations of molar absorbtivity coefficients were performed in MATLAB (version 7.0, MathWorks, Inc.) environment. All computations were performed on a Pentium IV personal computer (CPU 1.70 GHz) with windows XP operating system. 2.3.2. Data set The response data analyzed in this work was the wavenumber of maximum absorption (max ), which was obtained for 25 solutes in 6 solvents. The data were arranged in a (25 × 6) data matrix (see Fig. S2 of supplementary materials). For each type of substitution position (i.e., ortho, meta and para) a separate sub-matrix was extracted and used for further analysis. 2.3.3. MLR modeling for calculation of substituent effect Each column of the matrix shown in Fig. S2 represents the variations in the max of solute over changing in the substitution in a specified solvent. The aim is to relate these changes to a meaningful molecular descriptor. Therefore, for each of the 6 columns (in each data set individually), a separate regression model was obtained using molecular descriptors calculated by Dragon and Hyperchem software. Since the number of descriptors was much higher than the number of molecules, we used two-step stepwise regression to select the most relevant subset of descriptors for each solvent and to obtain meaningful models having as lower number of variables as possible [12–14]. Firstly, all descriptors were divided into different groups (according to the classification made in Dragon software) and then a separate stepwise MLR was developed for each group of descriptors. After evaluating the models by cross-validation and selecting the best model of each group, the variables appeared in the models were used as input of another stepwise selection model. 2.3.4. MLR modeling for calculation of solvent effect Each row of the data matrix shown in Fig. S2 represents the changes in the max of an individual solute in different solvents. The aim is to relate these changes to solvatochromic parameters of the solvents [15]. Table 1 lists the solvatochromic parameters (hydrogen bond donor ˛, hydrogen bond acceptor ˇ, and dipolarity/polarizability * ) for the employed solvents.
Table 1 Solvatochromic parametes of the solvent used. Solvents
␣
ˇ
*
Acetonitrile CHCl3 Cyclohexane Dioxane DMSO Methanol
0.19 0.44 0 0 0 0.93
0.31 0 0 0.37 0.76 0.62
0.75 0.58 0 0.55 1 0.6
200
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
Fig. 2. The calculated pure spectra of all Schiff bases used in this study in DMSO and chloroform solvents.
Thus, for each row (in each data set individually), a regression model was obtained by stepwise selection of the solvatochromic parameters.
3. Results and discussion
2.3.5. Factor analysis procedure Factor analysis (FA) and/or principal component analysis (PCA) are multivariate techniques for reducing matrices of data to their lowest dimensionality by the use of orthogonal factor space and transformations that yield predictions and/or recognizable factors. Scientist do not always distinguish between FA and PCA, but for chemists factors often have a physical significant, therefore FA is a procedure to relate the abstract factors or principal components (PCs) to chemical factors (e.g., solute or solvent properties) [11,16]. Instead of analysis of the behavior of each solute in different solvent or analysis the effect of the structure of a list of solutes in one solvent by MLR, FA provide a bilinear decomposition of the wavenumber data matrix of all solutes in all solvents to develop multi-linear models for all solutes or all solvents [16]. The wavenumber data matrices for each set of ortho, meta and para substituents were firstly subjected to PCA using singular value decomposition (SVD). The numbers of principal factors were determined utilizing different criteria such as indicator function, scree plot, PRESS and so on. Target transformation factor analysis (TFA) was employed to separate the solvent and solute effects and then to obtain regression equations for all solute and all solvents, simultaneously. All the solvent and solute parameters used in MLR analysis were tested one-by-one as target. The score matrix (which was the row matrix) was correlated with the molecular descriptors and the loading matrix (which was the column matrix) was correlated with the solvatochromic parameters.
