Analytical and Bioanalytical Chemistry
Electronic Supplementary Material
Rapid determination of eight oxoisoaporphine alkaloids in Rhizoma
Menispermi by the optimal homogenate extraction followed by
UPLC-MS/MS
Jinxia Wei, Zhen Jiang, Zhi Cui, Xingjie Guo
1
Experimental
Chemicals, reagents and materials
Table S1 1H NMR (600 MHz), 13C NMR (150 MHz), HMBC and NOE data for compounds 1-2 in
CDCl3
1 δ (ppm) J (Hz)
13
Position
1
H NMR
C
HMBC
NMR
(H→C)
2
8.90,d,J = 5.2
144.5
C(3), C(11b)
3
8.14,d,J = 5.2
119.4
C(3b)
2 δ (ppm) J (Hz)
13
NOE
(H↔H)
H(-OCH3)
C
HMBC
NOE
NMR
(H→C)
(H↔H)
8.65,d,J = 5.1
142.0
C(3), C(11b)
7.53,d,J = 5.1
119.6
C(3b)
H(4)
C(3),C(3b),C(5),C(6)
H(3)
1
H NMR
3a
136.4
126.4
3b
123.0
118.6
4
151.5
7.16,s
10.69,s (5-OH)
108.9
a
5
136.7
6
149.7
150.7
6a
102.9
105.4
7
182.7
183.8
7a
130.9
134.5
8.00,d,J = 2.8
148.5
8
8.54,d,J = 7.8
126.8
C(6a),C(7a),C(9),C(11a)
107.7
9
7.50,t,J = 7.6
127.8
C(7a),C(8)
10
7.69,t,J = 7.6
134.0
C(11),C(11a)
7.39,dd,J = 8.8,2.8
121.7
C(11a)
11
8.55,d,J = 7.8
128.9
C(7a),C(9),C(11a)
8.96,d,J = 8.8
127.0
C(7a)
160.9
11a
133.3
130.3
11b
145.3
143.8
12
-OCH3
6.31,s
102.4
C(5),C(6)
4.29,s
60.3
C(3a)
a
C(10),C(11a)
H(3)
-
-
4.10,s
56.4
C(6)
4.02,s
55.9
C(9)
10.69 represented the chemical shift of phenolic hydroxyl hydrogen (5 position)
Experimental design for the optimization of HGE
In order to optimize the extraction conditions of HGE, the influences of the process parameters were
firstly separately investigated in single factor experiments to limit the total experimental work. Dried
Rhizoma Menispermi (from Neimenggu) was treated under different HGE conditions as shown in
Table S2. Based on the single factor experimental results, major influence factors were selected. Then,
response surface methodology coupled with three factors-three level Box-Behnken response surface
experimental design (BBD) was employed to investigate the individual and interactive effects of
process variables to extract the oxoisoaporphine alkaloids from Rhizoma Menispermi using HGE.
2
Table S2 Factors and Levels for the single factor experimental design
Factors
Levels
Ethanol concentration (%)
50
Extraction time (min)
60
3
6
70*
80
95
*
10
12
8
*
Solvent-to-solid ratio (mL/g)
10
20
Extraction voltage (V)
90
100*
30
40
110
120
Note: The levels marked with asterisk (*) kept constant when other factors were investigated
Three main effective factors, including the ethanol concentration (%, X1), extraction time (min, X2)
and solvent-to-solid ratio (mL/g, X3), were selected as independent variables while the total extraction
yields (μg/g) of the 8 alkaloids were taken as marker to evaluate the extraction efficiency. The factor
levels were coded as −1 (low), 0 (central point or middle) and 1 (high), respectively. Table S3 presents
the design matrix, which requires a total of 17 experimental runs including five replicates at central
point. The experiments were performed in random order to avoid systematic errors. Regression analysis
of the experimental data from BBD to fit a second-order polynomial equation (quadratic model) was
carried out according to the following general equation (Eq. (1)) which was, then, used to predict the
optimum conditions of extraction process.
