基于近红外光谱技术建立连翘叶中5种活性成分动态定量模型

潘秀珍; 刘路路; 任晓红; 耿泽宇; 左旭丽; 马威; 李卫东

doi:10.3969/j.issn.1006-2157.2023.09.013

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基于近红外光谱技术建立连翘叶中5种活性成分动态定量模型

中药化学 | 更新时间：2023-10-09

- 基于近红外光谱技术建立连翘叶中5种活性成分动态定量模型
- Establishing dynamic quantitative models of five active ingredients in Forsythia suspensa leaves based on near-infrared spectroscopy
- 北京中医药大学学报 2023年46卷第9期页码：1280-1290
- 作者机构：
  
  1.北京中医药大学中药学院　北京　102488
  2.山西省安泽县林业局
  3.北京宇辰致业科技有限公司
- 作者简介：
  
  潘秀珍，女，在读硕士生
  # 李卫东，男，博士，研究员，博士生导师，主要研究方向：中药资源评价与利用，E-mail：liweidong2005@126.com
- 基金信息：
  
  北京市自然科学基金项目(5212014);安泽连翘新品种选育及质量标准研究项目(2021110031010788)
- DOI：10.3969/j.issn.1006-2157.2023.09.013
  中图分类号： R284.1
- 纸质出版日期：2023-09-30，
  
  网络出版日期：2023-07-03，
  
  收稿日期：2023-03-24，
- 稿件说明：
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潘秀珍, 刘路路, 任晓红, 等. 基于近红外光谱技术建立连翘叶中5种活性成分动态定量模型[J]. 北京中医药大学学报, 2023,46(9):1280-1290.

PAN Xiuzhen, LIU Lulu, REN Xiaohong, et al. Establishing dynamic quantitative models of five active ingredients in Forsythia suspensa leaves based on near-infrared spectroscopy[J]. Journal of Beijing University of Traditional Chinese Medicine, 2023,46(9):1280-1290.
潘秀珍, 刘路路, 任晓红, 等. 基于近红外光谱技术建立连翘叶中5种活性成分动态定量模型[J]. 北京中医药大学学报, 2023,46(9):1280-1290. DOI： 10.3969/j.issn.1006-2157.2023.09.013.

PAN Xiuzhen, LIU Lulu, REN Xiaohong, et al. Establishing dynamic quantitative models of five active ingredients in Forsythia suspensa leaves based on near-infrared spectroscopy[J]. Journal of Beijing University of Traditional Chinese Medicine, 2023,46(9):1280-1290. DOI： 10.3969/j.issn.1006-2157.2023.09.013.

摘要

目的

基于近红外(NIR)光谱技术，通过建立连翘叶中5种活性成分(总黄酮、总酚、连翘酯苷A、连翘苷和芦丁)的动态定量模型，快速检测其含量并追踪年动态变化规律。

方法

2021年5—11月，采收连翘叶共72批次。采用NIR光谱技术结合偏最小二乘法，建立连翘叶中总黄酮、总酚、连翘酯苷A、连翘苷和芦丁含量的NIR快速检测动态定量模型。

结果

连翘叶中总黄酮、总酚、连翘酯苷A、连翘苷和芦丁动态定量模型的相关系数分别为0.950 1、0.931 1、0.957 3、0.942 9和0.977 3；校正集预测标准偏差分别为1.39、0.68、0.48、0.10和0.07；验证集中预测值与实测值的平均相对误差分别为9.39%、10.27%、16.40%、5.58%和7.96%。5种活性成分的验证集样本的参考值与NIR光谱预测值的配对

检验值分别为1.137、0.127、0.785、1.491和0.150；

值分别为0.269、0.900、0.441、0.149和0.882。5种活性成分含量均在5—6月较高，7月大幅度降低，8月有所回升，9—10月开始降低。初霜后活性成分含量较霜前明显升高，达全年最高，为连翘叶的最佳采收期。

结论

建立的NIR光谱动态定量模型稳定性和准确性较好，且预测准确度良好。此模型可快速预测不同采收期连翘叶中5种活性成分含量及其动态变化规律。

Abstract

Objective

In order to rapidly detect the contents of the five active compounds (total flavonoids

total polyphenols

forsythiaside A

forsythin and rutin) in

Forsythia suspensa

leaves and explore the nature of their annual dynamic changes

we established dynamic quantitative models based on near-infrared(NIR) spectroscopy.

Methods

This study used

suspensa

leaves

which had been collected from May to November 2021(72 batches altogether)

as samples. NIR spectroscopy combined with partial least-squares analysis was used to establish NIR rapid-detection models for the contents of the five active compounds

including total flavonoids

total polyphenols

forsythiaside A

forsythin

and rutin.

Results

The correlation coefficients in the quantitative models of total flavonoids

total polyphenols

forsythiaside A

forsythin

and rutin in

suspensa

leaves were 0.950 1

0.931 1

0.957 3

0.942 9

and 0.977 3

respectively. The standard errors of the validation set were 1.39

0.68

0.48

0.10

and 0.07

while the average relative errors between the predicted values and the measured values of the validation set were 9.39%

10.27%

16.40%

5.58%

and 7.96%. Paired

test values of the reference values of total flavonoids

total polyphenols

forsythiaside A

forsythin

and rutin to their corresponding predicted values of NIR spectroscopy were 1.137

0.127

0.785

1.491

and 0.150

while the

values were 0.269

0.900

0.441

0.149

and 0.882. The content of the five active compounds were higher from May to June

decreased quickly in July

recovered in August

and decreased from September to October. The content of active ingredients increased significantly after the first frost compared to before the frost

reaching the highest during the whole year

suggesting the best period for harvesting

suspensa

leaves.

Conclusion

The NIR quantitative models established in this study were stable

showed efficacy

and displayed great prediction accuracy. This model can quickly predict the content of five active compounds and their dynamic changes in

suspensa

leaves across different harvesting periods.

关键词

连翘叶活性成分近红外光谱技术动态定量模型

Keywords

Forsythia suspensa leavesactive compoundsnear-infrared spectroscopydynamic quantitative model

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