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

(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 <i>Dendrobium officinale</i> (<i>D. officinale</i>). Then, the detection of AO content in <i>Saffron</i> and <i>Curcuma</i> using the <i>D. officinale</i> training set as a calibration model. Finally, <i>Saffron</i> and <i>Curcuma</i> samples were added to the training set of <i>D. officinale</i> to predict the AO content in <i>Saffron</i> and <i>Curcuma</i> 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 <i>D. officinale</i>, with <i>R_p</i>² = 0.962, and RMSEP = 0.080 mg/mL. Some <i>Curcuma</i> samples and <i>Saffron</i> samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted <i>R_p</i>² fluctuated in the range of 0.780 ± 0.035 for the content of AO in <i>Saffron</i> when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm^-1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted <i>R_p</i>² fluctuated in the range of 0.500 ± 0.035 for the content of AO in <i>Curcuma</i> 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.