3
3
3
i =1
i =1
1≤i ≤ j
Y = B 0 + ∑ BiXi + ∑ BiiXi 2 +
∑ B XX
ij
i
j
(1)
where Y represents the predicted response; B0, Bi, Bii, and Bij indicate the regression coefficients for
intercept, linear, quadratic and interactive terms, respectively and Xi and Xj are the independent
variables. The three-dimensional (3D) response surface and contour plots (2D) constructed according
to the fitted polynomial model were used to study the interactive effect of extraction variables on the
yield of oxoisoaporphine alkaloids. Each extraction trial and all the analyses were carried out in
triplicate and all the data in this paper have been reported as means ± SD. Analysis of the experimental
design data and calculation of predicted responses were carried out using Design Expert software 8.05b
software (State-Ease lnc., Minneapolis, MN, USA)
3
Table S3 Box-Behnken design matrix of three variables in coded and natural units along with the
responses measured and predicted values; S/S, solvent to solid
Stda
6
Run
1
Independent variables
Responses
Actual value (coded value)
Extraction efficiency (μg/g)b
X1 Ethanol (%)
X2 Time (min)
X3 S/S ratio (mL/g)
Experimental
Predicted
80 (1)
8 (0)
10 (-1)
245.8
244.6
5
2
60 (-1)
8 (0)
10 (-1)
232.5
237.4
15
3
70 (0)
8 (0)
20 (0)
318.3
310.2
9
4
70 (0)
6 (-1)
10 (-1)
241.1
231.8
10
5
70 (0)
10 (1)
10 (-1)
239.8
245.4
4
6
80 (1)
10 (1)
20 (0)
301.9
297.5
8
7
80 (1)
8 (0)
30 (1)
285.6
280.7
1
8
60 (-1)
6 (-1)
20 (0)
242.0
246.3
17
9
70 (0)
8 (0)
20 (0)
305.0
310.2
7
10
60 (-1)
8 (0)
30 (1)
237.3
238.5
13
11
70 (0)
8 (0)
20 (0)
315.3
310.2
16
12
70 (0)
8 (0)
20 (0)
304.2
310.2
12
13
70 (0)
10 (1)
30 (1)
267.6
276.9
2
14
80 (1)
6 (-1)
20 (0)
248.7
259.1
14
15
70 (0)
8 (0)
20 (0)
308.1
310.2
3
16
60 (-1)
10 (1)
20 (0)
271.5
261.0
17
70 (0)
6 (-1)
30 (1)
243.0
237.4
11
a
Standard order
b
The total extraction yields of oxoisoaporphine alkaloids 1-8
4
Results and discussion
Screening solvent for HGE
Selection of an appropriate extraction solvent is fundamental as it will determine the amount of
extracted alkaloid compounds. In order to obtain an optimal HGE solvent, consideration should be
given to the oxoisoaporphine alkaloids solubility in solvent. Chloroform, ethyl acetate, acetone and
ethanol-water (50:50, 70:30, 100:0, v/v) are the most commonly employed solvents in fat-soluble
alkaloids extraction from botanical materials. For the present work, all of solvents above were tested on
the basis of the total content of 8 alkaloids from Rhizoma Menispermi. Taken into consideration that
ethanol has several advantages over other solvents, including higher extraction efficiency,
environmental compatibility and lower toxicity and cost, ethanol becomes the first choice of HGE
extraction solvent for the subsequent evaluation. Furthermore, aqueous ethanol at different percentages
can affect the extraction efficiency. Hence, the concentration of ethanol was optimized in the further
experiments.
Selection of HGE relevant variables and experimental ranges using single factor experimental design
In a preliminary test, in order to select the most important factors and to determine their levels for
further experiments, various extraction parameters were studied according to the table S2. The results
are shown in Fig.S1.
