Sens. & Instrumen. Food Qual. (2011) 5:91–96 DOI 10.1007/s11694-011-9115-7
ORIGINAL PAPER
Application of surface enhanced Raman spectroscopy for analyses of restricted sulfa drugs Keqiang Lai • Fuli Zhai • Yuanyuan Zhang Xichang Wang • Barbara A. Rasco • Yiqun Huang
•
Received: 12 May 2011 / Accepted: 28 June 2011 / Published online: 8 July 2011 Ó Springer Science+Business Media, LLC 2011
Abstract The presence of sulfonamide residues in muscle foods is an important concern for consumers and regulatory agencies since these residues may pose potential health risks and result in an increase of drug-resistant bacteria. Surface enhanced Raman spectroscopy (SERS) was applied to analyze three sulfa drugs including sulfamerazine, sulfamethazine and sulfamethoxazole with concentrations ranging from 10 ng mL-1 to 5 lg mL-1. Partial least squares regression (PLS) and principal component analysis (PCA) were used for the spectral data analyses. The three sulfa drugs could be detected at concentration levels as low as 10 ng mL-1. For the quantitative analyses, the R2 values of actual sulfa drug concentrations versus their concentrations predicted by the PLS models ranged from 0.8149 to 0.9009. Plotting of principal components based upon PCA showed clear, separated clusters between different sulfonamides. This study indicated potential for detection and determination of trace amounts of prohibited or restricted drugs with SERS technology. Keywords Surface enhanced Raman spectroscopy Nanosubstrate Sulfamerazine Sulfamethazine Sulfamethoxazole
K. Lai F. Zhai Y. Zhang X. Wang Y. Huang (&) College of Food Science and Technology, Shanghai Ocean University, No. 999 Hucheng Huan Road, Lingang New City 201306, Shanghai, People’s Republic of China e-mail:
[email protected];
[email protected] B. A. Rasco School of Food Science, Washington State University, Pullman, WA 99164-6376, USA
Introduction Sulfonamides or sulfa drugs, such as sulfamerazine, sulfamethazine and sulfamethoxazole, are a group of synthetic broad spectrum antimicrobial agents that contain sulfonamide group. Sulfonamides are commonly used to cure infections, to control disease outbreaks, and to promote the growth of farmed animals [1]. Recently, due to their high effectiveness against gram-negative and gram-positive organisms, sulfonamides have emerged in the aquaculture industry to control fish diseases [2]. The presence of sulfonamide residues in muscle foods is an important concern for consumers and regulatory agencies because some of the compounds may possess carcinogenic potency, trigger allergic reactions in human [3], or result in an increase of drug-resistant bacteria [4]. In order to ensure food safety as well as to prevent potential health problems for consumers, the European Union has adopted a Maximum Residue Limits (MRL) of 100 ng g-1 for total sulfonamides content in foods of animal origin [5]. China has established a MRL of 100 ng g-1 for total sulfonamides and a MRL of 25 ng g-1 for sulfamethazine in edible animal tissue [6]. Commonly used methods for determination of sulfonamide residues in aquaculture products are based upon HPLC [7] or LC/MS [8]. It has always been a hot research topic to develop simpler, faster and highly sensitive analytical method for determination of the residues of prohibited or restricted drugs in foods [9, 10]. In recent years, surface enhanced Raman spectroscopy (SERS), a promising technology for rapid and accurate detection and determination of chemicals and biochemicals, has attracted much research interest in a wide range of areas [11, 12]. With the aid of novel metal substrates, such as silver- and gold-based nanosubstrates, Raman signals of an analyte can be enhanced tremendously, which makes it possible to
123
92
detect trace amounts of a wide range of analytes with SERS technology [13]. Electromagnetic and chemical enhancements are two generally accepted theories for SERS enhancement effects. The electromagnetic enhancement theory attributes the SERS enhancement effects to a physical phenomenon that involves the excitation of localized surface plasmas resulting in the creation of an intensive magnetic field. The chemical enhancement theory attributes the SERS enhancement effects to charge-transfer between adsorbed analyte molecules and roughened metal surface. Although SERS enhancement effect is a rather complicated phenomenon with many questions unresolved, SERS has a great promise as an analytical method in detecting and determining trace amounts of toxicant or prohibited drugs as indicated in some research reports over a diverse fields [14–16]. The objective of this study was to investigate the feasibility of using SERS technology for qualitative and quantitative analysis of sulfa drugs including sulfamerazine, sulfamethazine and sulfamethoxazole. Currently, there are very few reports on the application of SERS in the field of food science, and this study could be served as the basis for further studies on determining sulfa drugs in muscle foods.
