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In order to establish the extraction technology of flavonoids from

Flavonoids, which are a class of compound with a 2-phenylchromanthone structure and C6-C3-C6 as basic carbon scaffold, are usually combined with sugars to form glycosides in plants and a small part in the form of free (glycosides) [

Thus, single-factor experiments were designed to improve the extraction rate of flavonoids in

Preparation of rutin standard solution was as follows: approximately 10 mg of rutin standard was accurately weighed, placed in a 50 mL volumetric flask, diluted with 70% ethanol to constant volume, and shaken to obtain the mass concentration of 0.2 mg/mL rutin standard solution.

Drawing of the standard curve was as follows: rutin standard solutions of 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 mL were precisely drawn and placed in 10 mL plugged test volumetric flasks. Subsequently, 70% ethanol was added to 5ml; then 0.4 mL of 5% NaNO_{2} solution was added and placed for 6 min. Approximately 0.4 mL of 10% Al (NO_{3})_{3} solution was added and then shaken for 6 min, and 4 mL of 4% NaOH solution was added and placed for 15 min. Finally, the reagent blank was used as reference to measure the absorbance at 510 nm. A standard curve was drawn to obtain a linear regression equation for the rutin standard curve.

Determination of total flavonoids was as follows: the sample was accurately obtained and processed a similar method to the standard. The result was then brought into the standard [

A single-factor experiment was executed to estimate the optimal range of each influencing factor. The effects of the ethanol concentration (35%–85%), number of extractions (1–5), extraction time (0.5–2.5 h), and liquid ratio (1:10–1:60) on the extraction rate of flavonoids were investigated [

The effects of four factors on flavonoid extraction were evaluated by the PBD (Table

PBD and results.

Run | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Response |
---|---|---|---|---|---|

Liquid Ratio _{1}) | Time _{2}) | Number _{3}) | Ethanol _{4}) | Flavonoid | |

1 | 1:60 | 2.5 | 3 | 80 | 24.58 |

2 | 1:40 | 2.5 | 5 | 70 | 24.96 |

3 | 1:60 | 1.5 | 5 | 80 | 32.28 |

4 | 1:40 | 2.5 | 3 | 80 | 26.18 |

5 | 1:40 | 1.5 | 5 | 70 | 24.45 |

6 | 1:40 | 1.5 | 3 | 80 | 22.04 |

7 | 1:60 | 1.5 | 3 | 70 | 22.58 |

8 | 1:60 | 2.5 | 3 | 70 | 20.98 |

9 | 1:60 | 2.5 | 5 | 70 | 27.31 |

10 | 1:40 | 2.5 | 5 | 80 | 30.68 |

11 | 1:60 | 1.5 | 5 | 80 | 32.28 |

12 | 1:40 | 1.5 | 3 | 70 | 21.98 |

The steepest ascent design determined the best areas of these key factors. By analyzing the positive and negative effects on the results of the PBD, the step length and null point of the extraction concentration and number of extractions were 70% and 1.5 and 1 and 1, respectively (Table

Path of steepest ascent experimental design and results.

Run | Factor 1 | Factor 2 | Response |
---|---|---|---|

Number | Ethanol | Flavonoid | |

null point(0) | 1 | 70 | |

step length(Δ) | 1 | 1.5 | |

1 | 1 | 70 | 12.94 |

2 | 2 | 71.5 | 20.26 |

3 | 3 | 73 | 22.67 |

4 | 4 | 74.5 | 28.49 |

5 | 5 | 76 | 30.27 |

| | | |

7 | 7 | 79 | 31.13 |

On the basis of the results of the steepest ascent design, the optimal ethanol concentration was 77.5% and the number of extractions was six [

Central composite design and results.

