Food Chemistry 148 (2014) 42–46

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Analytical Methods

Recovery and quantitative detection of thiabendazole on apples using a surface swab capture method followed by surface-enhanced Raman spectroscopy Lili He a,b, Tuo Chen a, Theodore P. Labuza a,⇑ a b

Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, United States Department of Food Science, University of Massachusetts, Amherst, MA 01003,United States

a r t i c l e

i n f o

Article history: Received 7 November 2012 Received in revised form 12 September 2013 Accepted 6 October 2013 Available online 16 October 2013 Keywords: Thiabendazole Apple Surface swab Surface-enhanced Raman spectroscopy

a b s t r a c t We developed a rapid and simple method which combines a surface swab capture method and surfaceenhanced Raman spectroscopy for recovery and quantitative detection of thiabendazole on apple surfaces. The whole apple surface was swabbed and the swab was vortexed in methanol releasing the pesticide. Silver dendrites were then added to bind the pesticide and used for enhancing the Raman signals. The recovery of the surface swab method was calculated to be 59.4–76.6% for intentionally contaminated apples at different levels (0.1, 0.3, 3, and 5 ppm, lg/g per weight). After considering the releasing factor (66.6%) from the swab, the final accuracy of the swab-SERS method was calculated to be between 89.2% and 115.4%. This swab-SERS method is simple, sensitive, rapid (10 min), and quantitative enough for QA/QC in plant procedure. This can be extended to detect other pesticides on raw agricultural produce like pears, carrots, and melons etc. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The use and distribution of pesticides in the United States are regulated by the Environmental Protection Agency (EPA). Based on the evaluation of the human health and environmental effects of each pesticide, EPA establishes maximum residue limits (MRL) for each pesticide on various raw agricultural and livestock commodities. The golden analytical methods for detecting pesticides in agricultural and livestock commodities are based on separation by chromatographic methods (e.g. HPLC and GC) followed by detection using UV or mass spectroscopy. However, the limitations of these methods are the tedious sample preparation steps, high cost of the instruments, requirement of well trained personnel and a laboratory. It is important to develop a much simpler, faster, and cost-effective analytical method that can possibly be used out on the plant floor. Recently, much interest has been given to surface-enhanced Raman spectroscopy (SERS), especially as a rapid, simple and sensitive detection method in complex food matrices (He, Deen, et al., 2011; He, Haynes, Diez-Gonzalez, & Labuza, 2011; He, Lamont et al., 2011; He, Rodda, et al., 2011). SERS is a combination of Raman spectroscopy and nanotechnology. Raman spectroscopy, a vibrational spectroscopic technique, has been used widely for mol-

⇑ Corresponding author. Tel.: +1 612 624 9701; fax: +1 612 625 5272. E-mail address: [email protected] (T.P. Labuza). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.10.023

ecule identification and structural characterization of various compounds. It provides high structural information content, which is a so-called molecular ‘‘fingerprint’’ (McCreery, 2000). The time for collecting the spectra is usually a few seconds. However, the major drawback is its low sensitivity. Placement of the analyte on noble metal nanoscale-roughened surfaces (typically silver or gold) enhances the inherently weak Raman molecular signatures tremendously (Haynes, McFarland & Van Duyne, 2005). Parts per billion (ppb) limits of detection have been achieved, and in some cases, a single molecule can be detected using this method (Haynes et al., 2005). A portable Raman instrument has potential for on-site analysis. Spectral characterizations of pesticides using SERS have been reported (Kim, Kim, Lee, Jung & Lee, 2009; Vongsvivut, Robertson & McNaughton, 2010). These studies provide good references for peak assignments of pesticide molecules. There are also some SERS studies for the detection of pesticides. However, most of the studies only involved detection of pure pesticides in water or buffer (Carrillo-Carrion, Simonet, Valcarcel, & Lendl 2012; Costa et al., 2009; Lee et al., 2006; Liron, Zifman, & Heleg-Shabtai, 2012), or with complex sample preparation procedures (Liu et al., in press; Tang et al., 2012). Thus it is important to develop simple, fast, reliable, and SERS compatible methods for recovery of pesticides from both raw agriculture samples (e.g. apples) as well as processed foods such as pasteurized apple juice or apple sauce. However, there has been little effort put into the development and optimization of sample preparation steps for specific matrices.

