Geographical Differentiation of  Using UV Spectroscopy Combined with Chemometrics

DI Zhun; ZHAO Yan-li; ZHANG Ji; WANG Yuan-zhong; LI Li

doi:10.13422/j.cnki.syfjx.2016180021

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Geographical Differentiation of Using UV Spectroscopy Combined with Chemometrics

更新时间：2024-01-04

- Geographical Differentiation of Using UV Spectroscopy Combined with Chemometrics
- Chinese Journal of Experimental Traditional Medical Formulae Vol. 22, Issue 18, Pages: 21-26(2016)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.13422/j.cnki.syfjx.2016180021
  CLC： R284
- Published：2016
- 稿件说明：
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DI Zhun, ZHAO Yan-li, ZHANG Ji, et al. Geographical Differentiation of Using UV Spectroscopy Combined with Chemometrics[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(18): 21-26.
DOI：

DI Zhun, ZHAO Yan-li, ZHANG Ji, et al. Geographical Differentiation of Using UV Spectroscopy Combined with Chemometrics[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(18): 21-26. DOI： 10.13422/j.cnki.syfjx.2016180021.

摘要

目的：分析鉴别4个产地川东獐牙菜，并建立预测模型，预测产地区分准确性。方法：光谱数据导入UVProbe 2.34，比较不同产地相同部位的紫外光谱图，将原始光谱数据以及经过8点平滑、一阶求导和二阶求导后的数据导入SIMCA-P 11.5，进行主成分分析（PCA），比较三维得分图的产地鉴别效果。结果：主成分分析中以叶的原始数据以及8点平滑处理数据鉴别效果最佳，主成分累计贡献率均为98.8%，其余预处理方式无法取得较好的鉴别效果可能与主成分数累计值有关（一阶求导为83.9%，二阶求导为47.3%）。根部数据能将重庆、湖北的样品和湖南样品分开，但重庆和湖北的样品无法区分。建立偏最小二乘判别分析（PLS-DA）模型，检测鉴别模型的可靠性，并为预测更多产地的区分提供依据。将验证集带入训练集建立的模型进行偏最小二乘判别分析，能区分产地，证明该模型产地鉴别效果可行。PLS-DA中训练集的预测值和真实值相关系数为0.985，其评估均方差（RMSEE）为0.159，验证集导入训练集后其预测值与真实值的相关系数为0.927，预测均方差（RMSEP）为0.327，RMSEE与RMSEP两者相近，且都<0.500，该模型的预测可靠性高。结论：运用紫外光谱结合主成分分析和偏最小二乘判别分析能够较好的鉴别不同产地川东獐牙菜，构建模型预测效果较好，加入未知产地样品也能较好区分。

Abstract

Objective: In this study

a high efficient and rapid method was used to identify the origin of herbal medicines in order to safeguard our country's economic interests in the international trade. Method: Ultraviolet(UV) spectroscopy combined with principal component analysis(PCA) and partial least square discriminant analysis(PLS-DA) was used to discriminate the Swertia davidi which collected from different origins and establish the prediction model to predict the accuracy of the regions. The spectra data were imported into UV Probe 2.34 software to compare the same part of S. davidi. Raw and pre-processed data(8 point smoothing

the first derivative and the second derivative) were imported into SIMCA-P 11.5 and the effect of discrimination of origins was compared by 3D score plot of PCA. Result: PCA indicated that the raw and 8 point smoothing data of leaves showed the best classification and the cumulative contribution rate of the first three factors was 98.8%. The other pre-processed methods could not obtain better identification and it may be related to the cumulative contribution value(the cumulative contribution rate of the data processed by the first derivative was 83.9%while the second derivative was 47.3%). Samples from Chongqing and Hubei could be distinguished with that of Hunan by the data of roots

but the samples of Chongqing and Hubei could not be separated. The model of PLS-DA may provide the basis of discrimination of more origins. The validation set was imported into the model developed by the training set and it proved that the model was feasible and effective. In PLS-DA

the correlation index of predictive value and true value in the training set was 0.985 and the RMSEE was 0.159. The correlation index of predictive value and true value after importing the validation set in the training set was 0.972 and RMSEP was 0.327. Both RMSEE and RMSEP were similar and less than 0.500. So the model had high reliability. Conclusion: UV spectra combined with PCA and PLS-DA can discriminate S. davidi from different origins and the predicted effect of the model was better. Furthermore

samples with unknown origins could also be distinguished.

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