Run | Factor 1 | Factor 2 | Response |
---|---|---|---|

Number | Ethanol | Flavonoid | |

1 | 5 | 76% | 30.22 |

2 | 5 | 79% | 31.68 |

3 | 7 | 76% | 26.27 |

4 | 7 | 79% | 29.18 |

5 | 6 | 75% | 26.40 |

6 | 6 | 80% | 29.90 |

7 | 5 | 78% | 31.40 |

8 | 7 | 78% | 28.40 |

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Three parallel experiments were conducted on the optimal extraction conditions determined by CCD experiments. The resulting values were averaged to obtain the final results.

The obtained extract was concentrated to 0.4 g/mL crude drug, centrifuged for 30 min at 8000 r/min for clarification, and washed off with 50% ethanol by ADS-17 macroporous resin. The extract was concentrated to 2, 3, 4, 5, and 6 mg/mL, and

L-Tyrosine and L-DOPA were used as substrates, and four groups of reaction liquids were accurately absorbed into 96-well plates according to the volume presented in Table _{0}, A_{1},

Loading table.

Reagent /Number | A_{0} | A_{1} | | |
---|---|---|---|---|

L-tyrosine /L-dopa | 100 | 0 | 100 | 0 |

Tyrosinase | 30 | 30 | 30 | 30 |

Inhibitor | 0 | 0 | 10 | 10 |

Buffer | 70 | 170 | 60 | 160 |

The results of the PBD, steepest ascent design, and CCD were analyzed by Design Expert.V.8.0.6 software. Other relevant data were analyzed through ANOVA by SPSS 21.0 and considered significant at p < 0.05 and highly significant at p < 0.01.

The results of single-factor optimization experiments are shown in Figure

Effect of reaction conditions on the extraction efficiency of flavonoids: (a) extraction time, (b) number of extractions, (c) ethanol concentration, and (d) liquid ratio.

On the basis of the results of single-factor optimization experiments and the influence of extraction conditions, four factors and two levels were used to filter the ethanol concentration, number of extractions, extraction time, and liquid ratio, as shown in Table

Final equation in terms of coded factors:

Flavonoid = +25.86 + 2.80 × C + 2.15 × D

Final equation in terms of actual factors:

Flavonoid = −17.57651 + 2.80253 × Number + 0.42966 × Methanol

ANOVA and R-squared for selected factorial model.

Source | Sum of squares | Mean | F | p-value | ||
---|---|---|---|---|---|---|

df | Square | Value | Prob>F | |||

Model | 149.63 | 2 | 74.82 | 24.96 | 0.0002 | significant |

C-number | 94.25 | 1 | 94.25 | 31.44 | 0.0003 | |

D-Methanol | 55.38 | 1 | 55.38 | 18.48 | 0.002 | |

Residual | 26.98 | 9 | 3.00 | |||

Cor Total | 176.61 | 11 | ||||

Std. Dev. | 1.73 | |||||

Mean | 25.86 | |||||

C.V.% | 6.70 | |||||

PRESS | 47.96 | |||||

R-Squared | 0.8472 | |||||

Adj R-Squared | 0.8133 | |||||

Pred R-Squared | 0.7284 | |||||

Adeq Precisior | 11.438 |

In line with the results of the PBD experiment, the regression model obtained by software analysis showed a significant R-squared of 0.8472; thus, the equation was consistent with the actual situation [

The path of steepest ascent was used to approach the optimal region of the two aforementioned factors [

The results of climbing experiment showed that the flavonoid content gradually increased with the number of extractions and ethanol concentration. When the number of extractions was six and the ethanol concentration was 77.5%, the highest value was reached at 33.81 mg/mL, and the extracted flavonoid content further improved [

Through the PBD and steepest ascent design experiment, the significant parameters [

Final equation in terms of coded factors:

Flavonoid = +35.26 +1.16 × A −1.34 × B + 0.36 × A × B − 3.48 × A^{2} − 2.60 × B^{2}

Final equation in terms of actual factors:

Flavonoid = −9278.13580 + 238.81473 × Methanol + 11.08481 × Number + 0.24249 × Methanol × Number − 1.54512 × Methanol^{2} − 2.60128 × Number^{2}

ANOVA and R-squared for the selected factorial model.

