Application of FT-Raman and FTIR measurements using a novel spectral reconstruction algorithm

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1 JOURNAL OF RAMAN SPECTROSCOPY J. Raman Spectrosc. 2003; 34: Published online in Wiley InterScience ( DOI: /jrs.1054 Application of FT-Raman and FTIR measurements using a novel spectral reconstruction algorithm Su Ying Sin, Effendi Widjaja, Liya E. Yu and Marc Garland Department of Chemical and Environmental Engineering, 4 Engineering Drive 4, National University of Singapore, Singapore Received 23 October 2003; Accepted 2 April 2003 Recently, an advanced spectral reconstruction algorithm based on information entropy was developed to identify individual compounds contained in mixture spectra without recourse to any library or any apriori knowledge. In this study, standard mixtures containing various polycyclic aromatic hydrocarbons (PAHs) and a,!-dicarboxylic acids were measured by solid-state FT-Raman and FTIR spectroscopy, and this was followed by the application of the aforementioned algorithm to recover all pure component spectra contained therein. The results demonstrate that the developed algorithm successfully recovered the spectra of individual species even though only a very limited number of mixture spectral measurements were made. FT-Raman measurements coupled with chemometric analysis provides satisfactory pure spectral results for both PAHs and a,!-dicarboxylic acids. Additionally, FT-Raman measurements together with FTIR measurements provided even better spectral recovery for some of the a,!-dicarboxylic acids when using band-target entropy minimization (BTEM). This study demonstrates that chemometric analysis without aprioriinformation provides a promising solution and simplified approach to identify individual components from samples containing a complex composition. In particular, this is desired for a better understanding of environmental samples. Copyright 2003 John Wiley & Sons, Ltd. KEYWORDS: pure component spectral reconstruction; entropy minimization; FT-Raman; FTIR; complementary spectroscopic information; polycyclic aromatic hydrocarbons;,ω-dicarboxylic acids; chemometrics INTRODUCTION Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants in our environment, and have received increasing attention in recent years owing to their carcinogenic and/or mutagenic nature. In addition to natural emissions from forest fires and volcanic activities, anthropogenic activities contribute significantly to atmospheric PAHs. 1 Since airborne PAHs tend to condense on small particles, such as soot, and subsequently deposit into human lungs, their presence can substantially increase air toxicity and pose health concerns. Oxygenated organic compounds, in particular,ωdicarboxylic acids, have been identified in both urban and rural areas as one of the most prevalent compound classes produced from the formation of secondary organic aerosols. 2,3 Since polar components can significantly affect the chemical and physical properties of airborne aerosols, it is important to measure efficiently polar organic compounds Ł Correspondence to: Marc Garland, Department of Chemical and Environmental Engineering, 4 Engineering Drive 4, National University of Singapore, Singapore. chemvg@nus.edu.sg contained in atmospheric particulate matter. However, analyzing airborne aerosols usually suffers from material loss owing to tedious preparation procedures, resulting in an incomplete understanding of the pollution problems in the atmosphere. Therefore, developing a robust and simplified analytical method is highly desirable. A variety of analytical techniques have been employed to measure PAHs. 4,5 These studies showed that the Raman spectra of 11 PAHs (naphthalene, anthracene, tetracene, pentacene, rubrene, phenanthrene, chrysene, triphenylene, pyrene, perylene and fluoranthene) in the solid state were unique and highly specific, indicating considerable potential for species identification. Munro et al. 6 utilized UV resonance Raman (UVRR) spectroscopy to monitor trace PAHs in aqueous systems. Fourier transform (FT)-Raman spectroscopy has been used as one of the techniques for characterizing the chemical constituents of coal, focusing on the degree of polycondensation of PAHs. 7 Other applications of FT-Raman spectroscopy include study of the vibration modes of PAHs in order to understand the possible molecular mechanisms of PAH carcinogenesis 8 and identifying trace PAH monolayer contamination on a mechanically polished Ag surface. 9 Recently, fluorescence measurements coupled Copyright 2003 John Wiley & Sons, Ltd.