Materials and methods Sample preparation Sulfamerazine (S8876), sulfamethazine (S6256) and sulfamethoxazole (S7507) used in this study were HPLC reagents purchased from Sigma-Aldrich (St. Louis, MO, USA). Sulfamerazine, sulfamethazine, and sulfamethoxazole were diluted in 50% ethanol (Sigma-Aldrich, HPLC reagent) water solution to prepare a series of standard solutions (10, 20, 50, 100, 200, 500, 103 and 5 9 103 ng mL-1) for each of the tested sulfa drugs.
K. Lai et al.
The KlariteTM (Renishaw Diagnostics Ltd, Glasgow, UK) was used as surface enhanced substrate to acquire SERS spectra of the tested sample. The gold coated active surface of Klarite has regular arrays of inverted square pyramid subunits (deep: 1 lm; length: 1.8 lm; distance between subunits: 0.4 lm), which was fabricated by nanoscale lithographic patterning technique [17]. The Klarite has shown potential in detection of trace amounts of melamine, cyanuric acid and melamine cyanurate as well as Bacillus spores [18, 19]. SERS spectra of a tested solution were recorded after the solvent of the solution (1.0 lL) deposited on the active area of KlariteTM being evaporated. Raman spectra of each tested solution were recorded at different locations of the substrate. Data analyses Multivariate analyses including principal component analysis (PCA) and partial least squares regression (PLS) were applied to analyze SERS spectral data with DeLight 3.2 software package (D-Squared Development Inc., La Grande, OR, USA). PCA was used to see the possibility of classifying three sulfa drugs based upon their SERS spectral data, while PLS was applied to check the possibility for quantitative analysis of the tested sulfa drugs. Preprocessing algorithms including binning, smoothing and second-derivative transformation were employed to reduce background interference, separate overlapped peaks and increase the signal to noise ratio [20]. Leave-one-out cross validation was used to evaluate the PLS model predictability, which uses all but one sample to build a calibration model and repeats for each sample in the data set [21]. The root mean square error of prediction (RMSEP) of PLS models were used to determine the optimum number of latent variables used in the final PLS model for a tested sulfa drug. The performance of models was indicated by R2 of predicted analyte concentrations against their actual values.
Raman spectral acquisition Results and discussion A Nicolet DXR microscopy Raman Spectrometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) was used to acquire spontaneous Raman and SERS spectra. A 780 nm near-infrared diode laser source with 14 mW laser power, and 109 microscope objective were used for all spectral acquisitions. Before experiments, calibration was performed using a silicon standard. All spectra were recorded with a resolution of 5 cm-1 and were average of five scans with 10 s per scan. To acquire spontaneous Raman spectra, solid sulfa drug powder was deposited on a glass slide, pressed to form a thin film, and then its Raman spectra were recorded.