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Thiabendazole is a fungicide used to control a variety of fruit and vegetable diseases caused by various fungi. Approximately 150,000 lbs. of thiabendazole are used annually (USEPA, 2002). It is mostly used in post-harvest as a dip or spray application prior to the waxing procedure on apples, citrus fruits, pears, carrots, potatoes, etc. for cold storage and later distribution out of season. Thiabendazole generally is of low acute toxicity, however, it has been classified as likely to be carcinogenic at doses high enough to cause disturbance of the thyroid hormone balance (USEPA, 2002). People may be exposed to residues of thiabendazole through the diet. The MRL for apples (post-harvest) is 5 ppm (lg/g) on an apple weight basis (USEPA, 2002). Apples are at the top of the list of produce most contaminated with pesticides (USDA, 2006). In this study, we aimed to develop and optimize a rapid and simple surface swab method for recovery of thiabendazole, the most frequently found pesticide on apples) followed by surface-enhanced Raman scattering (SERS) detection. Surface swab methods have been routinely used to recover chemical and biological compounds (e.g. ATP (Davidson, Griffith, Peters, & Fielding, 1999)), as well as microbes on various surfaces (Depprich, Handschel, Meyer, & Meissner, 2008; Lewandowski, Kozlowska, Szpakowska, Stepinska, & Trafny, 2010). They are simple, fast and fieldable. To the best of our knowledge, this is the first report of the combination of a surface swab method and SERS method for pesticides recovery and detection on the surface of fresh produce. This novel swab-SERS method can be extended to detect other pesticides on produce like, pears, carrots, potatoes etc.

variance within a class and between different classes. The PC score reveals the percentage of data variance. A higher percentage indicates more data variance within the PCA model. Generally speaking, if two data clusters (classes) do not overlap, then it means they are significantly different at the p = 0.05 level. Therefore, PCA can be used to determine the LOD. The LOD was determined to be the lowest concentration of the data cluster that can be separated from the negative control. Before analyzing by PCA, spectra were pre-processed using second derivative transformation to separate overlapping bands and remove baseline shifts. 2.4. Establishment of a standard calibration model of TBZ in methanol To establish a standard calibration model of TBZ in methanol, spectra of different concentrations of TBZ were analyzed by partial least squares (PLS) using the TQ analyst software. PLS was constructed by calibrating sample concentrations based on the spectral information and their actual (spiked) values. The constructed PLS model was validated by leave-one-out cross validation, which uses all but one sample to build a calibration model and repeats the procedure for each sample in the data set. The quality of the model was determined by root mean square error of calibration (RMSEC), root mean square error of validation (RMSEV), and correlation coefficient. The lower the RMSEC and RMSEV value, the higher the correlation coefficient and the closer of the range between the RMSEC and RMSEV values, the better quality of the model. Before constructing PLS, second derivative transformation was used to separate overlapping bands and remove baseline shifts.

2. Materials and methods 2.5. Development of the surface swab and SERS method 2.1. Materials Thiabendazole (TBZ), methanol, zinc, and silver nitrate were purchased from Fisher Scientific Inc. Apples (Gala) were purchased from Brooks Bros., a local food produce broker in Minneapolis. The origin was a farm in Washington State. The average weight of each apple was about 200 ± 10 g. Ag dendrites were prepared through a simple replacement reaction involving both zinc (Zn) and silver nitrate (AgNO3) (He, Lin, Li, & Kim, 2009). 2.2. Sample preparation for SERS analysis TBZ solutions were prepared by dissolving TBZ powder in methanol at final concentrations from 0.01 to 100 lg/mL (ppm). Two millilitres of each TBZ solution was mixed with 5 lL (20 lg) Ag dendrites for 4 min under constant rotation using a Talboys advanced vortex mixer (speed 3000 rpm, Auto model). After that, the Ag dendrites quickly settled down to the bottom of the tube. Three microlitres of the silver dendrites were sucked from the bottom of the tube and separately deposited onto different spots on a microscopic glass slide and air-dried for 2 min in a forced air laboratory hood at room temperature. They were then examined by Raman measurement. 2.3. Determination of the limit of detection of SERS method for TBZ in methanol To determinate the limit of detection (LOD) of the SERS method for TBZ in methanol, spectra of different concentrations of TBZ were analyzed by principal component analysis (PCA) using the TQ analyst software v8.0 (Thermo Fisher Scientific). The PCA procedure reduces a multidimensional data set to its most dominant features, removes random variation, and retains the principal components (PCs) that capture the variation between sample treatments. The information provided by the PCA shows the