Source | Sum of squares | Mean | F | p-value | ||
---|---|---|---|---|---|---|

df | Square | Value | Prob>F | |||

Model | 142.41 | 5 | 28.48 | 23.24 | 0.0003 | Significant |

A- Methanol | 10.85 | 1 | 10.85 | 8.85 | 0.0207 | |

B- number | 14.31 | 1 | 14.31 | 11.68 | 0.0112 | |

AB | 0.53 | 1 | 0.53 | 0.43 | 0.5321 | |

A^{2} | 84.08 | 1 | 84.08 | 68.59 | <0.0001 | |

B^{2} | 47.07 | 1 | 47.07 | 38.40 | 0.0004 | |

Residual | 8.58 | 7 | 1.23 | |||

Lack of Fit | 0.84 | 3 | 0.28 | 0.15 | 0.9275 | Not Significant |

Pure Error | 7.74 | 4 | 1.93 | |||

Cor Total | 150.99 | 12 | ||||

Std. Dev. | 1.11 | |||||

Mean | 31.52 | |||||

C.V.% | 3.51 | |||||

PRESS | 18.09 | |||||

R-Squared | 0.9432 | |||||

Adj R-Squared | 0.9026 | a | ||||

Pred R-Squared | 0.8802 | |||||

Adeq Precisior | 11.890 |

ANOVA was used to evaluate the optimization results. A small p-value indicates a significant influence on the response variables [^{2} and B^{2} both had the negative coefficient. The lack of fit was not significant, and the goodness of fit of the model was determined by estimating the variance of the coefficients (R^{2}), which was observed as 0.9432; thus, 94.32% of this factor could be interpreted by fitting the model [

In this study, 3D curved surface (Figure

3D response surface plots (a) and 2D contour plots (b).

A confirmatory experiment was performed based on the optimal extraction conditions obtained from the aforementioned experiments. A method was considered credible if the percentage error was less than 10% [

Verification of the experimental results.

Category | Run | Yield (mg/50mL) | STDEV (%) | |
---|---|---|---|---|

Predictive | Experimental | |||

Flavonoids | 1 | 35.51 | 35.41 | 4.2% |

2 | 35.51 | 35.13 | ||

3 | 35.51 | 35.81 |

In addition, our research group found that the flavonoids in

Flavonoids in

Herb | Flavonoids |
---|---|

| Apigenin-6, 8- two -C- |

Apigenin-6-C- | |

Isophora | |

Schaftoside | |

Apigenin-6-C- | |

Apigenin-6-C- | |

Apigenin-6,8-di-C- | |

Apigenin-6-C- |

L-Tyrosine and L-dopa were used as substrates to detect the inhibitory activity of tyrosinase and

(a)–(f) Monophenolase activity inhibition rates at 10, 30, 60, 90, 120, and 240 min, respectively. (g)–(l) Diphenolase activity inhibition rates at 10, 30, 60, 90, 120, and 240 min, respectively. ^{##}^{#}^{&&}^{ &}

Six time points were tested to comprehensively understand the inhibition of tyrosine monophenolase activity (Figures

Figures

This study investigated the extraction of flavonoids by RSM from the leaves of

The results showed that the flavonoid extracts from the leaves of

However, this experiment still had some limitations. The composition of flavonoid extracts can be further analyzed in the future, and the types of interaction between flavonoid extracts and tyrosinase can be further researched on cellular and overall levels.

The data used to support the findings of this study are available from the corresponding author upon request.

Haixia Lu and Ke Yang contributed equally to this work and are co-first authors.

The authors declare that they have no conflicts of interest.

This study was supported by the National Key Research and Development Program (no. 2017YFC1702200), the National Science Foundation of China (no. 81673638, no. 81874352, no. 81703772, no. 81803760, and no. 81803761), the Funding for Young Talents Project of Zhejiang University of Technology (no. GY17034148004), the National Science and Technology on New Drug Creation and Development Projects (no. 2011ZX09101-002-07), and the Zhejiang Provincial Key Laboratory Project (no. 2012E10002).