For each solute in each solvent, a data matrix was provided from the digitized absorbance data as a function of concentration. A selection of the absorbance spectra are given in Fig. S3 of the supplementary materials. This data matrix was then regressed over the solute concentration to obtain pure spectrum of that solute in the considered solvent. It is obvious that these spectra are independent of solute concentration in the studied concentration ranges. As an example the calculated pure spectra of each solute in two solvents of high and low polarities (i.e., chloroform and DMSO) are represented in Fig. 2. Obviously, the shape of pure spectra is highly dependent upon substitution pattern as well as the solvent. Whilst most molecules represent a collection of overlapped peaks, some of them represent distinct absorbance peak. This behavior is not only dependent on type and characteristics of substituents but also it dependents on the substitution position. Due to the highly spectral overlapping of the peaks, it was very difficult to detemine the max of each peak accurately. Actually, in solvatochromic studies the max of the longest wavelength is usually studied. As it is observed from the shown pure spectra in Fig. 2, the peaks of the longest wavelength in almost all cases are also overlapped with the peaks of the shorter wavelengths. There are some different approaches for accurate determination of peak position, among which first and second derivatives are more popular [11]. In the first derivative method, which is more useful for non-overlapping or low-overlapping peaks, the derivative approches zero at the max and thus the wavelengths at zero crossing are considered the max . This method does not give highly
3.1. Pure spectra and max
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
201
Table 2 max of the longest wavelength for the ortho, meta and para substituted derivatives in different solvents (nm). No
Substituent
Acetonitrile
CHCl3
Cyclohexane
Dioxane
DMSO
Methanol
O01 O02 O03 O04 O05 O06 O07 O08 O09 O10 M01 M02 M03 M04 M05 M06 M07 P01 P02 P03 P04 P05 P06 P07 P08
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-OH o-OMe o-H m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2 p-OMe
347 344 347 351 337 342 402 355 353 340 342 342 342 340 342 344 346 344 344 351 348 340 344 365 352
351 347 351 353 338 344 398 368 368 344 348 348 348 346 346 348 350 350 350 354 352 344 346 368 354
352 350 352 354 NS 346 378 NS 351 347 350 350 NS NS 348 NS 352 353 353 355 NS 349 348 NS 354
351 347 351 353 338 344 398 363 370 344 348 348 348 346 346 348 350 350 350 354 352 344 346 368 354
334 340 333 336 334 338 419 366 364 337 336 335 331 334 334 332 344 341 341 334 335 340 344 391 355
349 345 349 352 337 343 404 358 352 341 344 344 344 341 344 346 348 346 346 352 349 342 344 367 352
NS: The compound is not soluble in that solvent.
accurate results for peaks of serious overlapping. In this case, second derivative method is more useful. If two overlapped peaks represents a single absorbance maxima, their second derivatives produces two peaks maxima or minima at the corresponding max . We used the Savitsky–Golay method for derivative calculations [17,18]. The estimated max values of the longest wavelength are represented in Table 2. By scanning this Table, even vertically or horizontally, the variations of max by changing substituents and solvents are evident. These changes can be explained by selecting suitable molecular descriptors or solvent parameters. Besides, the molar absorptivity of the solutes at the calculated max was also calculated and the results are given in Table S1. Dependency of molar absorption on the substituent and solvent is also evident from Table S1 and Fig. 2. However, it was difficult to explain it using mathematical models. Before bringing out the quantitative results of MLR and FA in the substituent effect, a discussion about the qualitative results seems to be suitable. It is seen from Table 2 that the maximum absorption wavelength for ortho and para substituents shifts to longer wavelengths for CN, CO2 H, NO2 , OH and OMe groups with respect to other substituents whereas this phenomenon does not observe for meta substituents. This behavior can be simply attributed to the resonance of the mentioned substituent with the aromatic rings of the Schiff bases. In the molecular-orbital treatment, electrons are considered to be further delocalized by conjugation. The effect of this delocalization is to lower the energy level of the * orbital and give it less antibonding character. Absorption maxima are therefore shifted to longer wavelengths as a consequence of conjugation [4]. Thus, this provided data of UV/vis absorbance confirm the fact that meta substituents in an aromatic ring does not participate in resonance in contrast to ortho and para substituents. (It should be noted that for clarification in this text and * symbols has been used for molecular orbitals and * for the polarity of the solvent). 3.2. MLR results According to the solvatochromism point of view [4,5], max values were converted to max values and used for statistical analysis. As it was mentioned before, for each set of ortho, meta