To recover pesticides from the apple surface, a surface swab method was developed. Swab sticks with sealed knit polyester foam head (W  L: 0.4  1.0 in., CONTEC, SC) were used in the study. Parameters like swab area and time, and vortex time were optimized to get the highest recovery within the shortest time (see details in the Supplementary data). The procedure is illustrated in Fig. 1, firstly, the whole apple surface was swabbed for 1.5 min using a swab pre-soaked with methanol, then the swab stick was immersed in 2 mL methanol and vortexed for 4 min to release the pesticides. After that, 5 lL Ag dendrites were added to the solution of the released pesticides and incubated for 4 min under constant rotation to bind the pesticide. Then the Ag dendrites were spun down and 3 lL of the pellet were deposited onto a glass slide and dried for 2 min at room temperature for Raman measurement. Considering that the swab may not release all the TBZ molecules in the vortex step, it is important to calculate the release factor in order to obtain a more accurate result using the calibration model. To calculate the releasing factor, 0.1 mL of 500 lg/mL TBZ solution was dropped on a clean glass slide and dried. The same procedure with the optimized parameters using the swab test was applied. The final TBZ concentration was calculated using the calibration model. In this way, we were able to calculate the releasing factor to be 66.6% using Eq. (1).

Releasing factor ¼

½TBZ concentration after vortexlmL  2 mL 0  1 mL  500lg=mL  100% ð1Þ

2.6. Validation of the swab-SERS method on intentionally contaminated apples Considering that the EPA MRL for TBZ is 5.0 ppm (lg/g) by weight of apple, it is important to translate results into the EPA MRL units of lg/g into the units of lg/mL of the test solution in

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Fig. 1. The illustration of the swab-SERS procedure on apples.

order to be applied to the prediction model. The average weight of the apples used in this study is about 200 g, thus the maximum amount of allowed pesticide residue is 1 mg (200 g  5 lg/ g = 1 mg per whole apple). If all of the pesticide that is on the surface is recovered and then released by the swab, the 100% recovered TBZ in a 2 mL solution is 500 lg/mL (1000 lg/ 2 mL = 500 lg/mL). The translation formula is

The accuracy of the swab-SERS method was calculated by

½Accuracy ¼

½translated predicted value lg=g  100ð%Þ ½spiked value lg=g  release factor

ð4Þ

2.7. Raman instrumentation

½TBZ concentration on apple lg=g ¼

½TBZ concentration in methanollg=g=mL 200 g

ð2Þ

To intentionally contaminate TBZ at 0.1, 0.3, 3, and 5 ppm (lg/g) by weight of apple on the whole apple surface, and to mimic the real dip application of TBZ on apples, we firstly determined the surface uptake of the TBZ solution on apples by immersing an apple in a methanol or water solution, and calculated the difference of the solution volume before and after the dipping of the apple for 2 s. The average volume for the surface uptake was 0.4 mL based on ten different apples. Taking the 3 ppm contamination for example, the concentration of TBZ solution for dipping should be 1.5 mg/mL (0.03  200/0.4 mL = 1.5 mg/mL). Then we dipped the whole apple into a 600 mL beaker with 400 mL of 1.5 mg/mL TBZ solution for 2 s. After that, the apple was air-dried in a hood at room temperature for 20 min. After it dried, the swab method was then applied to the apple. The final spectra were analyzed in the built in PLS model to predict the amount recovered from the swab method. For high contamination at 5 and 3 ppm, a twofold dilution was needed to fit the range of the calibration model. The experiment was conducted in triplicate (three apples were used for each concentration). The recovery of the surface swab method was calculated by

½Recovery ¼

½translated predicted value lg=g  100ð%Þ ½spiked value lg=g

ð3Þ

An Almega Raman microscope (Thermo Fisher Scientific) was used in this study. This instrument has 532 nm and 785 nm lasers and 10, 50, and 100 microscopic objectives. We used the 785 nm laser and the 10 objective (laser spot diameter 3 lm) in this study. Each spectrum was scanned from 2000 to 500 cm1 for 10 s using 50% laser power. The spectral resolution is 3 cm1. Five Raman scans were taken per each sample. 3. Results and discussion 3.1. LOD of SERS for TBZ in methanol The molecular structure and the ‘‘fingerprint’’ SERS spectra of 100 lg/mL TBZ in methanol are shown in Fig. 2. Silver dendrites were able to adsorb TBZ though the Ag–S bond. According to the work of Kim et al. (2009), the assignments to the major peaks at 1547, 1285, and 785 cm1 were C@N stretching, ring stretching, and the out of plane bending of C–H, respectively. To determine the LOD of SERS for TBZ in methanol, TBZ was dissolved in methanol to final concentrations of 0–10 lg/mL, respectively. Fig. 3a shows the overlaid spectra after secondary derivative transformation for the 1285 cm1 peak. It is clear to see the concentration dependent intensity changes. PCA was used to analyze the data variance of TBZ samples of the lowest concentrations. The PCA plot in Fig. 3b shows the data cluster of the lowest concen-

Fig. 2. The molecular structure and the SERS spectra of 100 lg/mL TBZ in methanol.

