Rapid determination of ligustilide content in angelica sinensis by near infrared spectroscopy technology

LI Si-hai; CHEN Jian-guo; REN Guo-jin

doi:10.13873/J.1000-9787(2017)12-0114-04

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Rapid determination of ligustilide content in angelica sinensis by near infrared spectroscopy technology

更新时间：2025-09-11

- Rapid determination of ligustilide content in angelica sinensis by near infrared spectroscopy technology
- Vol. 36, Issue 12, Pages: 114-117(2017)
- 作者机构：
  
  1. 甘肃中医药大学信息工程学院
  2. 甘肃省高校中(藏)药化学与质量省级重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)12-0114-04
  CLC： R284.1;O657.33
- Published：2017
- 稿件说明：
移动端阅览
[1]李四海,陈建国,任国瑾.近红外光谱技术快速测定当归中藁本内酯含量[J].传感器与微系统,2017,36(12):114-117.

LI Si-hai, CHEN Jian-guo, REN Guo-jin. Rapid determination of ligustilide content in angelica sinensis by near infrared spectroscopy technology[J]. 2017, 36(12): 114-117.
[1]李四海,陈建国,任国瑾.近红外光谱技术快速测定当归中藁本内酯含量[J].传感器与微系统,2017,36(12):114-117. DOI： 10.13873/J.1000-9787(2017)12-0114-04.

LI Si-hai, CHEN Jian-guo, REN Guo-jin. Rapid determination of ligustilide content in angelica sinensis by near infrared spectroscopy technology[J]. 2017, 36(12): 114-117. DOI： 10.13873/J.1000-9787(2017)12-0114-04.

摘要

基于近红外光谱技术

运用偏最小二乘回归（PLSR）方法实现当归中藁本内酯含量的快速、无损检测。采用高效液相色谱（HPLC）法测定当归中藁本内酯含量

一阶导数结合正交信号校正对原始光谱进行预处理

建立当归近红外光谱和藁本内酯含量之间的最小二乘回归定量分析模型。结果表明:模型在校正集上的均方根误差（RMSEE）、交叉验证均方根误差（RMSECV）和决定系数R

分别为0.199 9

0.348 9和0.993 2

在预测集上的预测均方根误差（RMSEP）和决定系数R

分别为0.23和0.994 1。方法具有简单、快速、不破坏样品等特点

可用于当归中藁本内酯含量的快速检测。

Abstract

Partial least squares regression（ PLSR） method is utilized for determination of ligustilide content in angelica sinensis samples based on near infrared（ NIR） spectroscopy. Ligustilide contents is determined by high performance liquid chromatography（ HPLC）. The NIR spectra pretreatments method of first derivative combined with orthogonal signal correction（ OSC） is investigated. The quantitative analysis model is built through the PLSR method. The Experimental results show that the root mean square error of estimation（ RMSEE）

root mean square error of cross validation（ RMSECV）

coefficient of determination R

is 0. 1999

0. 348 9 and 0. 993 2 in the calib

ration set

the root mean square error of prediction（ RMSEP） and R

is 0. 23 and 0. 994 1 respectively. The quantitative analysis model established in this study is simple

fast

it can be used for detection of ligustilide content in angelica sinensis.

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