Ag@CDS SERS substrate coupled with lineshape correction algorithm and BP neural network to detect thiram in beverages.

Journal: Talanta
PMID: 39591862

Abstract

Surface enhanced Raman scattering (SERS) has been proved an effective analytical technique due to its high sensitivity, however, how to identify and extract useful information from raw SERS spectra is still a problem that needs to be resolved. In this work, a composite SERS substrate was prepared by encapsulating Ag nanoparticles within dialdehyde starch (Ag@CDS) to obtain dense "hot spot", and then a novel spectral preprocessing algorithm namely lineshape correction algorithm (LCA) was developed to separate the characteristic peaks of analytes from the original SERS spectra. Based on Ag@CDS and LCA, thiram residues in different beverages were quantitatively detected using back propagation (BP) neural network regression model. It was found that LCA provided an easy-to-use method for improving prediction ability of BP model. The R of BP model was improved from 0.2384, 0.3647 and 0.5581 to 0.9327, 0.9127 and 0.9251 for the quantitative detection of thiram residue in apple juice, grape juice and milk, respectively, while LCA was used for SERS spectra preprocessing. The optimal model can accurately detect thiram residue with a low limit of detection at 1.0 × 10 M, which is far below the maximum residue limit of thiram (2.9 × 10 M) regulated by the US Environmental Protection Agency. This study demonstrated that the proposed LCA can be used as a simple and valid spectra-preprocessing method in SERS quantitative detection.

Authors

  • Yu Shen
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.
  • Qian Ou
    College of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, Nanning, 530006, China; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Product, Guangxi Minzu University, Nanning, 530006, China; Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Minzu University, Nanning, 530006, China.
  • Ya-Qi Yang
    College of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, Nanning, 530006, China; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Product, Guangxi Minzu University, Nanning, 530006, China; Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Minzu University, Nanning, 530006, China.
  • Wei-Wei Zhu
    Guangxi Colleges and Universities Key Laboratory of Environmental-Friendly Materials and New Technology for Carbon Neutralization, Guangxi Key Laboratory of Advanced Structural Materials and Carbon Neutralization, School of Materials and Environment, Guangxi Minzu University, Nanning 530105, China. Electronic address: zhuww1230@163.com.
  • Song-Song Zhao
    College of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, Nanning, 530006, China; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Product, Guangxi Minzu University, Nanning, 530006, China; Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Minzu University, Nanning, 530006, China.
  • Xue-Cai Tan
    College of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, Nanning, 530006, China; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Product, Guangxi Minzu University, Nanning, 530006, China; Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Minzu University, Nanning, 530006, China.
  • Ke-Jing Huang
    College of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, Nanning, 530006, China; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Product, Guangxi Minzu University, Nanning, 530006, China; Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Minzu University, Nanning, 530006, China.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.

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