and para compounds separate models were obtained. Firstly the effects of molecular structure were investigated. The final obtained QSPR models in each solvent are summarized in Table 3. It is observed that for all molecules mono-parametric equations have been obtained and the correlation coefficient of multiple determination is varied between 0.64 and 0.97. The variables selected for QSPR models are different for different substitution position. This suggest that the effect of substituent on max is dependent on its position. In addition, the selected variables are more or less different in different solvent. This means that the effect of solute on max could be also dependent on the type of solvent. To study the solvent effects by MLR, individual equations were derived for each solute by regressing max against solvatochromic parameters of the solvent. The results are represented in Table 4. Obviously, the solvent dipolarity/polarizability is the only solvatochromic parameter that has been appeared in all equations. Unlike substituent effect, the MLR equations of the solvent have high statistical quality and for limited number of solutes R2 is less than 0.80. In these equations, * has positive coefficient for all substituents, expect o-NO2 . The positive coefficients explain that by increasing in the solvent polarity, the energy need to excite the electrons to higher level orbital is increased. Consequently, blue spectral shift was observed by increasing in solvent polarity. The negative coefficient of o-NO2 will be discussed later. 3.3. Models obtained by FA By MLR analysis, QSPR models were generated for a list of solutes in individual solvents and also solvatochromic models were created for individual solutes. Actually, MLR can analyze only a vector of response (e.g., each column or row of the wavenumber data matrix) at each time. One main drawback of MLR analysis for multiresponse data is the inconsistency between the obtained equations for different situations, such as different QSPR models obtained here in different solvents. This makes the interpretation of the results difficult. However, by FA it is possible to analyze a multiresponse data and therefore generate a unique model for the set of responses at all conditions (e.g., providing QSPR models having the same variables in all solvents). Note that in this case we have
202
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
Table 3 MLR models obtained for investigating the effect of molecular structure on the frequency of absorption of the longest wavelength. Subset
Solvent
Equation
R2
ortho
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 32096.68 (±365.83) − 9292.25 (±1386.98) E2v max = 30262.15 (±409.34) − 26391.25 (±8821.98) R8p+ max = 31439.38 (±718.35) − 8408.61 (±2431.74) E2v max = 31358.42 (±671.01) − 8621.33 (±2271.48) E2v max = 31358.42 (±671.01) − 8621.33 (±2271.48) E2v max = 35982.89 (±2006.66) − 10836.74 (±2928.31) Mv
0.65 0.64 0.71 0.74 0.74 0.77
meta
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 30451.28 (±174.98) + 8830.99 (±2181.38) Mor22v max = 18494.02 (±1964.03) + 14083.92 (±2585.49) SIC2 max = 29226.78 (±48.66) + 3297.89 (±652.89) Mor22v max = 28874.43 (±37.84) + 1969.50 (±507.67) Mor22v max = 28874.43 (±37.84) + 1969.50 (±507.67) Mor22v max = 22695.44 (±2963.86) + 7752.58 (±3909.61) SIC2
0.81 0.86 0.84 0.75 0.75 0.66
para
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
max = 27954.55 (±147.62) − 3010.31 (±93.41) RDF120v max = 29222.34 (±92.64) − 1394.27 (±288.65) RDF120v max = 30667.99 (±431.55) − 9174.11 (±2096.22) Mor30p max = 35681.54 (±2104.14) − 3388.74 (±1002.68) GATS4p max = 35681.54 (±2104.14) − 3388.74 (±1002.68) GATS4p max = 28650.44 (±119.81) − 1809.37 (±556.78) Mor30p
0.97 0.82 0.79 0.69 0.69 0.78
three data matrices; (i) data matrix of response variables, D, (ii) a data matrix of molecular descriptors, (X) and (iii) a data matrix of solvent properties (Y). FA is performed on D and then the score and loading matrices of D are related to X and Y, respectively. Consider the arrangement of the wavenumber data in a data matrix D as shown in Fig. S2 so that the rows are related to the solute structure and the columns are related to solvent property. Application of PCA on this data matrix results in score (T) and loading (P ) matrices spanning the row and column spaces of the original data matrix, respectively. The non-modeled data by PCA model are included in matrix E, which has the same dimension as D: D = TP + E
(3)
The first step in FA is to determine the number of chemical factor or chemical dimensionality of the response data matrix D (or the number of columns in T and P). For all subsets of molecules, data matrices were found to be explained by two chemical factors.
