Optimization on the Main Active Components Extraction Using Ultrasonic-assisted Method from Safflower

doi:10.7525/j.issn.1673-5102.2021.05.021

Abstract

Abstract:

To extract the main components from Carthamus tinctorius L.， the Box-Behnken design method and response surface methodology were used to optimize the extraction process of C. tinctorius L.； Hydroxy-safflower yellow A and kaempferol-3-O-rutinoside were the main flavonoids in C. tinctorius L. petals . The results showed that Ultrasonic extraction method were the optimal extraction technology： using 57% methanol as solvent， liquid-material ratio was 0.1∶25 g·mL^-1， ultrasonic temperature was 70℃， extraction time was 45 min， ultrasound power was 177 W， Under these conditions， hydroxy-safflower yellow A extraction yield was 1.74%， kaempferol-3-O-rutinoside extraction yield was 1.53%. The safflower main ingredient extraction was recommended to use ultrasonic extraction.

Key words: Carthamus tinctorius L., Hydroxy-safflower yellow A, kaempferol-3-O-rutinoside, extraction technology, response surface methodology

CLC Number:

Q949.783.5

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.

Figures/Tables 17

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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

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

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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