Bian Xihui, Lu Zhankui, van Kollenburg Geert
State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemistry and Chemical Engineering, Tiangong University, Tianjin, 300387, P. R. China.
Department of Analytical Chemistry, Institute for Molecules and Materials (IMM), Radboud University, 6500 GL Nijmegen, The Netherlands.
Anal Methods. 2020 Jul 16;12(27):3499-3507. doi: 10.1039/d0ay00285b.
Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.
首次将紫外可见漫反射光谱法（UV-Vis DRS）与化学计量学相结合，用于区分当归与其他四种相似的草药（要么来自同一属，要么外观相似）。共收集了191个样本，包括40个当归样本、39个独活样本、38个川芎样本、35个白术样本和39个白芷样本，并将其分为训练集和预测集。主成分分析（PCA）用于观察校正集的样本聚类趋势。研究了不同的预处理方法，并根据光谱信号特征和三维主成分分析（3D PCA）聚类结果选择了最佳的预处理组合。最终的判别模型使用极限学习机（ELM）构建。对原始光谱及其3D PCA得分的探索性研究表明，通过原始光谱的PCA无法实现这五种草药的分类。自动缩放、连续小波变换（CWT）和Savitzky-Golay（SG）平滑可以不同程度地改善聚类结果。此外，它们按CWT+自动缩放+SG平滑的顺序组合可以提高光谱分辨率并获得最佳的聚类结果。这些结果也使用原始光谱和不同预处理方法的ELM模型进行了验证。通过使用CWT+自动缩放+SG平滑+ELM，在校正集和预测集中均可实现100%的分类准确率。因此，所开发的方法可作为一种快速、经济且有效的方法，用于鉴别本研究中使用的这五种草药。