2 796 S. Y. Sin et al. with three-way analysis were used to provide qualitative information about components in PAH mixtures. 10 With respect to infrared (IR) spectroscopy, FTIR has been utilized to identify oxygenated compounds in aerosol samples. However, the spectrum can only indicate functional groups present without specifying the individual species involved. In this study, known and complex environmentally relevant mixtures were prepared and their spectra measured by FT-Raman or FT-Raman and FTIR techniques. The resulting mixture spectra were analyzed using a few entropydriven approaches including the novel algorithm band-target entropy minimization (BTEM) Individual standard compounds and standard mixtures containing various PAHs and,ω-dicarboxylic acids (representing a wide range of structural characteristics for atmospheric compounds) were selected to test the spectral reconstruction methods. These multivariate methods utilize only the raw spectral data and can satisfactorily reconstruct the spectra of individual compounds contained in the mixtures without any apriori information. Whereas other spectroscopic techniques may fail to resolve unknown individual constituents contained in multi-component mixtures, our results offer a promising and efficient approach, which is particularly applicable to actual environmental samples. EXPERIMENTAL Sample preparation and measurements Polycyclic aromatic hydrocarbons The standard PAHs tested consist of 2 6 condensed aromatic rings, and are commonly found in the environment. The five PAH standards were naphthalene (Aldrich, Milwaukee, WI, USA), phenanthrene (TCI Tokyo Kaisei, Tokyo, Japan), fluoranthene (TCI Tokyo Kaisei), pyrene (TCI Tokyo Kaisei) and coronene (Aldrich). To prepare PAH standard mixtures, weighed PAHs, ranging from 1 to 12 mg, were mixed and ground to a fine powder before being packed into the FT- Raman sample cup. All samples were examined in the solid state at ambient temperature without further purification. Table 1 summarizes the standard mixtures examined via FT-Raman spectroscopy, including the respective PAHs and corresponding weight ratios utilized in individual mixtures. Six,ω-dicarboxylic acids, oxalic acid dihydrate (C 2 ; Merck, Darmstadt, Germany), malonic acids (C 3 ;Aldrich), succinic acids (C 4 ; Aldrich), glutaric acids (C 5 ; Aldrich), adipic acids (C 6 ; Merck) and azelaic acids (C 9 ; Aldrich), were used. Table 2 displays the 12 standard mixtures consisting of various,ω-dicarboxylic acids tested by both FT-Raman and FTIR measurements. All 12 samples measured by both FT-Raman and FTIR spectroscopy are listed in Table 2. FT-Raman measurements FT-Raman spectra of the PAHs were obtained using a Bruker (Ettlingen, Germany) Equinox 55 instrument with an FRA 106/S attachment. The spectrometer utilized a Nd : YAG laser operating at a wavelength of 1064 nm [near-infrared (NIR)], and the Raman sampling compartment incorporated 180 collection optics. A secondary filter was used to remove the Rayleigh line before the scattered radiation reached the detector (InGaAs). In addition, a laser power level of 130 mw was used after trial-and-error optimization in order to produce good-quality spectra. A resolution of 4 cm 1 was used, with an aperture of 3.5 mm. To provide a satisfactory signal-to-noise ratio, only 2 20 scans per sample were needed for the PAH pure samples and mixtures, while 100 scans per sample were performed to measure pure and mixture standards of,ω-dicarboxylic acids. FTIR measurements Prior to FTIR measurements, solid standard samples were prepared by diluting the finely ground solid sample with powdered potassium bromide (KBr) in a ratio of ca 1 : 100 before preparing KBr sample discs with a diameter of 1.3 cm and a thickness of about 1 mm. Mid-infrared (MIR) spectra in the range cm 1 were recorded on a Bio-Rad (Boston, MA, USA) FTS-3500 FTIR spectrometer equipped with a DTGS detector. A resolution of 4 cm 1 was used and 20 scans were collected to obtain accurate results for measuring all the standard compounds and mixture standards. To utilize the coupled FT-Raman spectra and FTIR spectra fully, a solid sample was first examined via FT- Raman spectroscopy. Then powder held in the FT-Raman sample cup was used again to prepare a disc for subsequent FTIR measurements. Table 1. PAH mixtures for FT-Raman analysis No. of Mixture Weight ratio components no. n a Naphthalene Phenanthrene Fluoranthene Pyrene Coronene a n D Number of separate FT-Raman measurements performed by rotating the sample to a new position.

