基于差分拉曼光谱对黄芪药材的快速检验分析

Rapid Inspection and Analysis of Drug Packaging Cartons Based on Differential Raman Spectroscopy

为建立一种快速准确的黄芪药材的分类方法,利用差分拉曼光谱仪,在光源使用双频输出(δλ≤1nm),单频激光输出功率为250 mw,波长为785 nm,光谱范围为180~2800 cm-1,积分时间为3秒的条件下,对88批次黄芪药材进行检验。根据拉曼谱峰的不同,比较样品的差分拉曼光谱发现样品可以分为四种;通过标准差标准化方法(Z-Score)对原始光谱进行预处理;利用判别分析模型进行训练,预测集和交叉验证下的准确率分别为95.5%、81.8%;建立的卷积神经网络模型中测试集的准确率达到了100%;为了更进一步确定特征波长对预测的重要性,使用相同的训练集和测试集建立随机森林算法模型,准确率分别为90.8%、85.0%。结果显示差分拉曼光谱能够对不同种类的黄芪药材进行检测,建立的通用预测模型方便快捷,对黄芪药材的快速检验分析奠定了基础。

To establish a rapid and accurate classification method for Astragalus herbs.Using a differential Raman spectrometer,88 batches of Astragalus herbs were examined under the conditions that the light source used a dualfrequency output(δλ≤1 nm),a single-frequency laser with an output power of 250 mw,a linewidth of≤0.06 nm,a wavelength of 785 nm,a spectral range of 180-2800 cm-1,and a scanning time of 3 seconds.According to the difference of Raman peaks,comparing the differential Raman spectra of the samples found that the samples could be classified into four kinds;the raw spectra were preprocessed by standard deviation standardisation method(Z-Score);the discriminant analysis model was used for training,and the accuracy rates under prediction set and cross-validation were 95.5%and 81.8%,respectively;the accuracy rate of the test set in the established convolutional neural network model reached 100%;to further determine the importance of feature wavelengths for prediction,a random forest algorithm model was built using the same training and test sets,with accuracies of 90.8%and 85.0%,respectively.Differential Raman spectroscopy is able to detect different kinds of Astragalus herbs,and the general prediction model established is convenient and fast,which lays a foundation for the rapid examination and analysis of Astragalus herbs.