Material attributes
The quality attributes of different Astragali radix concentrates are shown in Table 2. The electrical conductivity was between 1442 μS/cm and 2390 μS/cm, indicating different electrolyte contents in concentrates. The content of Astragaloside IV and Astragaloside Ⅱ was lower than 2000 μg/g dry matter. The content of CG among the three flavonoid contents was the highest, which can exceed 1600 μg/g. The other flavonoids were less than 1000 μg/g dry matter. The sucrose content was higher than the D-fructose content, which could be more than 700 mg/g dry matter. At most occasions, sucrose was the main component of dry matter. The D-fructose content was lower than 30 mg/g dry matter.
Table 2. Quality attributes different batches of Astragali radix concentrates
Concentrate number
|
Electrical conductivity (dry matter content of 2%)
(Z8, μS/cm )
|
Contents of flavonoids and saponins (μg/g dry matter)
|
Sugar contents (dry matter)
|
Astragaloside IV
(Z1)
|
Astragaloside Ⅱ
(Z2)
|
CG
(Z3)
|
PG (Z4)
|
IFG
(Z5)
|
D-fructose
(Z6)
|
Sucrose
(Z7)
|
N1
|
1944
|
1062
|
1200
|
1119
|
641.6
|
471.5
|
25.03
|
568.0
|
N2
|
1832
|
1020
|
763.4
|
1490
|
688.7
|
725.6
|
27.26
|
489.0
|
N3
|
2390
|
1887
|
1948
|
1550
|
1000
|
881.2
|
11.07
|
616.2
|
N4
|
1995
|
928.6
|
1093
|
1693
|
794.2
|
603.4
|
25.48
|
631.8
|
N5
|
1490
|
390.0
|
478.9
|
1121
|
272.9
|
228.6
|
18.54
|
714.4
|
N6
|
1667
|
247.1
|
314.3
|
1390
|
303.0
|
250.3
|
24.35
|
732.7
|
N7
|
1597
|
321.2
|
558.7
|
839.2
|
190.0
|
288.4
|
19.65
|
706.5
|
N8
|
1442
|
314.7
|
291.5
|
1067
|
207.4
|
179.2
|
20.02
|
695.6
|
N9
|
1655
|
284.5
|
562.1
|
1401
|
341.9
|
191.4
|
15.82
|
670.0
|
N10
|
1534
|
371.4
|
178.3
|
1445
|
299.3
|
198.8
|
23.66
|
754.0
|
The identification of CMAs
The results of the CMA identification experiments are shown in Table 3. Though process conditions were fixed, the experimental results were quite different, indicating that the material attributes significantly affected the performance of Astragali radix ethanol precipitation process.
Table 3 CMA identification results
Experimental No.
|
Concentrates
|
Purity of flavonoids and saponins in the supernatant (μg/g dry mater)
|
Dry matter removal (Y6)
|
Astragaloside IV (Y1)
|
Astragaloside Ⅱ (Y2)
|
CG (Y3)
|
PG (Y4)
|
IFG
(Y5)
|
1
|
N1
|
1997
|
2294
|
2198
|
1408
|
1047
|
0.594
|
2
|
N2
|
1406
|
1024
|
2116
|
1161
|
973.0
|
0.464
|
3
|
N3
|
2985
|
2905
|
2501
|
1722
|
1527
|
0.471
|
4
|
N4
|
1366
|
1519
|
2468
|
1266
|
951.5
|
0.566
|
5
|
N5
|
563.4
|
661.2
|
1972
|
501.2
|
372.6
|
0.702
|
6
|
N6
|
415.2
|
420.0
|
2145
|
451.3
|
420.7
|
0.436
|
7
|
N7
|
749.2
|
722.0
|
1350
|
420.9
|
573.4
|
0.616
|
8
|
N8
|
437.2
|
457.8
|
1754
|
354.4
|
305.0
|
0.422
|
9
|
N9
|
614.0
|
781.3
|
2324
|
579.7
|
472.4
|
0.619
|
10
|
N10
|
424.6
|
239.4
|
1967
|
427.1
|
302.9
|
0.343
|
The correlation analysis of material attributes was carried out to find attributes with similar trends [34]. The Pearson coefficients are shown in Table 4. The Pearson coefficients among Astragaloside IV content (Z1), Astragaloside Ⅱ content (Z2), PG content (Z4), IFG content (Z5) and electrical conductivity (Z8) was higher than 0.90. It means that one of them can roughly represent other three material attributes because they contained similar information. Electrical conductivity (Z8) was selected as the potential CMAs in the four material attributes because it is easy to measure. Other potential CMAs are CG (Z3), D-fructose content (Z6), and sucrose content (Z7).
