| Literature DB >> 26312073 |
Wang Yanhua1, Wu Fuhua1, Guo Zhaohan1, Peng Mingxing1, Zhang Yanan1, Pang Zhen Ling1, Du Minhua1, Zhang Caiying1, Liang Zian1.
Abstract
This study was aimed to optimize the extraction process for Salvia miltiorrhiza Bunge polysaccharide using response surface methodology The results showed that four operating parameters including microwave power, microwave time and the particle size had notable effects on the polysaccharide extraction of Salvia miltiorrhiza Bunge. The effects could be ranked in decreasing order of importance as follows:. Microwave power > microwave time > the comminution degree. The optimal extraction parameters were determined as 573.83W of Microwave power and 8.4min of microwave time and 67.51mesh of the comminution degree, resulting in the yield of Salvia miltiorrhiza Bunge polysaccharide of 101.161mg / g. The established regression model describing polysaccharide extraction from as a function of the three extraction parameters was highly significant (R 2 = 0.9953). The predicted and experimental results were found to be in good agreement. Thus, the model can be applicable for the prediction of polysaccharide extraction from Salvia miltiorrhiza Bunge.Entities:
Keywords: CAD; Freehand sketching; ceramic design; ceramic products; three- dimensional
Year: 2014 PMID: 26312073 PMCID: PMC4541331 DOI: 10.2174/1874120701408010153
Source DB: PubMed Journal: Open Biomed Eng J ISSN: 1874-1207
Fig. (2)Effect of the comminution degree on yield of Salvia miltiorrhiza Bunge polysaccharide.
Box-Behnken experimental design and results.
| No. | S, granule size/mesh | W microwave power/ W | T microwave power/min | Y polysaccharide yield/(mg/g) |
|---|---|---|---|---|
| 1 | 40 | 525 | 10 | 55.6 |
| 2 | 60 | 525 | 8 | 97.8269 |
| 3 | 60 | 700 | 10 | 78.9038 |
| 4 | 80 | 700 | 8 | 77.6538 |
| 5 | 60 | 525 | 8 | 97.9923 |
| 6 | 60 | 525 | 8 | 97.9231 |
| 7 | 40 | 525 | 6 | 45.9423 |
| 8 | 60 | 525 | 8 | 99.75 |
| 9 | 40 | 700 | 8 | 69.3077 |
| 10 | 80 | 525 | 10 | 75.5192 |
| 11 | 80 | 350 | 8 | 39.462 |
| 12 | 60 | 700 | 6 | 39.6346 |
| 13 | 40 | 350 | 8 | 37.8654 |
| 14 | 60 | 525 | 8 | 95.6923 |
| 15 | 60 | 350 | 10 | 38.3269 |
| 16 | 80 | 525 | 6 | 38.25 |
| 17 | 60 | 350 | 6 | 29.1923 |
Analysis of variance for the established regression model.
| Source | Sum of squares | dfa | Mean of square | F value | P value | Significance |
|---|---|---|---|---|---|---|
| Model | 11016.67 | 9 | 1224.07 | 163.12 | < 0.0001 | ** |
| S | 61.44 | 1 | 61.44 | 8.19 | 0.0243 | * |
| W | 1819.65 | 1 | 1819.65 | 242.48 | < 0.0001 | ** |
| T | 1135.99 | 1 | 1135.99 | 151.38 | < 0.0001 | ** |
| SW | 11.39 | 1 | 11.39 | 1.52 | 0.2577 | |
| ST | 190.6 | 1 | 190.6 | 25.4 | 0.0015 | ** |
| WT | 227.02 | 1 | 227.02 | 30.25 | 0.0009 | ** |
| S2 | 1249.35 | 1 | 1249.35 | 166.49 | < 0.0001 | ** |
| W2 | 2535.44 | 1 | 2535.44 | 337.86 | < 0.0001 | ** |
| T2 | 3020.43 | 1 | 3020.43 | 402.49 | < 0.0001 | ** |
| Residual | 52.53 | 7 | 7.5 | |||
| Lack of fit | 44.24 | 3 | 14.75 | 7.11 | 0.0442 | |
| Pure error | 8.29 | 4 | 2.07 | |||
| Cor total | 11069.2 | 16 |