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Fig. 3. Secondary derivative transformed SERS spectra for the 1285 cm1 peaks of TBZ at different concentrations (a), and the PCA plot of the SERS spectra of TBZ of the low concentrations (b).

tration that can be separated from the negative control (0 lg/mL) is 0.01 lg/mL, which defines the LOD to be 0.01 lg/mL. PCA helped to confirm that there is a statistically significant difference between the data of 0 and 0.01 lg/mL. 3.2. Calibration model and validation model A calibration model was constructed by PLS using TBZ solutions from 0 to 100 lg/mL . The RMSEC was 7.49 and the correlation coefficient was 0.977 (Fig. 4a). This calibration model was then self-validated using the ‘‘leave-one-out’’ validation method. The RMSECV was 7.75 and the correlation coefficient was 0.975 (Fig. 4b). The low difference between RMSEC and RMSECV, i.e. 0.26, and the high values of the two correlation coefficients (close to 1), indicates that the calibration model was very good and reliable. This calibration model was used in the next step to predict the concentration of the TBZ residues on apples. 3.3. Validation of the swab-SERS method on intentionally contaminated apples

Fig. 4. PLS plots of the calibration model (a) and the validation model (b).

Apples were intentionally contaminated by TBZ for the final concentrations at 5, 3, 0.3, and 0.1 ppm (lg/g per weight), respectively. For each concentration, 3 apples were used. Each apple was swabbed using the swab method described in Materials and Methods 2.5. The results are shown in Table 1. The predicted values were calculated using the developed calibration model. The average recoveries of the surface swab were calculated to be 59.4– 76.7% according to the Eq. (3). As expected, most of the loss for the recovery was due to the incomplete release of TBZ during the

Table 1 Prediction and recovery of TBZ from apple using the swab-SERS method. Spiked value on apple (ppm, lg/g per weight)

Predicted value in solution (ug/mL)

Translated predicted value (ppm, lg/g per weight)

Recovery of swab (%)

Translated predicted value/releasing factor (ppm, lg/g per weight)

Accuracy (%)

5 3 0.3 0.1

297.2 ± 7.2 178.8 ± 40.8 23.0 ± 10.0 6.0 ± 0.35

2.97 ± 0.07 1.79 ± 0.41 0.23 ± 0.10 0.06 ± 0.00

59.4 59.6 76.6 60.0

4.46 ± 0.11 2.80 ± 0.64 0.35 ± 0.16 0.09 ± 0.00

89.2 90.0 115.4 90

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vortex step. The recovery may be improved by using a larger amount of solvent and longer time of vortexing. However, that would significantly dilute the sample and increase the total analytical time. After considered the releasing factor, the final accuracy of the swab-SERS method was calculated to be 89.2–115.4% based on Eq. (4). These results validated that the calibration model was reliable when applied to the swab-SERS method for the recovery and detection of TBZ on apple surfaces. The variance was mainly due to the manual steps in the swab procedure; nevertheless, the range was good enough for quantification. The lowest tested concentration was 0.1 ppm with 90% accuracy. The detection capability of this method was sufficiently sensitive to detect at or below the MRL of TBZ on apples (5 ppm lg/g per weight). 4. Conclusion A surface swab capture followed by surface-enhanced Raman scattering detection was developed and optimized for recovery and quantification of thiabendazole on apple surfaces. This method is simple, sensitive, rapid, and fairly quantitative. The whole procedure including sample preparation and detection was about 10 min. The surface swab procedure can be done in the field. If done with a portable Raman instrument, on-site detection has high potential. Further studies will focus on validating this method using other pesticides and on various agricultural produce like apples, pears, carrots, etc. Acknowledgement This project was supported by the Agriculture and Food Research Initiative Program of the US Department of Agriculture (USDA-AFRI, Grant No. 2012-67017-30194). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem.2013. 10.023. References Carrillo-Carrion, C., Simonet, B. M., Valcarcel, M., & Lendl, B. (2012). Determination of pesticides by capillary chromatography and SERS detection using a novel Silver-Quantum dots ‘‘sponge’’ nanocomposite. Journal of Chromatography A, 1225, 55–61.

