植物研究 ›› 2021, Vol. 41 ›› Issue (5): 824-835.doi: 10.7525/j.issn.1673-5102.2021.05.021
李钰1, 杨晓1, 叶霄1, 邓洁琼1, 黄位年1, 代沙2()
收稿日期:
2020-08-11
出版日期:
2021-09-20
发布日期:
2021-07-05
通讯作者:
代沙
E-mail:daisweet@foxmail.com
作者简介:
李钰(1988—),女,助理研究员,主要从事中药材遗传育种方面的研究。
基金资助:
Yu LI1, Xiao YANG1, Xiao YE1, Jie-Qiong DENG1, Wei-Nian HUANG1, Sha DAI2()
Received:
2020-08-11
Online:
2021-09-20
Published:
2021-07-05
Contact:
Sha DAI
E-mail:daisweet@foxmail.com
About author:
LI Yu(1987—),female,assistant researcher,mainly engaged in research on genetic breeding of Chinese medicinal materials.
Supported by:
摘要:
采用HPLC-UV测定红花提取物中黄酮类成分含量,确定评价指标,通过单因素试验设计,筛选红花活性成分提取方法;利用Box-Behnken试验设计原理,以花瓣中主要黄酮类成分为响应值,以单因素结果所选因素为自变量,建立响应面分析试验模型,优化提取工艺。红花花瓣中主要黄酮类成分为羟基红花黄色素A和山奈酚-3-O-芸香糖苷,占比85%以上;红花活性成分提取方法应采用超声提取法,响应面优化的最佳提取条件为:料液比0.1∶25 g·mL-1、超声温度70℃、超声时间45 min、溶剂为57%甲醇、超声功率177 W,在该最优条件下红花黄色素A提取率为1.74%,山奈酚-O-β-芸香糖苷提取率为1.53%。该方法羟基红花黄色素A提取率比药典中提取方法显著提高,其余黄酮类成分提取率也呈极显著增加。
中图分类号:
李钰, 杨晓, 叶霄, 邓洁琼, 黄位年, 代沙. 超声辅助提取红花中主要活性成分的工艺优化[J]. 植物研究, 2021, 41(5): 824-835.
Yu LI, Xiao YANG, Xiao YE, Jie-Qiong DENG, Wei-Nian HUANG, Sha DAI. Optimization on the Main Active Components Extraction Using Ultrasonic-assisted Method from Safflower[J]. Bulletin of Botanical Research, 2021, 41(5): 824-835.
表3
响应面试验方案与结果
序号 No. | X1 | X2 | X3 | X4 | X5 | 羟基红花黄色素A Hydroxysafflor yellow A(%) | 山奈酚-3-O-芸香糖苷 Kaempferol-3-O-rutinoside(%) |
---|---|---|---|---|---|---|---|
1 | 0 | 0 | 1 | 1 | 0 | 1.06 | 0.95 |
2 | 1 | 0 | 0 | 1 | 0 | 0.90 | 0.88 |
3 | 0 | -1 | 1 | 0 | 0 | 1.57 | 1.46 |
4 | 0 | 1 | -1 | 0 | 0 | 1.59 | 1.48 |
5 | 0 | 0 | 0 | -1 | -1 | 1.59 | 1.11 |
6 | 0 | 0 | -1 | 0 | -1 | 1.63 | 1.49 |
7 | 0 | 0 | -1 | 1 | 0 | 1.03 | 0.94 |
8 | 1 | 0 | -1 | 0 | 0 | 1.60 | 1.46 |
9 | 0 | -1 | 0 | 1 | 0 | 0.97 | 0.89 |
10 | 0 | 0 | 1 | 0 | 1 | 1.58 | 1.49 |
11 | 0 | 0 | 1 | -1 | 0 | 1.57 | 1.28 |
12 | 1 | 0 | 0 | 0 | 1 | 1.60 | 1.47 |
13 | 0 | -1 | -1 | 0 | 0 | 1.58 | 1.48 |
14 | 0 | 0 | 0 | 0 | 0 | 1.63 | 1.49 |
15 | 0 | 1 | 0 | 0 | -1 | 1.60 | 1.48 |
16 | 0 | 0 | -1 | 0 | 1 | 1.59 | 1.46 |
17 | 1 | 0 | 1 | 0 | 0 | 1.62 | 1.55 |
18 | 0 | 0 | -1 | -1 | 0 | 1.51 | 1.42 |
19 | 0 | 0 | 0 | 0 | 0 | 1.58 | 1.48 |
20 | -1 | 0 | -1 | 0 | 0 | 1.59 | 1.48 |
21 | 0 | 1 | 0 | 1 | 0 | 1.04 | 0.93 |
22 | 1 | 0 | 0 | 0 | -1 | 1.57 | 1.46 |
23 | 0 | 1 | 1 | 0 | 0 | 1.64 | 1.50 |
24 | -1 | 0 | 0 | -1 | 0 | 1.59 | 1.42 |
25 | 0 | -1 | 0 | -1 | 0 | 1.57 | 1.09 |
26 | -1 | 1 | 0 | 0 | 0 | 1.65 | 1.48 |