The matrices T and P are pure mathematical factors and the next step is to transform these factors to chemically meaningful factors using solute descriptors (X) and solvent properties (Y) as suitable targets for T and P, respectively. This was done via target factor analysis (TFA). Apparent error in target (AET) was used to select the most suited targets [16,19]. The targets, which represented the lowest AET for investigating the solute structure, are listed in Tables S2–S4 of supplementary materials. Unity vector, which is used to account the effect of offset or intercept, was found as one of the significant target for all subsets. Beside the unity vector, the molecular descriptors, which represented the least AET were E2v, Mor22v and Mor30p for ortho, meta and para subsets, respectively. This means that for the meta substituted derivatives, the descriptors E2v is the most significant parameters in all solvents and hence in all solvents the effect of molecular structure for ortho-substituted derivatives can be written as max = bx0 + bx1 E2v. Similarly, the respective QSPR models for meta and para subsets can be written as max = bx0 + bx1
Table 4 MLR models for investigating the efect of solvent on max for different solutes.a Subset
Substituent
Equation
ortho
R2
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-H
*
max = 26665.85 (±303.73) + 3178.93 (±424.36) max = 28477.48 (±101.58) + 807.61 (±155.47) * max = 26547.28 (±338.55) + 3375.12 (±473.02) * max = 26481.97 (±438.24) + 3134.59 (±612.29) * max = 29172.10 (±93.44) + 746.67 (±130.56) * max = 28801.94 (±100.24) + 634.97 (±153.42) * max = 26475.35 (±170.51) − 2486.03 (±260.98) * max = 28733.99 (±113.45) + 851.98 (±173.64) *
0.95 0.87 0.94 0.90 0.92 0.81 0.98 0.86
meta
m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe
max = 28358.21 (±225.74) + 1139.33 (±345.50) * max = 28333.16 (±246.94) + 1208.05 (±377.95) * max = 26981.35 (±269.27) + 3185.50 (±376.22) * max = 27849.47 (±411.43) + 2078.60 (±574.84) * max = 28497.74 (±236.03) + 1092.61 (±361.25) * max = 26999.01 (±274.49) + 3036.83 (±383.51) * max = 28322.22 (±102.33) + 668.31 (±156.61) *
0.73 0.72 0.96 0.81 0.70 0.95 0.82
para
p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2
max = 28208.47 (±160.46) + 1010.82 (±245.59) * max = 28208.47 (±160.46) + 1010.82 (±245.59) * max = 26110.21 (±480.37) + 3673.95 (±671.16) * max = 26651.17 (±336.42) + 3104.03 (±470.02) * max = 28665.51 (±85.09) + 822.71 (±130.23) * max = 28745.68 (±63.38) + 366.18 (±97.01) * max = 26530.46 (±105.42) + 1157.74 (±168.74) *
0.81 0.81 0.91 0.94 0.91 0.78 0.96
a
o-OH, o-OMe and p-OMe did not show significant relation with any of the solvatochromic parameters.
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
203
Table 5 Regression coefficients of the QSPR model of (max = bx0 + bx1 x)a obtained by TFA for accounting the effect of molecular structure. Subset
Solvent
bx0
bx1
ortho
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
28845 31838 32071 31906 31906 31117
3142 −9443 −10564 −10492 −10492 −8362
meta
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
30638 29329 29161 28849 28849 28683
13686 2353 2169 1526 1526 1509
para
DMSO Acetonitrile Methanol CHCl3 Dioxane Cyclohexane
20239 30683 30930 30927 30927 30545
44495 −8772 −10458 −11542 −11542 −10586
a
x is E2v, Mor22v and Mor30p for ortho, meta and para subsets, respectively.