3 Novel spectral reconstruction algorithm 797 Table 2. Dicarboxylic acid mixtures for FT-Raman and FTIR measurements Weight ratio No. of Mixture components no. n a Oxalic Malonic Succinic Glutaric Adipic Azelaic a n D Number of separate FT-Raman measurements performed by rotating the sample to a new position. COMPUTATIONAL ASPECTS Minimum-entropy spectral reconstruction was initiated by Sasaki et al., 15,16 followed by Neal et al., 17 Brown and Harper 18 and Volkov. 19 Recently, we re-examined the entropy minimization approach for large-scale pure component spectral reconstruction. 20 It was found that an entropy function plus spectral dissimilarity approach together with a global search using Corana s SA effectively recovered the seven pure component spectra from synthesized mixture spectra. Basically, this technique works through a combination of singular value decomposition, entropy minimization and simulated annealing approaches. It starts with the principal component analysis of a data matrix followed by rotation of the resulting basis vectors into physically meaningful pure component spectra. In all of the following development, let I denote either Raman intensity or FTIR absorbance and let J denote either Raman emissivity or FTIR absorptivity. Next, let I kð represent an experimentally measured spectral data matrix, where k denotes the number of spectra and is the number of data channels associated within the experimental spectroscopic wavenumber range. I kð is a linear combination of a concentration matrix C kðs, the pure component emissivity/absorptivity matrix J sð and an experimental error matrix ε kð,wheresdenotes number of observable species in chemical mixture: I kð D C kðs J sð C ε kð The consolidated data matrix I kð is then subjected to singular value decomposition (SVD) [Eqn (2)], to obtain its abstract orthonormal matrices U kðk and V T ð with its singular matrix 6 kð. Furthermore, I kð can be approximated by Eqn (3), where z is the number of right singular vectors used for spectral reconstruction/transformation. 1 T sðz is a rectangular transformation matrix that maps the vector-space of V T zð into Ĵ sð, the matrix of averaged pure component spectral estimates for the s species. T 1 sðz is then a generalized inverse and Ĉ kðs is the corresponding expectation for concentrations calculated from Eqn (4). I kð D U kðk 6 kð V T ð 2 Î kð ³ Ĉ kðs Ĵ sð D U kðs 6 sðz T 1 sðz T sðzv T zð k ½ z ½ s 3 Ĉ kðs D U kðs 6 sðz T 1 sðz D Î kð Ĵ T ðs Ĵ sð Ĵ T ðs 1 The transformation of the z right singular vectors into a set of pure component spectral estimates associated with the global optimization of the s ð z elements in T matrix are subject to the proposed non-linear constrained objective function. This function includes the non-negative solutions of estimated emissivities/absorptivities Ĵ sð and the corresponding concentrations Ĉ kðs, an entropy function and a spectral dissimilarity function: Ĵ sð D T sðz V T zð Non-negative solutions All admissible estimates for the pure component spectra must ensure non-negativity in the estimated Ĵ sð and also the associated concentrations Ĉ kðs. However, soft non-negativity constraints are imposed, in which slightly negative estimates are still acceptable. a and c are penalty coefficients for the constraints defined by Eqns (9) and (10) and 1 D 10 3 and 2 D 10 2 are bounds for the emissivity/absorptivity constraint defined in Eqn (9). P Ĵ sð, Ĉ kðs D a F 1 OJ C c F 2 OC k 4 5 6