Ethanol concentration is very important in improving the target ingredients extraction yield of
herbs. It can be seen from Fig. S1A that the extraction yields of 8 alkaloids increased markedly with
increasing ethanol concentration from 50% to 70%. This phenomenon could be explained by the fact
that with the increase in ethanol concentration, solvent polarity declined and the analyte solubilities
increased according to the theory of polarity and intermiscibility. At ethanol concentration over 70%,
the extraction yields were decreased slightly. As a result, the ethanol concentration range 60-80% was
chosen for the RSM trials.
Extraction time also plays a crucial role in the HGE (Fig. S1B). In the early stage of HGE, the
extraction yields of alkaloids were sped up when the homogenate treatment was applied from 2 to 8
min, and then the extraction yields remained constant or slightly decreased for longer extraction time.
The obtained results were probably due to that the particle size of materials become smaller after a
longer extraction time, with consequent increase of surface area and extraction efficiency. Up to a
5
certain extraction time, especially more little particle made subsequent filtration difficult. Therefore,
6-10 min was selected for the RSM study.
Fig. S1C demonstrates a gradual increase of alkaloid yields within the solvent-to-solid ratio range
of 10:1-20:1, with no further increase even at higher solvent-to-material ratio. Large solvent content
may cause complex procedure and unnecessary wasting, while small solvent content may cause
incomplete extraction. In this study, the solvent-to-solid ratio 10:1-30:1, therefore, was chosen for
further optimization experiments.
Extraction voltage is also the key parameter affecting extraction of bioactive components from
plant matrix. As shown in Fig. S1D, the extraction yields of eight alkaloids increased significantly with
the increase of voltage from 90 to 100, which may be due to the fact that the increased extraction
voltage resulting in accelerated crushing of herbs will facilitate permeability or diffusivity of solvent
into material. However, when the voltage exceeded 100V, the extraction yields showed no significant
differences. Hence, a fixed extraction voltage (100V) was selected as the optimal parameter for the
subsequent experiments.
6
Fig. S1 Effects of different factors (A: Ethanol concentration, B: Extraction time, C: Solvent-to-solid
ratio, D: Extraction voltage) on the extraction yields of 8 alkaloids
Optimization of HGE conditions using Box-Behnken design
On the basis of the single factor experimental results, the ethanol concentration (X1), extraction time
(X2) and solvent-to-solid ratio (X3) were selected as independent variables. To further study the optimal
levels and interactions between the factors, we further optimized ethanol concentration (X1), extraction
time (X2) and solvent-to-solid ratio (X3) using the RSM by employing Box-Behnken design. Table S3
shows the results of alkaloid yields obtained in the BBD experiments and the corresponding predicted
values according to the applied second-order regression model.
Fitting the model and optimization of the extraction parameters for total alkaloids
Second-order polynomial model for total alkaloids is developed by applying multiple regression
analysis to the results of the BBD. The mathematical regression model obtained in terms of coded
7
factors is given as follows:
Y = 310.16 + 12.34 X 1 + 13.26 X 2 + 9.29 X 3 + 5.92 X 1 X 2 + 8.76 X 1 X 3 +
6.47 X 2 X 3 − 20.89 X 12 − 23.29 X 22 − 39.00 X 32
In order to determine whether the quadratic model is significant and adequate, it is necessary to run
analysis of variance (ANOVA). The ANOVA for the fitted quadratic polynomial model of total
extraction yields of 8 alkaloids were shown in Table S4.
The ANOVA demonstrated that the proposed model was adequate (p < 0.01), possessing no
significant lack of fit (p > 0.05) and with satisfactory values of the R2 for the yield. The determination
coefficient (R2) of the model is 0.9568, indicated that 95.68% of the variations could be illustrated by
the fitted model and predicted values correlate well with the experimental data. For a well statistical
model, R2 adj should be close to R2. As shown in Table S4, R2 adj was 0.9013, which implied that only
9.87% of the total variations were not explained by the model. The significance of each coefficient
measured using p-value and F-value is listed in Table S4. Smaller p-value and greater F-value mean the
corresponding variables would be more significant.
