Paper-based visualization of auramine O in food and drug samples with carbon dots-incorporated fluorescent microspheres as sensing element

Wang, Wenhui; Yang, Zhenquan; Xiao, Lixia; Han, Jie; Guan, Tianzhu; Gong, Xiaojuan; Hu, Qin

doi:10.1016/j.foodchem.2023.136890

Cited by 4 publications

(1 citation statement)

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“…At present, the detection methods of AO in food and medicine mainly include chromatography [2,27] and fluorescence [28][29][30], which have the advantages of high accuracy and good detection sensitivity. In addition, adsorptive stripping voltammetry [31], bionanosorbent [32], and terahertz spectroscopy [33,34] have also been applied for the determination of AO in foods and herbs.…”

Section: Introductionmentioning

confidence: 99%

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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