Mor22v and max = bx0 + bx1 Mor30p. The model constants, which were easily calculated by target transformation [20] are listed in Table 5. The plot of predicted max by the suggested model against the experimental value for all solutes in all solvents resulted in an stright line with squared correlation coefficient of 0.99. To investigate the solvent effect by TFA, the solvatochromic parameters were projected on the loading space. The AET calculated for each solvatochromic parameters are given in Tables S5–S7 of supplementary materials. Obviously, unity vector and solvent polarity/polarizability can be selected as meaningful factors. Thus, similar to MLR analysis, the solvatochromic parameter * was considered as the most significant solvent property effecting the shift in the longest max . According to these observations and in the similar manner as MLR, the effect of solvent on the max of all studied compounds can be described by the equation of the form of max = by0 + by1 * . The calculated regression coefficients by TFA are listed in Table 6. Table 6 Regression coefficients of the solvatochromic model of (max = by0 + by1 * ) obtained by TFA for accounting the effect of solvent. Subset
Substituent
by0
Ortho
o-Br o-CF3 o-Cl o-CN o-CO2 H o-Me o-NO2 o-H
26508 28100 26372 26266 29126 28398 26759 28412
by1 3405 1322 3626 3444 813.2 1186 −2887 1290
Meta
m-Br m-Cl m-CN m-CO2 H m-F m-NO2 m-OMe
27597 27455 26880 27803 27545 26822 28070
2172 2401 3331 2146 2393 3290 1006
para
p-Br p-Cl p-CN p-CO2 H p-F p-Me p-NO2
27872 27872 25785 26450 28834 28841 26610
1460 1460 4141 3392 584 233 1176
Fig. 3. (a) The recorded spectra of o-NO2 derivative in 12 solvents and (b) changes in max of this molecules against * scale of solvents.
A comparison between the results of MLR and FA would be interesting. Concerning with effect of solute structure, FA resulted a unique model for all solvents whereas MLR did not. However, one can find some consistency between the models produced by two methods. For example, MLR selected E2v as input variable for the QSPR models of the ortho-substituted in most of the solvents and FA detected this descriptor as the most important one. Similarly, for the meta-substituted molecules, the descriptor Mor22v, which selected by MLR in many solvents, was also identified as the most important one. Moreover, both MLR and FA identified * as the most important solvent parameter. A comparision between the regression coefficients of the solvatochoromic model calculated by MLR and FA suggest that although there is some differences between two calculated values they are almost similar. And another interesting point is that both methods calculated negative coefficients of * for o-NO2 . 3.4. Comments on the selected solvatochromic parameter MLR and FA results showed that solvent dipolarity/polarizability is the most important factor which affects the maximum absorption wavelengths by changing the solvents. There are, however, some points to be precisely delineated. The regression coefficient of * is one of the important points to pay attention. In all cases the regression coefficients are positive except for o-NO2 substituent. As it was noted previously, the positive coefficient confirms that n → * electron transition is responsible for such an absorbance peak. This is the characteristics of the C N functional group in the molecule. The o-NO2 substituent, however, does not obey this trend. To be sure about the results obtained for this molecule, the pure spectrum of this molecule was investigated in 6 extra solvents. Fig. 3 shows the absorbance spectra of o-NO2 in all studied solvents and also the changes in and max of maximum absorption of this molecule over * . The wavelengths of maximum absorbance for o-NO2 in the set of 12 solvents are listed in Table S8 of supplementary materials. Again a negative relationship with a high degree of linearity (R2 = 0.92) is observed between max and * . It can be concluded that unlike other
204
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
