Rapid quantitative analysis of Hg2+ residue in dairy products using SERS coupled with ACO-BP-AdaBoost algorithm
In this study, surface-enhanced Raman spectroscopy (SERS) coupled with multivariate calibrations were employed to develop a rapid, simple and sensitive method for determination of mercury ions residues in dairy products. Initially, spherical Au@SiO2 core shell nanoparticles with highly enhancement e...
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| Published in | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 223; p. 117281 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
05.12.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1386-1425 |
| DOI | 10.1016/j.saa.2019.117281 |
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| Abstract | In this study, surface-enhanced Raman spectroscopy (SERS) coupled with multivariate calibrations were employed to develop a rapid, simple and sensitive method for determination of mercury ions residues in dairy products. Initially, spherical Au@SiO2 core shell nanoparticles with highly enhancement effect were synthesized to serve as the SERS substrate. Afterwards, an optical sensor system, namely micro-Raman spectroscopy system, was constructed for rapid acquisition of Au@SiO2-mercury ions spectra. Then, ant colony optimization (ACO) and genetic algorithm (GA) were applied comparatively for selecting the characteristic variables from the Savitzky Golay-First derivative (SG-FD) processing data for subsequent quantitative analysis. Eventually, both linear (PLS and SW-MLR) and nonlinear (BPANN and BP-AdaBoost) methods were used for modeling. Experimental results showed that the variables selection methods significantly improved the model performance. Especially for the ACO algorithm, and the ACO-BP-AdaBoost model achieved the best results with the higher correlation coefficient of determination (R2 = 0.997), and lower root-mean-square error of prediction (RMSEP = 0.092) than other quantification models. Paired sample t-test exhibited no statistically significant difference (sig > 0.05) between the reference concentrations determined by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted concentrations by ACO-BP-AdaBoost model in adulterated foodstuffs.
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•Micro-Raman spectroscopy system was developed for SERS spectra measurement of Hg2+.•Spherical Au@SiO2 core shell nanoparticles were synthesized to serve as the SERS substrate.•ACO-BP-AdaBoost was developed to improve the performance of modeling.•In validation analysis, the proposed method showed satisfactory results (sig > 0.05) with ICP-MS. |
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| AbstractList | In this study, surface-enhanced Raman spectroscopy (SERS) coupled with multivariate calibrations were employed to develop a rapid, simple and sensitive method for determination of mercury ions residues in dairy products. Initially, spherical Au@SiO2 core shell nanoparticles with highly enhancement effect were synthesized to serve as the SERS substrate. Afterwards, an optical sensor system, namely micro-Raman spectroscopy system, was constructed for rapid acquisition of Au@SiO2-mercury ions spectra. Then, ant colony optimization (ACO) and genetic algorithm (GA) were applied comparatively for selecting the characteristic variables from the Savitzky Golay-First derivative (SG-FD) processing data for subsequent quantitative analysis. Eventually, both linear (PLS and SW-MLR) and nonlinear (BPANN and BP-AdaBoost) methods were used for modeling. Experimental results showed that the variables selection methods significantly improved the model performance. Especially for the ACO algorithm, and the ACO-BP-AdaBoost model achieved the best results with the higher correlation coefficient of determination (R2 = 0.997), and lower root-mean-square error of prediction (RMSEP = 0.092) than other quantification models. Paired sample t-test exhibited no statistically significant difference (sig > 0.05) between the reference concentrations determined by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted concentrations by ACO-BP-AdaBoost model in adulterated foodstuffs.
[Display omitted]
•Micro-Raman spectroscopy system was developed for SERS spectra measurement of Hg2+.•Spherical Au@SiO2 core shell nanoparticles were synthesized to serve as the SERS substrate.•ACO-BP-AdaBoost was developed to improve the performance of modeling.•In validation analysis, the proposed method showed satisfactory results (sig > 0.05) with ICP-MS. |
| ArticleNumber | 117281 |
| Author | Ouyang, Qin Ali, Shujat Wu, Xiangyang Hassan, Md Mehedi Chen, Quansheng Liu, Shuangshuang Xu, Zhenlin Li, Huanhuan |
| Author_xml | – sequence: 1 givenname: Huanhuan surname: Li fullname: Li, Huanhuan organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 2 givenname: Shuangshuang surname: Liu fullname: Liu, Shuangshuang organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 3 givenname: Md Mehedi surname: Hassan fullname: Hassan, Md Mehedi organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 4 givenname: Shujat surname: Ali fullname: Ali, Shujat organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 5 givenname: Qin surname: Ouyang fullname: Ouyang, Qin organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 6 givenname: Quansheng orcidid: 0000-0003-2498-3278 surname: Chen fullname: Chen, Quansheng email: q.s.chen@hotmail.com organization: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China – sequence: 7 givenname: Xiangyang surname: Wu fullname: Wu, Xiangyang organization: School of the Environment, Jiangsu University, Zhenjiang, People's Republic of China – sequence: 8 givenname: Zhenlin surname: Xu fullname: Xu, Zhenlin email: Jallent@163.com organization: Laboratory of Quality and Safety Risk Assessment in Agricultural Products Preservation Ministry of Agriculture, Guangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou, People's Republic of China |
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