27 | 1 | 0 | 0 | -1 | 0 | 1.50 | 1.39 |
28 | 0 | 1 | 0 | -1 | 0 | 1.61 | 1.07 |
29 | 0 | 0 | 0 | 0 | 0 | 1.58 | 1.50 |
30 | 0 | 0 | 0 | -1 | 1 | 1.44 | 0.97 |
31 | 0 | 0 | 0 | 0 | 0 | 1.55 | 1.49 |
32 | -1 | 0 | 0 | 0 | -1 | 1.66 | 1.51 |
33 | -1 | -1 | 0 | 0 | 0 | 1.66 | 1.49 |
34 | 0 | 0 | 0 | 1 | -1 | 0.91 | 0.91 |
35 | -1 | 0 | 0 | 1 | 0 | 1.30 | 1.08 |
36 | 1 | -1 | 0 | 0 | 0 | 1.57 | 1.46 |
37 | 0 | 0 | 1 | 0 | -1 | 1.72 | 1.62 |
38 | 0 | 0 | 0 | 1 | 1 | 1.03 | 0.95 |
39 | 0 | 0 | 0 | 0 | 0 | 1.58 | 1.50 |
40 | 1 | 1 | 0 | 0 | 0 | 1.58 | 1.48 |
41 | -1 | 0 | 0 | 0 | 1 | 1.62 | 1.50 |
42 | 0 | 1 | 0 | 0 | 1 | 1.60 | 1.50 |
43 | 0 | 0 | 0 | 0 | 0 | 1.54 | 1.49 |
44 | 0 | -1 | 0 | 0 | 1 | 1.56 | 1.46 |
45 | -1 | 0 | 1 | 0 | 0 | 1.72 | 1.56 |
46 | 0 | -1 | 0 | 0 | -1 | 1.57 | 1.46 |
表4
HSYA优化回归模型方差分析
方差来源 Source | 自由度 Degree of freedom | 偏差平方和 Deviation sum of squares | 均方差 Mean square | F值 F-value | P值 P value |
---|---|---|---|---|---|
模型Model | 9 | 2.23 | 0.25 | 121.43 | <0.000 1 |
X1 | 1 | 0.045 | 0.045 | 22.17 | <0.000 1 |
X3 | 1 | 0.008 1 | 0.008 1 | 3.98 | 0.053 7 |
X4 | 1 | 1.07 | 1.07 | 525.96 | <0.000 1 |
X5 | 1 | 0.003 306 | 0.003 306 | 1.62 | 0.210 8 |
X1X4 | 1 | 0.024 | 0.024 | 11.80 | 0.001 5 |
X4X5 | 1 | 0.018 | 0.018 | 8.95 | 0.005 0 |
X12 | 1 | 0.013 | 0.013 | 6.33 | 0.016 4 |
X32 | 1 | 0.006 324 | 0.006 324 | 3.11 | 0.086 5 |
X42 | 1 | 0.93 | 0.93 | 455.43 | <0.000 1 |
纯误差Pure error | 5 | 0.004 933 | 0.0 009 867 | ||
失拟相Lack of fit | 31 | 0.068 | 0.002 206 | 2.24 | 0.188 1 |
总和Sum total | 45 | 2.3 | |||
回归系数R2 Regression coefficient | 0.968 1 | ||||
信噪比 Signal-noise ratio | 37.861 | ||||
CV(%) | 3.02 |
表5
山奈酚-3-O-芸香糖苷优化回归模型方差分析
方差来源 Source | 自由度 Degree of freedom | 偏差平方和 Deviation sum of squares | 均方差 Mean square | F值 F-value | P值 P value |
---|---|---|---|---|---|
模型Model | 6 | 2.12 | 0.11 | 53.38 | <0.000 1 |
X2 | 1 | 0.001 056 | 0.001 056 | 0.17 | 0.678 5 |
X4 | 1 | 0.31 | 0.31 | 50.86 | <0.000 1 |
X5 | 1 | 0.003 6 | 0.003 6 | 0.59 | 0.445 4 |
X22 | 1 | 0.033 | 0.033 | 5.38 | 0.025 7 |
X42 | 1 | 1.8 | 1.8 | 296.94 | <0.000 1 |
X52 | 1 | 0.021 | 0.021 | 3.54 | 0.067 2 |
纯误差Pure error | 5 | 0.0 002 833 | 0.00 005 667 | ||
失拟相Lack of fit | 34 | 0.24 | 0.006 939 | 122.46 | <0.000 1 |
总和Sum total | 45 | 2.36 | |||
回归系数R2 Regression coefficient | 0.899 8 | ||||
信噪比 Signal-noise ratio | 20.782 | ||||
CV(%) | 5.77 |
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