Table 4 Pearson correlation coefficient of materials attributes and P value of significance test
|
Z1
|
Z2
|
Z3
|
Z4
|
Z5
|
Z6
|
Z7
|
Z2
|
0.948
(0.000)
|
|
|
|
|
|
|
Z3
|
0.454
(0.188)
|
0.359
(0.308)
|
|
|
|
|
|
Z4
|
0.947
(0.000)
|
0.906
(0.000)
|
0.660
(0.038)
|
|
|
|
|
Z5
|
0.941
(0.000)
|
0.867
(0.001)
|
0.530
(0.115)
|
0.947
(0.000)
|
|
|
|
Z6
|
-0.243
(0.499)
|
-0.371
(0.291)
|
0.099
(0.786)
|
-0.077
(0.833)
|
-0.062
(0.866)
|
|
|
Z7
|
-0.665
(0.036)
|
-0.583
(0.077)
|
-0.287
(0.421)
|
-0.705
(0.023)
|
-0.761
(0.011)
|
-0.247
(0.492)
|
|
Z8
|
0.943
(0.000)
|
0.960
(0.000)
|
0.547
(0.102)
|
0.953
(0.000)
|
0.907
(0.000)
|
-0.241
(0.502)
|
-0.585
(0.075)
|
Stepwise regression method was used to determine CMAs [35]. In this method, the term left in linear equations after stepwise regression indicates a CMA [35]. The ANOVA results of multiple linear regression analysis of each CQA using Eq. (3) are shown in Table 5. The determination coefficient (R2) of each model was higher than 0.70, indicating that the models can explain most of the variation of experimental data. However, these potential CMAs have no significant effect on the dry matter removal. It means that the determined material attributes were not main factors that influencing dry matter removal. According to the terms left in models, the CG content (Z3), the sucrose content (Z7), and the electrical conductivity (Z8) were found to be CMAs.
Table 5 Regression coefficient values, determination coefficients and ANOVA results
Process parameters
|
Y1
|
Y2
|
Y3
|
Y4
|
Y5
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Constant
|
-3401.126
|
|
-3301.511
|
|
628.697
|
|
-277.322
|
|
-487.500
|
|
Z3
|
-0.8486
|
0.0381*
|
-1.1012
|
0.0251*
|
1.1062
|
0.0021*
|
|
|
-0.2446
|
0.0129*
|
Z7
|
|
|
|
|
|
|
-1.8769
|
0.0148*
|
-1.1572
|
0.0024*
|
Z8
|
3.1972
|
<0.0001**
|
3.3332
|
< 0.0001**
|
|
|
1.3343
|
< 0.0001**
|
1.2904
|
< 0.0001**
|
R2
|
0.9482
|
0.9335
|
0.7126
|
0.9584
|
0.9915
|
* p<0.05
** p<0.01
The effects of CMAs and CPPs
The partial regression coefficients and variance analysis results of the models are shown in Table 6. The P value of each model was less than 0.05, indicating that the model was significant. The adjusted determination coefficient (R2adj) of each model was higher than 0.84. The contour plots were obtained to analyze the effects of CPPs on CQAs, as shown in Figure 3-6. The dry matter removal increased as dry matter contents increased. The purity of CG decreased as as temperature increased. The purity of Astragaloside IV was mainly affected by CMAs.The dry matter removal was mainly affected by CPPs. The purity of other flavonoids and saponins was affected by both CPPs and CMAs.
