State Key Laboratory of Separation Membranes and Membrane Processes, School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin 300387, PR China.
College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao, 266590, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jan 5;206:23-30. doi: 10.1016/j.saa.2018.07.094. Epub 2018 Aug 1.
Traditional methods for identification of Panax notoginseng (PN) such as high performance liquid chromatography (HPLC) and gas chromatography (GC) are time-consuming, laborious and difficult to realize rapid and online analysis. In this research, the feasibility of identification and quantification of PN with rhizoma curcumae (RC), Curcuma longa (CL) and rhizoma alpiniae offcinarum (RAO) are investigated by using near infrared (NIR) spectroscopy combined with chemometrics. Five chemical pattern recognition methods including hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), support vector machine (SVM) and extreme learning machine (ELM) are used to build identification model of the dataset with 109 samples of PN and its three adulterants. Then seven datasets of binary, ternary and quaternary adulterations of PN are designed, respectively. Five multivariate calibration methods, i.e., principal component regression (PCR), support vector regression (SVR), partial least squares regression (PLSR), ANN and ELM are used to build quantitative model and compared for each dataset, separately. Finally, in order to further improve the prediction accuracy, SG smoothing, 1st derivative, 2nd derivative, continuous wavelet transform (CWT), standard normal variate (SNV), multiple scatter correction (MSC) and their combinations are investigated. Results show that PLS-DA and SVM can achieve 100% classification accuracy for identification of 109 PN with its three adulterants. PLSR is an optimal calibration method by comprehensive consideration of prediction accuracy, over-fitting and efficiency for the quantitative analysis of seven adulterated datasets. Furthermore, the predictive ability of the PLSR model for PN contents can be improved obvious by pretreating the spectra by the optimal preprocessing method, with correlation coefficients of which all higher than 0.99.
传统的鉴定方法如高效液相色谱法（HPLC）和气相色谱法（GC）等对于鉴定三七（PN）需要耗费大量的时间和精力，并且难以实现快速和在线分析。在这项研究中，我们使用近红外（NIR）光谱结合化学计量学来研究姜黄（RC）、姜黄（CL）和益智（RAO）鉴定和定量鉴定三七的可行性。五种化学模式识别方法，包括层次聚类分析（HCA）、偏最小二乘判别分析（PLS-DA）、人工神经网络（ANN）、支持向量机（SVM）和极限学习机（ELM），用于建立包含 109 个三七及其三种掺杂物样本的数据集的识别模型。然后，设计了七个二进制、三元和四元掺杂物的数据集。使用五种多元校正方法，即主成分回归（PCR）、支持向量回归（SVR）、偏最小二乘回归（PLSR）、ANN 和 ELM，分别为每个数据集建立定量模型并进行比较。最后，为了进一步提高预测精度，研究了 SG 平滑、一阶导数、二阶导数、连续小波变换（CWT）、标准正态变量（SNV）、多散射校正（MSC）及其组合。结果表明，PLS-DA 和 SVM 可以实现对 109 个三七及其三种掺杂物的 100%分类准确率。综合考虑预测精度、过拟合和效率，PLSR 是七种掺杂物数据集定量分析的最佳校正方法。此外，通过最优预处理方法对光谱进行预处理，可以明显提高 PLSR 模型对三七含量的预测能力，相关系数均高于 0.99。
