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Rapid Detection of Acid Orange Ⅱ by Surface-Enhanced Raman Spectroscopy Coated with Different Nano-Substrates |
WANG Xiao-hui1, XU Tao-tao1, 2, HUANG Yi-qun3, OU Yi-ming1,4, LAI Ke-qiang1, 2, FAN Yu-xia1, 2* |
1. College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2. Engineering Research Center of Food Thermal Processing Technology, Shanghai Ocean University, Shanghai 201306, China
3. School of Chemistry & Biological Engineering, Changsha University of Science & Technology, Changsha 410076, China
4. College of Food Science, Shanghai Zhongqiao College, Shanghai 201514, China |
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Abstract Acid Orange Ⅱ, as an azo chemical dye, displays strong carcinogenesis and teratogenicity. Therefore, it is prohibited to use it in food industry. However, due to the bright color, good dyeing force and low price of Acid Orange Ⅱ, unscrupulous merchants illegally added Acid Orange Ⅱ to food for coloring, which seriously threatens food safety and consumer health. Acid Orange Ⅱ can be detected by traditional instrumental analysis methods. These methods have their own limitation such as complicated preprocessing, being time-consuming, and could not match the purpose of rapid detection and identification. Surface-enhanced Raman spectroscopy (SERS), as a fast, sensitive and novel fingerprint spectral analysis technology, has received extensive attention in the field of food safety detection. Therefore, this study aims to apply SERS spectroscopy technique combined with different nanosubstrates to explore the rapid detection method of Acid Orange Ⅱ. Firstly, gold nanoparticles (AuNPs), gold nanorods (AuNRs) substrate were synthesized in our laboratory, and we characterized its structure and properties using transmission electron microscipy (TEM). The results indicated that the nanosubstrates have the uniform scale and good dispersion. Then, the effect of two different Raman excitation sources was analyzed, which included the wavelengths of 633 and 780 nm. The results showed that the SERS response signal of Acid Orange Ⅱ is stronger based on the 633 nm excitation source. On this basis, three different substrates (KlariteTM commercial solid substrate, AuNPs, and AuNRs) were compared and the substrate enhancement performance was studied. The SERS signal of Acid Orange Ⅱ was significantly different based on different gold nanoparticle sizes. It exhibited better reinforcing properties for (18±2) AuNPs. Acid Orange Ⅱ standard solutions with a series of concentrations were detected using SERS combined with three nanosubstrates (KlariteTM, AuNPs with the diameter of (18±2) nm and AuNRs with an aspect ratio of 1.8), which showed almost similar enhancement for SERS signal of Acid Orange Ⅱ. The results demonstrated that the lowest detection concentrations of Acid Orange Ⅱ were 0.2, 0.1, and 0.1 mg·L-1 based on KlariteTM, AuNPs w and AuNRs substrates, respectively. As the SERS intensity increased with the increase of the concentration,the quantitative analysis models of Acid Orange Ⅱ were established. The Raman intensities of the selected peaks at 1 184, 1 385 and 1597 cm-1 were in linear relationship with the concentrations of Acid Orange Ⅱ. The linear determination coefficient R2 ranges were from 0.861 to 0.938, the RMSE is 0.88~1.15 mg·L-1, and the RPD is 2.5~4.0. The linear regression model between 1 597 cm-1 peak intensity and concentration (R2=0.933, RMSE=0.88 mg·L-1, RPD=4.0) showed the best linear correlation. The results showed SERS spectroscopy could be used for qualitative and quantitative analysis of Acid Orange Ⅱ. The proposed method, as a simple, rapid and highly sensitive approach, could be applied for detection colorants.
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Received: 2018-11-26
Accepted: 2019-03-04
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Corresponding Authors:
FAN Yu-xia
E-mail: nancyfyx@sjtu.edu.cn
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[1] Yadav A, Kumar A, Dwivedi P D. Toxicology Letters, 2012, 208(3):239.
[2] XU Qin, LIU Lin, FU Yu-qiang, et al(徐 琴,刘 琳,傅余强,等). Food Science(食品科学), 2010, 31(8): 219.
[3] Rebane R, Leito I, Yurchenko S, et al. Journal of Chromatography A, 2010, 1217(17): 2747.
[4] XIA Li-ya, HAN Yuan-yuan, KUANG Lin-he, et al(夏立娅,韩媛媛,匡林鹤,等). Chinese Journal of Analysis Laboratory(分析实验室), 2010, 29(6): 15.
[5] Güler, Z. Journal of Food Quality, 2010, 28(1): 98.
[6] Xue H Y, Xing Y, Yin Y M, et al. Food Addictives and Contaminants Part A-Chemistry Analysis Control Exposure & Risk Assessment, 2012, 29(12):1840.
[7] Altunbek M, Kuku G, Culha M. Molecules, 2016, 21(12): 1617.
[8] Li Y, Wei Q, Ma F, et al. Acta Pharmaceutica Sinica B, 2018, 8(3): 349.
[9] Galvan D D, Yu Q. Advanced Healthcare Materials, 2018, 7(13): 1701335.
[10] Shi R, Liu X, Ying Y. Journal of Agricultural and Food Chemistry, 2018, 66(26): 6525.
[11] Gillibert R, Huang J Q, Zhang Y, et al. Trac-Trends in Analytical Chemistry, 2018, 105: 185.
[12] Zhang Y, Huang Y, Zhai F, et al. Food Chemistry, 2012, 135(2): 845.
[13] Fan Y, Lai K, Rasco B A, et al. Food Control, 2014, 37: 153.
[14] Lin X M, Cui Y, Xu Y H, et al. Analytical and Bioanalytical Chemistry, 2009, 394(7): 1729.
[15] Frens G. Nature Physical Science, 1973, 241:20.
[16] Yang J A, Lohse S E, Boulos S P, et al. Journal of Cluster Science, 2012, 23(3): 799.
[17] FU Yun-peng, QI Ying, HU Xiao-peng, et al(符云鹏,齐 颖,扈晓鹏,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(8): 2419.
[18] ZHANG Zong-mian, LIU Rui, XU Dun-ming, et al(张宗绵,刘 睿,徐敦明,等). Acta Chimica Sinica(化学学报), 2012, 70: 1686.
[19] Xie Y, Li Y, Niu L, et al. Talanta, 2012, 100: 32.
[20] He L, Liu Y, Lin M, et al. Sensing and Instrumentation for Food Quality and Safety, 2008, 2(1):66. |
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