123
Raman spectra of sulfamerazine, sulfamethazine, sulfamethoxazole The chemical structures of sulfamerazine, sulfamethazine and sulfamethoxazole are quite similar (Fig. 1). Three sulfa drugs have common sulfonamide groups, but differ in N-substituted groups. The similarity of sulfamerazine, sulfamethazine and sulfamethoxazole in their molecular structures results in similar Raman spectral features as shown in Fig. 2. The spectra of all three drugs exhibit the two most prominent peaks at around 1598 and 1150 cm-1
Application of surface enhanced Raman spectroscopy CH 3
CH 3 O NH 2
S O
93
O
N
NH 2
NH N
S O
O
CH 3
N NH2
NH
S O
N
NH N
O
CH3
Fig. 1 Chemical structures of sulfamerazine (left), sulfamethazine (middle) and sulfamethoxazole (right)
Fig. 2 Raman spectra of sulfamerazine (top) sulfamethazine (middle), and sulfamethoxazole (bottom)
due to the stretching vibration of the benzene ring and the symmetric stretching of the SO2 group, respectively [22]. The peak at around 1095 cm-1 is attributed to the C–N stretching vibrations in sulfamerazine and sulfamethazine, or C–O stretching vibration in sulfamethoxazole [23]. Although sulfamerazine, sulfamethazine and sulfamethoxazole have similar Raman spectral features, each sulfonamide shows spectral singularities because of its unique molecular structure. Sulfamerazine and sulfamethazine have an absorption band at around 999 cm-1 due to the C–N stretching vibrational mode, while sulfamethoxazole had absorption band at around 919 cm-1 due to the C–O stretching vibration [24, 25]. Peak splitting at 841 cm-1 observed for sulfamethazine is assigned to C–N Femi resonance of symmetric structure in the substituted group [25]. In addition, the relative intensity of the peak at around 1095 cm-1 varied among three sulfa drugs due to asymmetric substituted groups, and the absorption intensity of sulfamethazine at around 1095 cm-1 was relatively weak compared to the other two sulfonamides [25]. SERS spectra of sulfamerazine, sulfamethazine, sulfamethoxazole The SERS spectra of sulfamerazine, sulfamethazine and sulfamethoxazole are shown in Fig. 3. The SERS spectrum of a sulfonamide had broadened absorption bands, and the
Fig. 3 SERS spectra of sulfamerazine (top) sulfamethazine (middle), and sulfamethoxazole (bottom) obtained at concentration level of 5 lg mL-1
relative intensity of these bands differed from that of its spontaneous Raman spectrum. Interactions between analyte molecules and substrate surface are required for the enhancement of Raman scattering signals, which sets the SERS apart from spontaneous Raman spectroscopy. For SERS, the sulfa drug molecules were adsorbed to the Klarite substrate in different orientations and the types of adsorption sites on the surface also varied, which contributed to the broadening of Raman signals and the change of relative intensities of absorption bands; while for spontaneous Raman spectroscopy, the analyte molecule could be considered as evenly distributed at different orientations [26, 27]. In addition, some peaks appeared in the spontaneous Raman spectrum could be downshifted, upshifted, or disappeared in the SERS spectrum. For example, the peak at 919 cm-1 in the normal Raman spectrum of sulfamethoxazole was upshifted to 929 cm-1 in the SERS spectrum. The wavenumbers of vibrational modes are normally found downshifted when the bonds of pertinent functional groups were weakened, but upshifted when the bonds were strengthened [26, 28]. Again, the molecular orientations attached to the surface and the types of adsorption sites affect the molecular frequencies. Nevertheless, the prominent peaks in the SERS spectra of sulfonamides were consistent with that in their spontaneous Raman spectrum. The prominent peaks at around
123
94
840 and 1598 cm-1 in the spontaneous Raman spectra were still among the most prominent peaks in their SERS spectra counterparts. Other major peaks in Raman spectra, such as the peak at around 998 cm-1 for sulfamerazine and sulfamethazine as well as at around 919 cm-1 for sulfamethoxazole were also prominent peaks in their SERS spectra. Representative SERS spectra of three sulfa drugs at different concentrations are shown in Fig. 4 (sulfamerazine), Fig. 5 (sulfamethazine) and Fig. 6 (sulfamethoxazole). For sulfamerazine, the characteristic peaks at around 999 and 1042 cm-1 could be discernable at as low as 10 ng mL-1 level, although the other two characteristic peaks at around 837 and 1597 cm-1 were very weak (Fig. 4). Similarly, the characteristic peaks at around 835, 995, 1049 and 1597 cm-1 for sulfamethazine (Fig. 5), and
