拉曼光谱结合偏最小二乘法分析大鼠血液中紫杉醇含量

Analysis of paclitaxel concentration in rat plasma by Raman spectrums combined with partial least square

利用偏最小二乘法(PLS)结合拉曼光谱技术,建立了血液中紫杉醇含量的预测模型。本实验利用拉曼光谱对312个样本进行了扫描,采用高效液相色谱技术(HPLC)对血液中紫杉醇含量进行了常规分析,利用蒙特卡罗偏最小二乘法(MCPLS)剔除异常样本,确定了校准集和预测集,采用可移动窗口偏最小二乘法(MWPLS)以逼近度(Da)为指标优化了最佳预处理方法、波长变量和隐变量数等参数,并最终建立了紫杉醇的预测模型。其校准集和预测集的预测值与真实值之间的相关系数(Rc2和Rp2)分别为0.933 1和0.926 4。最后对预测模型进行了独立验证实验,结果表明20个验证样本的相关误差为9.36%±2.03%,表明模型具有很好的拟合度和预测能力。

Partial least square（PLS） combining with Raman spectroscopy was applied to develop predictive models for plasma paclitaxel concentration detection. In this experiment, 312 samples were scanned by Raman spectroscopy. High performance liquid chromatography（HPLC） was applied to determine the paclitaxel concentration in312 rat plasma samples. Monte Carlo partial least square（MCPLS） method was successfully performed to identify the outliers and the numbers of calibration set. Based on the values of degree of approach（Da）, moving window partial least square（MWPLS） was used to choose the suitable preprocessing method, optimum wavelength variables and the number of latent variables. The correlation coefficients between reference values and predictive values in both calibration set（Rc2）and validation set（Rp2） of optimum PLS model were 0.933 1 and 0.926 4, respectively. Furthermore, an independent verification test was performed on the prediction model. The results showed that the correlation error of the 20 validation samples was 9.36%±2.03%, which confirmed the well predictive ability of established PLS quantitative analysis model.