基于小波空间特征匹配及表面增强拉曼光谱技术快速检测混合物中的甲氨蝶呤和伏立康唑

Rapid Detection of Methotrexate and Voriconazole in Mixtures Using Surface-enhanced Raman Spectroscopy with Features Matching in Wavelet Space

为实现混合物中目标化合物的快速检测,合成了对甲氨蝶呤和伏立康唑具有普适性响应的金纳米复合(AuNPS-PDDA)基底,借助表面增强拉曼光谱技术,结合化学计量学方法,通过连续小波变换将甲氨蝶呤和伏立康唑的光谱信号转换到小波空间,依据小波空间特征匹配分析混合物的拉曼光谱,显著减轻信号中基线变化和随机噪声的影响,成功地识别出混合物中1.0×10^-8mol/L甲氨蝶呤和2.86×10^-4mol/L伏立康唑。其中,甲氨蝶呤和伏立康唑的小波特征匹配系数均大于0.96,高于传统的命中质量系数,且采用非负最小二乘法成功实现了两种物质含量比例的确定,实验的组分比例预测值与真实值之间相关性大于0.98。实验结果表明,小波空间特征匹配结合表面增强拉曼光谱技术能有效地识别且半定量混合物中的目标化合物。

A surface-enhanced Raman spectroscopy (SERS )with chemometrics was developed for the rapid detection of methotrexate and voriconazole in mixtures.An gold nanocomposite (AuNPS- PDDA ) subtrate was fabricated to obtain the SERS spectra signals of methotrexate and voriconazole,which were transformed into wavelet space by continuous wavelet transform (CWT ),then analyzed by features matching in wavelet space (FMWS ).This method could alleviate the influence of baseline variations and random noise,with detection limits for methotrexate and voriconazole of 1.0×10 -8 mol/L and 2.86×10 -4 mol/L,respectively.The feature matching coefficients for methotrexate and voriconazole were both higher than 0.96,which were significantly better than the traditional hit quality index.Besides,the actual ratio for two kinds of substance in the mixtures was determined by nonnegative least squares method,and the correlation between predicted values and real values was larger than 0.98.The results confirmed that the proposed method could be applied in the effective identification and semi- quantification of target compound in mixtures.