K. Lai et al.
Fig. 6 Representative SERS spectra of sulfamethoxazole obtained at different concentrations (10 ng mL-1 to 5 lg mL-1)
at around 835 and 1045 cm-1 for sulfamethoxazole could be identified at a concentration level as low as 10 ng mL-1 (Fig. 6). In general, the intensity of some prominent peaks increased with an increase of sulfa drug concentration, although the increase rate varied for different absorption bands. For example, the intensity of characteristic peaks at around 837, 999 and 1042 cm-1 increased with an increase of sulfamerazine concentration. This made it possible for quantitative analysis of sulfamerazine with SERS and chemometric methods. Quantitative analysis
Fig. 4 Representative SERS spectra of sulfamerazine obtained at different concentrations (10 ng mL-1 to 5 lg mL-1)
Fig. 5 Representative SERS spectra of sulfamethazine obtained at different concentrations (10 ng mL-1 to 5 lg mL-1)
123
When chemometric method was applied to correlate the actual log values of sulfamethazine concentrations with the SERS spectral data, the best performance of PLS model was obtained by using the spectral region from 500 to 2000 cm-1 with six latent variables. A log transformation of sulfamethazine concentrations was used to encapsulate the large range in concentration of the drug. Figure 7 shows the relationship between the values of RMSEP of sulfamethazine PLS model and the number of latent variables used in the PLS model. The lowest RMSEP was obtained when six latent variables were used for PLS model, indicating the optimal number of latent variable was six. The R2 of log values of sulfamethazine concentrations versus their values predicted by the PLS model was 0.9009 (Fig. 8). Similarly, for sulfamerazine and sulfamethoxazole, the R2 of the optimum PLS model were 0.8405 and 0.8149, respectively. Although the active surface of Klarite substrate consists of regular array of inverted pyramid subunits, the orientations of sulfonamide molecules adsorbed on the substrate surface and the binding sites varied. For example, molecules may be adsorbed inside pyramid subunits or between
Application of surface enhanced Raman spectroscopy
95
Fig. 7 RMSEP values of PLS models for sulfamethazine with different latent variables
Fig. 9 PCA plot of three sulfa drugs (sulfamerazine, sulfamethazine and sulfamethoxazole) with concentrations ranging from 10 ng mL-1 to 5 lg mL-1
sulfamethoxazole were clearly observed through plotting the first three principal components that accounted for 75% of variance (Fig. 9). This indicates that sulfamerazine, sulfamethazine and sulfamethoxazole could be differentiated with SERS technology coupled with chemometric method, and therefore there is potential to analyze three drugs simultaneously, although they have similar chemical structures. Fig. 8 Actual log values of sulfamethazine concentrations versus their PLS model predicted values
Conclusions pyramid subunits. The variation in molecular orientations and binding sites affected the consistency of SERS enhancement effects and also resulted in shifts in band positions, which are typical problems causing the difficulty in applying SERS for quantity analysis [28, 29]. For simple systems like sulfa drug standard solutions used in this study, a relatively large number of latent variables were used for PLS model development due to the complexity of SERS enhancement effects. The R2 values ranging from 0.8149 to 0.9009 for PLS models for the tested drugs were not very optimistic results, but indicating the possibility for applying SERS technology for quantitative or semi-quantitative analyses of drug residues. Classification of sulfa drugs PCA was used to classify sulfamerazine, sulfamethazine and sulfamethoxazole based upon their SERS spectral data. Segregations between sulfamerazine, sulfamethazine and
Sulfa drugs, such as sulfamerazine, sulfamethazine and sulfamethoxazole, could be detected at as low as 10 ng mL-1 level (about 10 ppb) using SERS technology coupled with a commercial substrate. Since generally accepted MRL for total sulfonamides in muscle food is 100 ng mL-1 (100 ppb), our study results indicated the possibility of applying this SERS technology for analyzing sulfonamides in foods. Although standard solutions instead of real food samples were used, the study results could be served as basis for further investigating the accuracy of applying SERS to analyze sulfonamides in food samples. With appropriate sample preparation, such as those used for HPLC or GC method including extraction and purification of sulfonamide from foods, a similar limit of detection may be achieved for a real food system. There is still a long way to go to apply SERS for quantitative analyses. However, with further development of SERS substrates and the aid of chemometric methods, there is
123
96
great promise of applying SERS as quantitative method for hazardous residues in foods. Acknowledgments This research was supported by the Science and Technology Commission of Shanghai Municipality (Project # 09PJ1405200 & 09320503800), the Leading Academic Discipline Project of Shanghai Municipal Education Commission (Project # J50704) and Shanghai Ocean University (A-2400-09-0145). Special thanks go to Dr. Yan-liang Zhang in Thermo Fisher Scientific Inc. for his technical support.