4 798 S. Y. Sin et al. where F 1 OJ D OJ 2 8 OJ < 0 7 F 2 OC k D OC k 2 k 8 OC k < F 1 OJ < 1 a D 10 1 F 1 OJ < F 1 OJ ½ 2 c D F 2 OC k 10 Entropy function To measure the degree of spectral simplicity, a Shannon-type information entropy function was employed: H D s h s ln h s 11 The measure H is the information entropy and h sv is a discrete probability distribution function that can be defined as the absolute value of the derivative of the estimated spectrum in an L 1 norm: h s D j OJ m s j joj m s j 12 The exponent m is the degree of spectral differentiation, either first, second or fourth derivative. The degree of differentiation will depend on the noise level of the mixture spectra. Higher derivatives may be employed if the signalto-noise ratio (S/N) of the mixture spectrum is fairly high. However, if the noise intensity is fairly high (S/N is low), it is preferable to use a lower degree of differentiation. Dissimilarity function Spectral dissimilarity can be quantified by a distance measure involving the estimated pure component spectra. Since it can be assumed that each pure component should have its own distinct or unique pure spectrum, maximizing the dissimilarities among the reconstructed pure component spectra can be useful; however, over-resolution may also occur. A distance measure is employed to prevent identical spectral reconstructions from occurring since s spectra are resolved simultaneously. In the present study, the distance measure is represented by a determinant of the covariance matrix of resolved spectral estimates Ĵ sð : ς a DjĴ sð Ĵ T ðs j 13 Objective function The above non-negativity, signal entropy and dissimilarity criteria form the basis for the general objective function used in the present study, where is the weightage for the determinant of the covariance matrix. The purpose of a weightage is to balance the magnitudes of the entropy function and determinant values. F obj D H C P ς 14 The final estimate of Ĵ sð corresponds to the global minimum value of the proposed objective function. This is performed by a global optimization method, specifically Corana et al. s 21 simulated annealing. For the analysis of,ω-dicarboxylic acids, a newly developed algorithm known as band-target entropy minimisation (BTEM) was utilized. 11,12 This method has a similar nature to the above-mentioned approach except that instead of resolving all pure component spectra simultaneously, the BTEM algorithm resolves the pure spectra one at a time. Briefly, in the BTEM method, one targets a spectral feature observed in the basis vectors which is of interest and which should be retained in the subsequent spectral reconstruction. Then combinations of these basis vectors are searched to achieve the global minimum value of an appropriate objective function, thereby resulting in the pure component spectrum estimate. This procedure is repeated for the other significant spectral features until all observable species are recovered. Generally, BTEM gives better results but the procedure is more computationally intense for exhaustive searches using all bands present as individual targets. The chemometric analysis of the PAH mixtures consisted of two parts: (i) mixtures 1 and 2 listed in Table 1 were used to resolve three pure component spectra; (ii) mixtures 3, 4 and 5 were used to resolve all five pure PAH spectra. The chemometric analysis for,ω-dicarboxylic acids was performed by using two approaches. For the first analysis, BTEM was used on FT-Raman spectra and FTIR spectra independently, whereas for the second analysis, spectra were resolved from BTEM after forming composite spectra (a combination of both FT-Raman and FTIR spectra after being variance weighted). Before spectral reconstruction, FTIR spectra were first baseline corrected (KBr background). In addition, all resolved spectra were normalized to unit height for easier comparison. RESULTS AND DISCUSSION Polycyclic aromatic hydrocarbons Three- PAH component mixtures FT-Raman mixture spectra of 11 samples consisting of three PAH components (naphthalene, pyrene and coronene) were consolidated into one data matrix, I 11ð2905, which is presented in Fig. 1. The data matrix was decomposed by SVD, resulting in three singular vectors, U, 6 and V T matrix. The first seven vectors in V T are shown in Fig. 2. It can be observed that there are significant localized signals (compared with the noise) in the first three vectors. In the fourth, fifth and sixth vectors, there is a significant increase in noise and only a very few localized features are seen. Finally, in the seventh

5 Novel spectral reconstruction algorithm 799 Figure 1. Consolidated I 11ð2905 intensity data matrix consisting of a three-pah component mixture. Figure 2. The first seven right singular vectors of V T matrix from three-pah component data matrix. (a) First vector; (b) second vector; (c) third vector; (d) fourth vector; (e) fifth vector; (f) sixth vector; (g) seventh vector. vector onwards, only random noise is present. Owing to this observation, the first six right singular vectors are rotated for spectral reconstruction. A first-derivative entropy measure was selected since the S/N is fairly low. The reconstructed pure component spectra and their reference spectra are presented together in Fig. 3. A high degree of similarity is clearly observed. To quantify the similarity degree between the recovered and reference spectra, the inner product with L 2 norm of these two spectra, q, was calculated. A q value closer to 1 suggests more successful reconstruction, whereas a q approaching 0 indicates poor spectrum recovery. All the q values given in Table 3 are >0.95, demonstrating that the Raman spectra of individual PAHs were successfully recovered from the original mixture spectra. Five PAH component mixtures The consolidated data from mixtures 3, 4 and 5 given in Table 1 gave rise to nine mixture spectra, I 9ð2905,shownin Fig. 4.

6 800 S. Y. Sin et al. Figure 3. Reference and the reconstructed pure component spectra from three-pah component data matrix. Figure 4. Consolidated I 9ð2905 intensity data matrix consisting five-pah component mixture. Upon data decomposition using SVD, it was decided to use all nine V T vectors for the subsequent transformation into pure component spectral estimates. The reconstructed and the reference pure component spectra are presented in Fig. 5 and the similarity degrees are shown in Table 4. The resolved pure component spectra of naphthalene, pyrene and coronene have high-resolution quality with q > 0.9, but phenanthrene and fluoranthene have lower resolution with q < 0.9. However, this lower resolution is acceptable, since identification is still possible. The reconstructions simply

