Journal
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
Volume 223, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.saa.2019.117281
Keywords
SERS; Heavy metal; Multivariate calibration; ACO-BP-AdaBoost; Foodstuffs
Categories
Funding
- National Natural Science Foundation of China [31772063]
- Key R&D Program of Ningxia Hui Autonomous Region [2018BCF1001]
- Key R&D Program of Jiangsu Province [BE2017357]
- Guangdong Provincial Key Laboratory of Food Quality and Safety, China [2019KF002]
- China Postdoctoral Science Foundation [2019M651748]
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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-Ada Boost model achieved the best results with the higher correlation coefficient of determination (R-2 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. (C) 2019 Elsevier B.V. All rights reserved.
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