References 1. S. Bogialli, R. Curini, A.D. Corcla, M. Nazzari, R. Samperi, Anal. Chem. 1798, 75 (2003) 2. Specification for the application of sulfonamides in aquaculture, The Ministry of Agriculture, PR China, SC/T 1084-2006 3. N.A. Littlefield, W.G. Sheldon, R. Allen, D.W. Gaylor, Food Chem. Toxicol. 157, 28 (1990) 4. D.R. Doerge, C.J. Decker, Chem. Res. Toxicol. 164, 7 (1994) 5. European Commission, Council Directive 178/2002, Off. J. Eur. Commun. L031, 1 (2002) 6. Establishment of maximum reside levels of veterinary medical products in foodstuffs of animal origin, The Ministry of Agriculture, PR China, Regulation No. 235 (2002) 7. I. Pecorelli, R. Bibi, F. Fioroni, R. Galarini, J. Chromatogr. A 23, 1032 (2004) 8. T.A.M. Msagati, M.M. Hindi, Talanta 87, 64 (2004) 9. R.J. Stokes, A. Ingram, J. Gallagher, D.R. Armstrong, W.E. Smith, D. Graham, Chem. Commun. 567, 5 (2008)
123
K. Lai et al. 10. N. Rodrı´gueza, M.C. Ortiza, L.A. Sarabiab, A. Herreroa, Anal. Chim. Acta 136, 657 (2010) 11. L. He, M. Lin, H. Li, N. Kim, J. Raman Spectrosc. 739, 41 (2010) 12. X. Zhang, N. Shah, R. Van Duyne, Vib. Spectrosc. 2, 42 (2006) 13. C. Haynes, A. McFarland, R. Van Duyne, Anal. Chem. 338, 77 (2005) 14. M. Lin, L. He, J. Awika, L. Yang, D.R. Ledoux, H. Li, A. Mustapha, J. Food Sci. 129, 73 (2008) 15. B. Liu, M. Lin, H. Li, Sens. Instrum. Food Qual. 4, 13 (2010) 16. T.A. Alexander, Anal. Chem. 2817, 80 (2008) 17. N. Perney, J. Baumberg, M. Zoorob, M. Charlton, S. Mahnkopf, C. Netti, Opt. Express 847, 14 (2006) 18. A. Szeghalmi, S. Kaminskyj, P. Rosch, J. Popp, K.M. Gough, J. Phys. Chem. B 12916, 111 (2007) 19. L. He, Y. Liu, M. Lin, J. Awika, D.R. Ledoux, H. Li, A. Mustapha, Sens. & Instrumen. Food Qual. 66, 2 (2008) 20. Y. Huang, A.G. Cavinato, D.M. Mayes, G.E. Bledsoe, B.A. Rasco, J. Food Sci. 2543, 67 (2002) 21. Y. Huang, A.G. Cavinato, J. Tang, B.G. Swanson, M. Lin, B.A. Rasco, LWT Food Sci. Technol. 1018, 40 (2007) 22. W.S. Sutherlank, J.J. Laserna, M.J. Angebranndt, J.D. Winefordner, Anal. Chem. 689, 62 (1990) 23. X. Cao, C. Sun, T.J. Thamann, J. Pharm. Sci. 1881, 94 (2005) 24. C.A. Topacli, J. Topacli, J. Mol. Struct. 145, 644 (2003) 25. H. Zhu, Spectral Analysis of Organic Molecular Structure. Chapter 2 (Chemical Industry Press, Beijing, 2005), p. 42 26. J.R. Lombardi, R.A. Birke, Acc. Chem. Res. 734, 42 (2009) 27. S.E.J. Bell, N.M.S. Sirimuthu, Chem. Soc. Rev. 1012, 37 (2008) 28. D. Sajan, G.D. Sockalingum, M. Manfait, I. Hubert Joe, V.S. Jayakumar, J. Raman Spectrosc. 1772, 39 (2008) 29. W.E. Smith, Chem. Soc. Rev. 955, 37 (2008)