7 Novel spectral reconstruction algorithm 801 Figure 5. Reference and the reconstructed pure component spectra from five-pah component data matrix. have some noise. Visual observation clearly reveals that all the primary peaks of these two compounds are recovered. Furthermore, it is expected that more measurements of mixture samples would certainly enhance the spectral recovery and thus the problem of S/N on resolved spectra can be prevented. The satisfactory correlation values (q > 0.85) shown in Tables 3 and 4 corroborate the capability of this chemometric analysis to identify compounds, regardless of the highly overlapping FT-Raman signals obtained from mixture samples. Table 3. Similarity degree (q) between recovered and reference pure spectra Component Naphthalene Pyrene Coronene q

8 802 S. Y. Sin et al. Table 4. Similarity degree (q) between recovered and reference pure spectrum Component Naphthalene Pyrene Coronene Phenanthrene Fluoranthene Dicarboxylic acids Figure 6 shows the raw spectra obtained by FT-Raman and baseline-corrected FTIR spectroscopy from the mixtures containing the various,ω-dicarboxylic acids listed in Table 2. The peaks appearing at around 1710 cm 1 in the IR spectrum represent the stretching absorption of carbonyl groups (C O) contained in the,ω-dicarboxylic acids. They are often broadened by the hydrogen bonding involved. The O H stretching vibration of the carboxylic acids has a broad band around cm 1 and the C H stretching q absorption appears at cm 1. Both the O H and C H vibrations result in a significant peak absorbance in the cm 1 region. The cm 1 region of the IR spectra is complex owing to many bending vibrations. 22 The reference pure component spectra of the six,ω-dicarboxylic acids measured via FT-Raman and FTIR spectroscopy are shown in Fig. 7. As mentioned before, two different types of analyses were performed. Method A involved independent analysis forft-ramanorftirmixturespectrausingbtem.method B was based on the use of adjoined FT-Raman and FTIR spectral data (composite spectra) again using BTEM. Figure 8 shows the recovered FT-Raman spectra of the six,ω-dicarboxylicacidsbasedonmethodaandfig.9shows the recovered FT-Raman spectra obtained with Method B. Although Raman spectroscopy is known to be less sensitive to oxygenated functional groups, it is surprising that the analysis successfully untangled individual species from the mixture spectra. In addition, the q values for the FT-Raman regions based on Methods A and B are shown in Table 5, indicating that FT-Raman measurements Intensity (a) Mixture Number Wavenumber / cm -1 2 Absorbance (b) Mixture Number Wavenumber / cm -1 Figure 6. (a) Consolidated FT-Raman raw mixture spectra consisting six,ω-dicarboxylic acids; (b) consolidated FTIR baseline-corrected mixture,ω-dicarboxylic acids.

9 Novel spectral reconstruction algorithm 803 Figure 7. FT-Raman and FTIR reference pure component spectra of the six,ω-dicarboxylic acids. can be sufficient by themselves (Method A) to provide accurate species identification. This finding is important for simplifying the analysis procedures for complicated samples without sacrificing the accuracy of compound identification. Figures 10 and 11 are the recovered FTIR pure component spectra of the six,ω-dicarboxylic acids obtained using Methods A and B respectively. The FT-Raman/FTIR combination spectral analysis (Method B) significantly improved the recovery efficiencies of oxalic acid and azelaic acid from <0.7 to >0.9, as shown in Table 5. This implies that the use of FTIR measurements alone (Method A) allows only satisfactory untangling of individual components, but that inclusion of the Raman measurements (Method B) can further improve the identification of the species in the FTIR region. Although the results in Figs 10 and 11 are not perfect (particularly in the important C O region), it must be repeated that the present spectral recoveries were achieved using BTEM on a very small data set. Also, the use of pressed KBr pellets for sample preparation results in considerable line broadening. Our experience is that BTEM performs much better when spectral lines are sharp, such as for most liquid-phase solutes. In this regard, we can mention that plans are under way to continue BTEM investigations of environmental samples using liquid Xe as the matrix for sample preparation. Sharper spectral features should be observed and a more accurate spectral recovery is expected. A final discussion concerning the advantages and limitations of BTEM is useful. As mentioned above, (i) larger data sets usually produce much betterbtem results (spectral

