| Literature DB >> 35031033 |
Yunhong Wang1, Weihan Qin1, Yujie Yang2, Hui Bai2, Jirui Wang1, Xiaomei Zhang1, Yanlei Guo1, Lei Hua1, Yong Yang3.
Abstract
BACKGROUND: The present study intends to optimize the processing technology for the wine-processing of Rhizoma Coptidis, using alkaloids as indicators.Entities:
Keywords: Alkaloids; Box–Behnken; Processing technology; Wine coptis; Yunlian
Mesh:
Substances:
Year: 2022 PMID: 35031033 PMCID: PMC8760793 DOI: 10.1186/s12896-021-00731-5
Source DB: PubMed Journal: BMC Biotechnol ISSN: 1472-6750 Impact factor: 2.563
Fig. 1High-performance liquid chromatogram. The numbered peaks represent: 2. Pharmacorhizine hydrochloride, 3. Tetrandrine hydrochloride, 4. Epiberberine hydrochloride, 5. Coptidine hydrochloride, 6. Palmatine hydrochloride, and 7. Berberine hydrochloride
Fig. 3Chromatogram of reference substances: 1. Epaperberine, 2. Coptidine, 3. Palmatine, and 4. Berberine
Fig. 2Contour and response surface
Alcohol absorption capacity of Rhizoma coptidis
| Weighing sample (g) | Add fluid volume (ml) | Residual fluid volume (ml) |
|---|---|---|
| 100.00 | 500 | 400 |
| 100.00 | 405 | |
| 100.00 | 405 |
Alkaloid contents in different types of yellow rice wine and prepared alcoholic products (% by dry weight)
| Variety and alcohol of yellow rice wine | Epiberberine (%) | Coptisine (%) | Palmatine (%) | Berberine (%) |
|---|---|---|---|---|
| Dry type,16% | 1.33 | 2.26 | 2.23 | 7.95 |
| Semi-dry,15% | 1.29 | 2.22 | 2.21 | 7.98 |
| Semi-Swee,10% | 1.36 | 2.28 | 2.22 | 7.89 |
| Sweet,11.5% | 1.34 | 2.29 | 2.23 | 8.01 |
| RSD% | 2.22 | 1.37 | 0.44 | 0.65 |
Ranges of design factors of Box-behnken
| Level | Moisture time/min | Processing temperature/°C | Processing time/min |
|---|---|---|---|
| Low | 0 | 80 | 1.5 |
| High | 3 | 150 | 12 |
Box-behnken design test and results
| No | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 150 | 6.75 | 0.18 | 0.39 | 0.58 | 1.41 | 2.28 | 2.13 | 7.77 | |
| 2 | 0 | 80 | 6.75 | 0.16 | 0.40 | 0.61 | 1.38 | 2.33 | 2.17 | 7.99 | |
| 3 | 1.5 | 80 | 1.5 | 0.15 | 0.39 | 0.52 | 1.26 | 2.14 | 1.95 | 7.03 | |
| 4 | 3 | 115 | 12 | 0.16 | 0.40 | 0.64 | 1.41 | 2.29 | 2.25 | 7.93 | |
| 5 | 1.5 | 80 | 12 | 0.17 | 0.43 | 0.65 | 1.35 | 2.36 | 2.23 | 8.01 | |
| 6 | 1.5 | 115 | 6.75 | 0.16 | 0.41 | 0.65 | 1.35 | 2.26 | 2.21 | 7.94 | |
| 7 | 1.5 | 115 | 6.75 | 0.20 | 0.42 | 0.66 | 1.49 | 2.31 | 2.22 | 7.83 | |
| 8 | 0 | 150 | 6.75 | 0.18 | 0.42 | 0.64 | 1.46 | 2.35 | 2.29 | 8.07 | |
| 9 | 3 | 115 | 1.5 | 0.17 | 0.41 | 0.64 | 1.38 | 2.19 | 2.23 | 7.61 | |
| 10 | 1.5 | 115 | 6.75 | 0.18 | 0.41 | 0.67 | 1.46 | 2.29 | 2.27 | 8.04 | |
| 11 | 1.5 | 150 | 12 | 0.17 | 0.40 | 0.64 | 1.33 | 2.14 | 2.20 | 7.65 | |
| 12 | 1.5 | 150 | 1.5 | 0.18 | 0.42 | 0.62 | 1.39 | 2.21 | 2.19 | 7.52 | |
| 13 | 3 | 80 | 6.75 | 0.19 | 0.42 | 0.64 | 1.43 | 2.36 | 2.21 | 7.98 | |
| 14 | 1.5 | 115 | 6.75 | 0.18 | 0.41 | 0.67 | 1.46 | 2.35 | 2.28 | 8.15 | |
| 15 | 0 | 115 | 12 | 0.18 | 0.42 | 0.68 | 1.47 | 2.35 | 2.38 | 8.16 | |
| 16 | 1.5 | 115 | 6.75 | 0.18 | 0.42 | 0.69 | 1.43 | 2.32 | 2.24 | 7.93 | |
| 17 | 0 | 115 | 1.5 | 0.19 | 0.42 | 0.67 | 1.45 | 2.51 | 2.33 | 8.42 | |
| Predictive value | 0.178 | 0.420 | 0.683 | 1.500 | 2.440 | 2.356 | 8.376 | 15.964 |
Y1% to Y7% are equally important
Variance analysis
| Source | Sum of Squares | df | F-value | ||
|---|---|---|---|---|---|
| Model | 4.36 | 9 | 0.4898 | 0.0350* | Significant |
| A-moisture time | 0.7323 | 1 | 6.41 | 0.0392 | |
| B- processing temperature | 0.0089 | 1 | 0.0775 | 0.7888 | |
| C- processing time | 0.4358 | 1 | 3.81 | 0.0918 | |
| AB | 0.1800 | 1 | 1.57 | 0.2498 | |
| AC | 0.1585 | 1 | 1.39 | 0.2774 | |
| BC | 0.7592 | 1 | 6.64 | 0.0366 | |
| A2 | 0.6634 | 1 | 5.80 | 0.0468 | |
| B2 | 1.15 | 1 | 10.10 | 0.0155 | |
| C2 | 0.3388 | 1 | 2.96 | 0.1288 | |
| Residual | 0.8001 | 7 | |||
| Lack of Fit | 0.6581 | 3 | 6.18 | 0.0555 | Not significant |
| Pure error | 0.1420 | 4 | |||
| Cor total | 5.16 | 16 |
* indicates the significant value
Verify test results
| 1 | 15.964 | 15.41 |
| 2 | 15.38 | |
| 3 | 15.31 |