derivative, the transition responsible for the longest wavelength absorbance maxima of o-NO2 is → * . The studied Schiff base molecules are potent to have two major transitions: n → * and → * . Among these electronic transitions, n → * needs lower energy and will be located in the longest wavelength region of the spectrum. However, some substituents having nonbonding electrons on N, O or halogen atoms, exert a significant hypsocromic shift on this transition and on the other hand exert a weaker bathochromic shift on → * transition. The bathochromic shift is due to the mentioned resonance effect and the hypsochromic shift is due to the induction effect of the N, O and halogen atoms. These atoms pull the nonbonding electrons of N in C N group (in the Schiff bases) toward themselves and the outcome results is that the nonbonding electrons of N in the C N group will be held more strongly with respect to the situation in which no induction effect exists. Thus, in some cases it is possible that n → * transition falls under the → * transition [4,21]. NO2 group in the ortho position of the aromatic ring, with a nitrogen and two oxygen atoms and also nonbonding electrons, is very potent to exert these shifts and exchange the positions of the transitions in the corresponding spectrum. In addition to the above behavior of o-NO2 derivative, it also represented the largest change in max in solvents of lowest to highest polarity (i.e., 43 nm shift in max from n-hexane to DMSO). Thus, it can be used as a new probe of solvent polarity/polarizability in solvatochromism studies. 3.5. Comments on the selected descriptors (a discussion about substituent effect) 3.5.1. Para substituted derivatives Initial regression results (MLR modeling) showed that by changing substituents in para molecules three descriptors [10] (RDF120v, Mor30P, GATS4p) showed relationship with max . FA results, however, revealed that Mor30p (a 3D-MoRSE descriptor) is the most significant one. 3D-MoRSE descriptors (three dimension molecule representation of structures based on electron diffraction) are molecular descriptors calculated by summing atomic weights viewed by a different angular scattering function. A typical MoRSE descriptor is denoted by M or sw where s and w take the values 1 ≤ s ≤ 32 and w ∈ {u, m, v, e, p} where u is unweighted, m is weighted by mass, v is weighted by van der Waals volume, e is weighted by electronegativity, p is weighted by polarizability. Thus, Mor30p is weighted by polarizability. The term polarizability describes a molecular property having to do with deformability of a bond. Polarizability is the relative tendency of a charge distribution, like the electron cloud of an atom or molecule, to be distorted from its normal shape by an external electric field, which may be caused by the presence of a nearby ion or dipole [21]. The main factor determining the polarizability is ground state dipole moments. A molecular polarizability effect occurring by an intramolecular electron displacement (sometimes called the “conjugative mechanism” and, previously, the “tautomeric mechanism”) characterized by the substitution of one electron pair for another within the same atomic octet of electrons. It can be indicated by curved arrows symbolizing the displacement of electron pairs, as in
which represents the hypothetical electron shift
This implies the fact that para substituents exert their influence on the chromophore directly by resonance. During resonance there is an electronic movement in the molecule. The double bond become a single bond and vice versa. There will be positive and negative charges on electropositive and electronegative atoms for a moment and the whole process lead to polarizability. A point is important to mention here: GATS4p and RDF120v are the descriptors which were selected in the first step of MLR but did not enter the final model (Table 5). GATS4p (a 2D-autocorrelation descriptor) is weighted by atomic polarizability and therefore confirm the effect of Mor30p descriptor. RDF120v is among RDF descriptors and is weighted by atomic van der Waals volumes. Appearing this descriptor, even in the first step of regression, conceptualize that the effect of para position could also be in the form of spatial induction if the volume of the substituents becomes a predominant factor. This point is important since the selected descriptors in meta and ortho case are weighted by atomic van der waals volumes, as will be discussed afterward. 3.5.2. Meta substituted derivatives MLR and FA results showed that Mor22v (a 3D-Morse descriptor weighted by atomic van der Waals volumes) is the most significant descriptor for meta-substituted derivatives. Substituents in meta position exert their influence on the reaction center by field effect not resonance. Selection of this descriptor validate that the influence of meta-position is feasible in the course of a spatial induction (field effect). 