Fabrication of Multifunctional SERS Platform Based on Ag NPs Self-Assembly Ag-AAO Nanoarray for Direct Determination of Pesticide Residues and Baicalein in Real Samples

Shi, Guochao; Li, Kuihua; Gu, Jungai; Yuan, Wenzhi; Xu, Shiqi; Han, Wei; Gu, Jianwen; Wang, Liyong; Zhang, Zhibin; Congzhe, Chen; Jialin, Ge; Wang, Mingli

doi:10.3390/coatings11091054

Cited by 8 publications

(2 citation statements)

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“…Raman spectroscopy (RS) is a spectroscopic technique for characterizing molecular structure based on the Raman effect, including molecular vibration and rotation [35]. Since it was discovered by Indian scientist C.V. Raman in 1928, with the continuous progress and development of computer and signal detection technology, Raman spectroscopy technology has gradually become more intelligent, non-destructive, convenient, and fast [36]. However, the fluorescence phenomenon, background noise, background baseline, and other signals will affect the weak Raman signal, which will reduce the signal-to-noise ratio of the spectrum and increase the analysis error, thus limiting its development in trace detection.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model

Zhang,

et al. 2023

Foods

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(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm−1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm−1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

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Section: Introductionmentioning

confidence: 99%

“…SERS is more specific and sensitive than Raman spectroscopy and can enhance the Raman signal of the molecule to be detected by 10 13 ~10 15 times. Therefore, SERS technology has a long-term application prospect in the detection of adulteration of Chinese herbal medicines [35], pesticide residues [36], and food safety testing [37].…”

Section: Introductionmentioning

confidence: 99%