Fa yi xue za zhi · 2025
[Rapid Identification of Etomidate and Its Structural Analogues Based on Surface-Enhanced Raman Spectroscopy and Machine Learning].
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Guo Zi-Wen, Qiu Tian-Yu, Cao Yue
Аннотация
OBJECTIVES: To obtain differential spectral characteristics of etomidate and its structural analogues, and to establish a rapid identification method using surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms for distinguishing etomidate and its analogues.
METHODS: Silver nanoparticles (AgNPs) were used as the SERS substrate to collect SERS spectra of etomidate, metomidate, propoxate, and isopropoxate at two concentrations of 1×10-4 and 1×10-5 mol/L. SERS spectra were also obtained from blood and urine samples containing 1×10-5 mol/L of etomidate, metomidate, propoxate, and isopropoxate, as well as from confiscated e-cigarette oil containing etomidate. Uniform manifold approximation and projection (UMAP) was employed for nonlinear dimensiona-lity reduction and visualization, and a classification model based on the XGBoost algorithm was constructed to enable discriminant analysis of these four structurally highly similar compounds.
RESULTS: Minor characteristic peak shifts (5-3 cm-1) were identified in the range of 1 398-811 cm-1. Qualitative identification of the compounds in serum, urine and e-cigarette oil samples was achieved without pretreatment. After UMAP dimensionality reduction, distinct clustering separation among different substances was observed. The XGBoost model achieved 100% classification accuracy on the test set. Feature weight analysis revealed that C-N stretching vibration (841 cm-1), C=O stretching vibration (1 367 cm-1), and C-O-C asymmetric vibration (1 049 cm-1) were the key spectral bands for discrimination.
CONCLUSIONS: The combination of SERS and machine learning can effectively amplify subtle differences in molecular structures, enabling rapid and accurate identification of etomidate and its analogues. This approach is suitable for on-site rapid screening in forensic toxicology.
METHODS: Silver nanoparticles (AgNPs) were used as the SERS substrate to collect SERS spectra of etomidate, metomidate, propoxate, and isopropoxate at two concentrations of 1×10-4 and 1×10-5 mol/L. SERS spectra were also obtained from blood and urine samples containing 1×10-5 mol/L of etomidate, metomidate, propoxate, and isopropoxate, as well as from confiscated e-cigarette oil containing etomidate. Uniform manifold approximation and projection (UMAP) was employed for nonlinear dimensiona-lity reduction and visualization, and a classification model based on the XGBoost algorithm was constructed to enable discriminant analysis of these four structurally highly similar compounds.
RESULTS: Minor characteristic peak shifts (5-3 cm-1) were identified in the range of 1 398-811 cm-1. Qualitative identification of the compounds in serum, urine and e-cigarette oil samples was achieved without pretreatment. After UMAP dimensionality reduction, distinct clustering separation among different substances was observed. The XGBoost model achieved 100% classification accuracy on the test set. Feature weight analysis revealed that C-N stretching vibration (841 cm-1), C=O stretching vibration (1 367 cm-1), and C-O-C asymmetric vibration (1 049 cm-1) were the key spectral bands for discrimination.
CONCLUSIONS: The combination of SERS and machine learning can effectively amplify subtle differences in molecular structures, enabling rapid and accurate identification of etomidate and its analogues. This approach is suitable for on-site rapid screening in forensic toxicology.