3.5.3. Ortho substituted derivatives For this subset, E2v as one of the WHIM descriptors was selected. WHIM descriptors are 3D molecular descriptors obtained as statistical indices of all the atoms projected onto the three principal components calculated from weighted covariance matrices of the atom coordinated. WHIM approach can be viewed as a generalization searching for the principal axes with respect to a defined atomic property (the weighting scheme). The following weighting schemes are used for computing the weighted covariance matrix, Sw : unweighted (u), atomic masses (wi = mi ), atomic van der Waals volumes (wi = vi ), atomic Sanderson electronegativities (wi = ei ), atomic polarizabilities (wi = pi ) and atomic electro topological states (wi = si ). E2v is the second component derived from the van der Waals volumes weighted covariance matrix. Two points are important to mention here: Firstly, E2v is weighted by atomic van der Waals volume. Secondly, the second PC of the aforementioned matrix shows a good relation with the wave numbers. Take into consideration the fact that the first PC usually contains the most variance, it is concluded that some other factors affect the results in this position. Steric hindrance is one of the aspects that can be discussed here. The factors that cause the steric hindrance were considered to be the bulkiness of the ortho substituents. Steric effects arise from the fact that each atom within a molecule occupies a certain amount of space. If atoms are brought too close together, there is an associated cost in energy due to overlapping electron clouds (Pauli or Born repulsion), and this may affect the molecule’s preferred shape (conformation) and reactivity. Taking into account the existence of spatial induction, like para position, together with steric hindrance it is not very astonishing that a descriptor weighted by van dar Waals volume be more important than the one weighted by polarizability. Meanwhile resonance effect in
B. Hemmateenejad et al. / Spectrochimica Acta Part A 91 (2012) 198–205
this position was also noticeable by the selection of R8p+ (A GETAWAY descriptor weighted by atomic polarizability) in the first step of performing MLR (Table 3). E2v shows 0.76% correlation with R8p+. 4. Conclusion A series of ortho, meta and para substituted Schiff bases were studied for the effects of solvent and substituent on electronic absorption spectra using chemometrics tools. Whilst MLR analysis resulted in different QSPR models in different solvents, FA produced a unique model for each subset of substituted molecules in all solvents. The study of solvent effect by both methods showed that diploarity/polarzability of the solvent was the most important factor. Hypsochromic shift was observed for all substituents (due to n → * electronic transition) except o-NO2 which showed bathochromic shift (due to → * transition). For the substituent effect descriptors related to polarizability or van der Walls volume were found to be important, emphasizing the main effect of polarity/polarizability interactions between solute and solvent on the electronic transition of the studied Schiff basses. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.saa.2012.01.023.
205
References [1] C.M. da Silva, D.L. da Silva, L.V. Modolo, R.B. Alves, M.A. de Resende, C.V.B. Martins, Â. de Fátima, J. Adv. Res. 2 (2011) 1–8. [2] K.C. Gupta, A.K. Sutar, Coord. Chem. Rev. 252 (2008) 1420–1450. [3] S. Kumar, D.N. Dhar, P. Saxena, J. Sci. Ind. Res. 68 (2009) 181–187. [4] D.L. Pavia, G.M. Lampman, G.S. Kriz, Introduction to Spectroscopy. A Guide for Students of Organic Chemistry, second ed., Saunders College Publishing, Philadelphia, 1996. [5] C. Reichardt (Ed.), Solvents and Solvent Effects in Organic Chemistry, third ed., Wiley-VCH, Germany, 2003. [6] H. Naemi, Ph.D Thesis, Shiraz University, Shiraz, Iran, 1999. [7] B. Hemmateenejad, L. Emami, H. Sharghi, Spectrochim. Acta Part A Molecular and Biomolecular Spectroscopy 75 (2010) 340–346. [8] C. Andraud, I.V. Kityk, H. Kutawara, G. Lemercier, K. Stromberger, M. Alexandre, W. Gruhn, K.S. Pak, Mater. Lett. 57 (2003) 1489–1495. [9] G. Lemercier, M. Alexandre, C. Andraud, I.V. Kityk, Chem. Phys. 298 (2004) 299–306. [10] R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Germany, 2000. [11] R.G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons Inc., England, 2003. [12] B. Hemmateenejad, M. Yazdani, Anal. Chim. Acta 634 (2009) 27–35. [13] B. Hemmateenejad, M. Elyasi, Anal. Chim. Acta 646 (2009) 30–38. [14] B. Hemmateenejad, R. Sabet, A. Fassihi, Chem. Biol. Drug Des. 74 (2009) 405–415. [15] M.J. Kamlet, J.L.M. Abboud, M.H. Abraham, R.W. Taft, J. Org. Chem. 48 (1983) 2877–2887. [16] E.R. Malinowski, Factor Analysis in Chemistry, third ed., Wiley-Intersciences, New York, 2002. [17] K. Kitamura, K. Hozumi, Anal. Chim. Acta 201 (1987) 301–304. [18] A. Savitzky, M.J.E. Golay, Anal. Chem. 36 (1964) 1627–1639. [19] M. McCue, E.R. Malinowski, Anal. Chim. Acta 133 (1981) 125–136. [20] E.R. Malinowski, M. McCue, Anal. Chem. 49 (1977) 284–287. [21] D.A. Skoog, F.J. Holler, T.A. Nieman, Principles of Instrumental Analysis, fifth ed., Harcourt Brace & Company, Philadelphia, 1998.