Costa, J. C., Ando, R. A., Sant’Ana, A. C., Rossi, L. M., Santos, P. S., Temperini, M. L., et al. (2009). High performance gold nanorods and silver nanocubes in surfaceenhanced Raman spectroscopy of pesticides. Physical Chemistry Chemical Physics, 11(34), 7491–7498. Davidson, C. A., Griffith, C. J., Peters, A. C., & Fielding, L. M. (1999). Evaluation of two methods for monitoring surface cleanliness-ATP bioluminescence and traditional hygiene swabbing. Luminescence, 14(1), 33–38. Depprich, R. A., Handschel, J. G., Meyer, U., & Meissner, G. (2008). Comparison of prevalence of microorganisms on titanium and silicone/polymethyl methacrylate obturators used for rehabilitation of maxillary defects. Journal of Prosthetic Dentistry, 99(5), 400–405. Haynes, C. L., McFarland, A. D., & Van Duyne, R. P. (2005). Surface-enhanced Raman spectroscopy. Analytical Chemistry, 77(17), 338a–346a. He, L., Deen, B., Rodda, T., Ronningen, I., Blasius, T., Haynes, C., et al. (2011a). Rapid detection of ricin in milk using immunomagnetic separation combined with surface-enhanced Raman spectroscopy. Journal of Food Science, 76(5), N49–N53. He, L., Haynes, C. L., Diez-Gonzalez, F., & Labuza, T. P. (2011b). Rapid detection of a foreign protein in milk using IMS-SERS. Journal of Raman Spectroscopy, 42(6), 1428–1434. He, L., Lamont, E., Veeregowda, B., Sreevatsan, S., Haynes, C. L., Diez-Gonzalez, F., et al. (2011c). Aptamer-based surface-enhanced Raman scattering detection of ricin in liquid foods. Chemical Science, 2(8), 1579–1582. He, L., Lin, M., Li, H., & Kim, N.-J. (2009). Surface-enhanced Raman spectroscopy coupled with dendritic silver nanosubstrate for detection of restricted antibiotics. Journal of Raman Spectroscopy, 41(7), 739–744. He, L., Rodda, T., Haynes, C. L., Deschaines, T., Strother, T., Diez-Gonzalez, F., et al. (2011). Detection of a foreign protein in milk using surface-enhanced Raman spectroscopy coupled with antibody-modified silver dendrites. Analytical Chemistry, 83, 1510–1513. Kim, M. S., Kim, M. K., Lee, C. J., Jung, Y. M., & Lee, M. S. (2009). Surface-enhanced Raman spectroscopy of benzimidazolic fungicides: benzimidazole and thiabendazole. Bulletin Korean Chemistry Society, 30(12), 2930–2935. Lee, D., Lee, S., Seong, G. H., Choo, J., Lee, E. K., & Gweon, D. G. (2006). Quantitative analysis of methyl parathion pesticides in a polydimethylsiloxane microfluidic channel using confocal surface-enhanced Raman spectroscopy. Applied Spectroscopy, 60(4), 373–377. Lewandowski, R., Kozlowska, K., Szpakowska, M., Stepinska, M., & Trafny, E. A. (2010). Use of a foam spatula for sampling surfaces after bioaerosol deposition. Applied Environmental Microbiology, 76(3), 688–694. Liron, Z., Zifman, A., & Heleg-Shabtai, V. (2012). Surface-enhanced Raman scattering detection of cholinesterase inhibitors. Analytica Chimica Acta, 703(2), 234–238. Liu, B., Zhou, P., Liu, X., Sun, X., Li, H., & Lin, M. (2012). Detection of pesticides in fruits by surface-enhanced Raman spectroscopy coupled with gold nanostructures. Food Bioprocess Technology, (in press). McCreery, R. L. (2000). Raman spectroscopy for chemical analysis. Wiley-Interscience. Tang, H., Fang, D., Li, Q., Cao, P., Geng, J., & Sui, T., et al., (2012). Determination of 374 tricyclazole content in paddy rice by surface enhanced Raman spectroscopy. 375 Journal of Food Science, 77(5), T105–T109, http://dx.doi.org/10.1007/ s11947-001-0774-5. USDA. (2006). USDA pesticide data program. Accessed 14.09.12. USEPA (2002). EPA reregistration eligibility decision thabendazole. Accessed 14.09.12. Vongsvivut, J., Robertson, E. G., & McNaughton, D. (2010). Surface-enahced Raman spectroscopic analysis of fonofos pesticide adsorbed on silver and gold nanoparticles. Journal of Raman Spectroscopy, 41, 1137–1148.