Table S4 Analysis of variance (ANOVA) for the fitted second-order regression model
Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value (Prob > F)
Model
15554.97
9
1728.33
17.23
0.0006 * significant
X1-Solvent
1218.20
1
1218.20
12.14
0.0102 *
X2-Time
1405.83
1
1405.83
14.01
0.0072 **
X3-Ratio
690.25
1
690.25
6.88
0.0343 *
X 1X 2
140.42
1
140.42
1.40
0.2754
X 1X 3
306.95
1
306.95
3.06
0.1237
X 2X 3
167.57
1
167.57
1.67
0.2372
X1
2
1836.78
1
1836.78
18.31
0.0037 **
X2
2
2283.65
1
2283.65
22.76
0.0020 **
X3
2
6403.80
1
6403.80
63.84
<0.0001 **
Residual
702.23
7
100.32
Lack of fit
542.28
3
180.76
4.52
0.0895 not significant
Pure error
159.94
4
39.99
Cor total
16257.20
16
2
R = 95.68%
Adj R2 = 90.13%
C.V. % = 3.70%
* Significant at 0.05 level
** Significant at 0.01 level
8
To consider the effects of the independent variables and their mutual interactions on the alkaloids
yield, the three dimensional response surface plots and two dimensions contour plots are constructed
according to the fitted model. Fig. S2 present the plots with one variable kept at medium level and the
other two within the tested range. All the three surfaces are upper convex, with a maximum point in the
center of the experimental domain. Using the derived model, the optimal calculated values of the
variables affecting the extraction yield of alkaloids were ethanol concentration of 73.87%, extraction
time of 8.72min, and solvent-to-solid ratio of 21.92:1. On the basis of the single factor experimental
results, the extraction voltage is set at 100 V.
Verification of the predictive models
Based on the above studies, the experimental optimal parameters were simplified as ethanol
concentration of 74%, extraction time of 9.0 min, and solvent-to-solid ratio of 22:1. It was of great
necessaries to validate the accuracy and reliability of the model equation for predicting an optimum
response value. In this study, the verification experiment was performed in five replicate under the
optimal extraction conditions. In this case, the average real alkaloids yield was 319.1 ± 4.2 µg/g, which
is in close agreement with the predicted maximum response of 315.8 μg/g. The good correlation
between these results confirmed that the regression model was adequate to reflect the expected
optimization.
Fig. S2 3D surface and contour plot showing the effect of (A) extraction time (min) and Ethanol
concentration (%); (B) Solvent-to-solid ratio (mL/g) and Ethanol concentration (%); (C)
Solvent-to-solid ratio (mL/g) and extraction time (min) on the total extraction efficiency of the
8 alkaloids from Rhizoma Menispermi
9
Comparison of HGE with other extraction methods
In order to evaluate the extraction efficiency of the proposed HGE method, ultrasonic-assisted
extraction (UAE, Kunshan Ultrasonic Instrument Co. Ltd., China), heating reflux extraction (HRE) and
infusion extraction (IE) were investigated for the extraction of alkaloids from Rhizoma Menispermi
(the sample from Neimenggu). Table S5 presents the levels of total content of the 8 alkaloids by
different methods. It can be seen that the HGE proved to be superior to the HRE and IE methods in
terms of the extraction yields of 8 alkaloids. Compared with UAE method, HGE method took shorter
time and less solvent. Taken together, HGE is considered a rapid, effectiveness, environmental-friendly
and economic method for the extraction of alkaloids from Rhizoma Menispermi.
Table S5 Comparison of HGE with other extraction methods (n = 3)
Extraction
Sample
Extraction
Solvent
Extraction
Extraction
The total content
methods
(g)
solvent
consumption (mL/g)
temperature (°C)
time (min)
of 8 alkaloids (μg/g)
HGE
1.0
74% ethanol
22
room temperature
9
319.1
UAE
1.0
75% ethanol
100
room temperature
40
304.3
HRE
1.0
75% ethanol
50
62
180
270.3
IE
1.0
75% ethanol
100
room temperature
120
262.2
10