10 804 S. Y. Sin et al. Figure 8. Recovered FT-Raman pure component spectra of the six,ω-dicarboxylic acids via BTEM (Method A). similarities of 0.98 or better ) and (ii) vibrational spectral features from liquid-phase solutes are usually much sharper than those from solid-phase solutes. The first issue cannot be over-emphasized. Indeed, for large data sets, (a) considerable noise reduction can be achieved by discarding the last right singular vectors containing noise before BTEM is performed and (b) BTEM can be performed using a much larger number of decision variables z, thereby providing highly optimized signal recovery. The second issue leads to the question of the applicability of BTEM to really broad spectral features such as those arising in UV visible spectroscopy (which could have been used here for PAHs). Our experience thus far is that BTEM does not work very well for UV visible spectra alone, probably because UV visible (electronic) spectra usually have only a relatively few spectral features compared with vibrational spectra. The more information contained in the spectra, the better is the performance of BTEM. CONCLUSIONS An advanced spectral reconstruction algorithm based on a minimum information entropy approach has successfully recovered individual PAH spectra from a limited number of mixture spectra. Good reconstruction quality can be seen Figure 9. Recovered FT-Raman pure component spectra of the six,ω-dicarboxylic acids via BTEM (Method B). Table 5. Similarity degree (q) between recovered and reference pure spectra based on Methods A and B for FT-Raman/FTIR analysis Method A a q Method B b Component FT-Raman FTIR FT-Raman FTIR Adipic acid Azelaic acid Glutaric acid Malonic acid Oxalic acid Succinic acid a Method A involves separate BTEM analysis on each of the spectroscopic regions. b Method B involves BTEM on composite FT-Raman/FTIR spectroscopy. from the resolved pure component spectra obtained from three- and five-component mixture analysis. In addition to PAH mixture data, the BTEM approach was also successful in recovering the pure component spectra of six,ω-dicarboxylic acids measured using both

11 Novel spectral reconstruction algorithm 805 Figure 10. Recovered FTIR pure component spectra of the six,ω-dicarboxylic acids via BTEM (Method A). Figure 11. Recovered FTIR pure component spectra of the six,ω-dicarboxylic acids via BTEM (Method B). FT-Raman and FTIR spectroscopy. Independent analyses of each spectral region (Method A) gave only good spectral reconstruction for FT-Raman data. However, adjoined information (Method B) created from the combination of FT-Raman and FTIR analysis significantly improved the reconstruction quality of IR spectra, particularly for oxalic and azelaic acids. This study demonstrates that FT-Raman and coupled FT-Raman and FTIR measurements together with entropy minimization techniques possess considerable potential for analyzing environmentally relevant samples. In particular, this is important for a better understanding of the complicated compositions of atmospheric aerosols. The method appears particularly attractive since no aprioriinformation is required. REFERENCES 1. Harvey RG. Polycyclic Aromatic Hydrocarbons. Wiley-VCH: New York, Saxena P, Hildemann LM, McMurry P, Seinfeld JH. J. Geophys. Res. 1995; 100: Blando JD, Porcia RJ, Li T-H, Bowman D, Lioy PJ, Turpin BJ. Environ. Sci. Technol. 1998; 32: Fisher M, Bulatov V, Hasson S, Schechter I. Anal. Chem. 1998; 70: Maddams WF, Royaud IAM. Spectrochim. Acta, Part A 1990; 46: Munro CH, Pajcini V, Asher SA. Appl. Spectrosc. 1997; 51: Kister J, Dou H. Fuel Process. Technol. 1986; 12: Chiang H-P, Song R, Mou B, Li KP, Chiang P, Wang D, Tse WS, Ho LT. J Raman Spectrosc. 1999; 30: Taylor CE, Schoenfisch MH, Pemberton JE. Langmuir 2000; 16: Wentzell PD, Nair SS, Guy RD. Anal. Chem. 2001; 73: Widjaja E, Li CZ, Garland M. Organometallics 2002; 21: Chew W, Widjaja E, Garland M. Organometallics 2002; 21: Li C, Widjaja E, Chew W, Garland M. Angew. Chemie, Int. Ed. Engl. 2002; 41: Li C, Widjaja E, Garland M. J Catal. 2003; 213: Sasaki K, Kawata S, Minami S. Appl. Opt. 1983; 22: Sasaki K, Kawata S, Minami S. Appl. Opt. 1984; 23: Neal SL, Davidson ER, Warner IM. Anal. Chem. 1990; 62: Brown SD, Harper AM. Computer Enhanced Analytical Spectroscopy, Vol. 4. Academic Press: New York, 1993; chapt Volkov VV. Appl. Spectrosc. 1996; 50: Widjaja E, Garland M. J. Comput. Chem. 2002; 23: Corana A, Marchesi M, Martini C, Ridella S. ACM Trans. Math. Softw. 1987; 13: Wade LG. Organic Chemistry. Prentice Hall: Englewood Cliffs, NJ, 1999.