Table 6. ANOVA results for multiple regression models
Process parameters
|
Y1
|
Y2
|
Y3
|
Y4
|
Y5
|
Y6
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Coefficient
|
P value
|
Constant
|
-346.607
|
|
325052.805
|
|
21493.781
|
|
-15523.097
|
|
5054.318
|
|
-0.7618
|
|
X1
|
|
|
|
|
-54.739
|
0.6925
|
683.552
|
0.6011
|
-80.341
|
0.3744
|
-0.0014
|
0.0170
|
X2
|
|
|
-9928.885
|
0.0200
|
-9026.701
|
0.0203
|
1888.456
|
0.7591
|
-2716.876
|
0.7914
|
0.1024
|
0.0017
|
X3
|
|
|
-7009.760
|
0.0804
|
-221.201
|
0.6957
|
|
|
|
|
0.0131
|
0.0062
|
X4
|
|
|
|
|
-181.541
|
0.0020
|
-10.598
|
0.0174
|
|
|
-0.0449
|
0.3841
|
Z3
|
|
|
-0.614
|
0.0108
|
|
|
|
|
|
|
|
|
Z7
|
-2.816
|
0.0324
|
|
|
|
|
-1.946
|
0.0147
|
-3.419
|
0.0037
|
|
|
Z8
|
1.846
|
0.0006
|
2.889
|
<0.0001
|
|
|
0.691
|
0.0053
|
0.810
|
0.0025
|
|
|
X1X2
|
|
|
|
|
|
|
|
|
60.954
|
0.0350
|
|
|
X1X4
|
|
|
|
|
3.365
|
0.0834
|
|
|
|
|
0.0005
|
0.0532
|
X2X3
|
|
|
110.205
|
0.0045
|
152.158
|
0.0213
|
|
|
|
|
|
|
X12
|
|
|
|
|
|
|
-7.643
|
0.0386
|
|
|
|
|
X22
|
|
|
|
|
1543.544
|
0.0278
|
-622.660
|
0.0826
|
|
|
|
|
X32
|
|
|
37.338
|
0.0102
|
|
|
|
|
|
|
|
|
X42
|
|
|
|
|
|
|
|
|
|
|
0.0007
|
0.0128
|
R2
|
0.8703
|
0.9872
|
0.9431
|
0.9844
|
0.9449
|
0.9262
|
R2adj
|
0.8443
|
0.9744
|
0.8633
|
0.9625
|
0.9005
|
0.8524
|
P value
|
<0.0001
|
<0.0001
|
0.0076
|
0.0003
|
0.0003
|
0.0036
|
Design space development
A Monte Carlo method was performed using a self-coded MATLAB program (R2016a, Version 9.0, The Math Works Inc., USA) to calculate the design space based on the specific goals of process CQAs. The calculation process was introduced in previous work [33]. The acceptable ranges of the CQAs and the probability requirements for compliance are shown in Table 7. 1000 simulations were carried out to get the probability of every possible condition.
Table 7. The lower limits of process CQAs and probability requirements for compliance
Process CQAs
|
Minimum
|
Acceptable probability of design space
|
Dry matter removal (%)
|
40
|
≥90%
|
Purity of Astragaloside IV (μg/g)
|
800
|
Purity of Astragaloside Ⅱ(μg/g)
|
700
|
Purity of CG (μg/g)
|
1800
|
Purity of PG (μg/g)
|
600
|
Purity of IFG (μg/g)
|
600
|
The conditions of design space were listed in Table S2, and shown in Figure 7(a) - (d). The design space was an irregular region.
Control strategy of Astragali radix concentrates
In order to obtain a satisfactory supernatant, Inequalities (1) should be satisfied for CQA requirements listed in Table 7.

where superscripts refer to dry matter removal, purity of Astragaloside IV, purity of Astragaloside Ⅱ, purity of CG, purity of PG, purity of IFG, and dry matter removal, respectively. The values of regression coefficients in Inequalities (1) can be found in Table 6. If the CMAs of a batch of Astragali radix concentrates meet Inequalities (1), the batch of Astragali radix concentrates is considered to be acceptable for ethanol precipitation. For a batch of acceptable Astragali radix concentrates, feasible process parameters can be chosen after calculation or selected from Table S2. A batch of Astragali radix concentrates is considered to be unacceptable when Inequalities (1) cannot be satisfied.
In industry, the process parameters are usually fixed. If the process parameters are fixed as follows: the ECR is 1.5 g/g, the dry matter content of concentrates is 45%, the ethanol solution concentration is 92% (v/v), and the temperature is 15 oC, Inequalities (1) can be simplified to Inequalities (2).

If a batch of Astragali radix concentrates with CMAs meeting Inequalities (2), this batch of Astragali radix concentrates is considered to be high quality material for the current parameter fixing process. If not, it is considered a low-quality material and should not be released for ethanol precipitation directly.
Examples of material quality control and verification experiments
The CMAs of 3 batches of Astragali radix concentrates were measured and are shown in Table 8. According to Inequalities (2), Astragali radix concentrates of N12 were low quality Astragali radix concentrates, and it should not be released. The design space calculation results of N12 was show in Figure S3. The results show that when the materials were unqualified, no matter how to change the CPP, the standards of CQA can not be achieved with a high probability.
Table 8. CMAs of Astragali radix concentrates for validation
concentrates No.
|
Electrical conductivity (Z8, μS/cm)
|
CG contents
(Z3, μg/g dry matter)
|
Sucrose contents
(Z7, mg/g dry matter)
|
Quality grade
|
N11
|
1736
|
1292
|
617.6
|
High quality
|
N12
|
1568
|
1384
|
595.2
|
Low quality
|
N13
|
1631
|
1166
|
638.9
|
High quality
|
N11 and N13 were high quality Astragali radix concentrates. The verification experiment conditions and results are listed in Table 9 and Fig. 7(e) and 7(f). All the predicted values were close to the experimental values, indicating that the models had good predictive performance.
Table 9. Verification conditions and results(n=3)
CPPs and CQAs
|
|
V1
|
V2
|
V3
|
concentrates
|
|
N11
|
N13
|
N11
|
Is it in the design space?
|
|
Yes
|
Yes
|
No
|
DM (%)
|
|
45
|
45
|
45
|
ECR (g/g)
|
|
1.9
|
1.9
|
1.2
|
The ethanol solution concentration (%)
|
|
93
|
94
|
90
|
temperature (oC)
|
|
5
|
5
|
5
|
Dry matter removal (%)
|
Experimental value*
|
49.23±0.64
|
50.95±2.46
|
37.88±1.20
|
Predicted value
|
49.82
|
51.14
|
38.71
|
Purity of Astragaloside IV (μg/g)
|
Experimental value*
|
1007.19±60.07
|
899.73±73.14
|
1083.41±50.90
|
Predicted value
|
1119.77
|
865.77
|
1119.77
|
Purity of Astragaloside II (μg/g)
|
Experimental value*
|
948.76±45.37
|
984.53±28.71
|
893.08±20.73
|
Predicted value
|
911.93
|
867.57
|
821.78
|
Purity of CG (μg/g)
|
Experimental value*
|
2691.32±133.66
|
2733.53±97.48
|
2451.62±24.05
|
Predicted value
|
2471.65
|
2539.55
|
2350.18
|
Purity of PG (μg/g)
|
Experimental value*
|
1054.72±46.97
|
765.27±25.26
|
939.22±25.75
|
Predicted value
|
1044.77
|
930.65
|
1074.02
|
Purity of IFG(μg/g)
|
Experimental value*
|
887.45±82.71
|
692.58±23.22
|
869.80±19.20
|
Predicted value
|
783.24
|
625.17
|
765.00
|
Experimental value*: average value ±SD