| Literature DB >> 35239075 |
Xiuhe Liu1, Aonan Sun1, Qing Li1, Yamin Du1, Tao Zhao2.
Abstract
Monacolin K is one of the bioactive substances produced by Monascus ruber during fermentation. The multi-factors and their interactions on the effect of solid-state fermentation of Monascus for high yield of monacolin K were attractive to industrial production. A detailed study of 7 single-factor experiments and a series of experiments with Plackette-Burman and Box-Benhnken design, data fitting and modeling, and analyzing the visual 3D response surface plots for investigation of the key factors for Monacolin K production. The results showed that initial moisture (50 ~ 55%) and bran content (4.5 ~ 5.5%) as the key factors of transport for nutrients and oxygen during the solid-state fermentation (SSF) process of Monascus. Under the optimal conditions, a temperature shifting of the SSF with a higher Monacolin K yield of 14.53 ± 0.16 mg·g- 1 compared with the content of monacolin K in the commercially available functional red yeast rice of 8 mg g- 1.Entities:
Keywords: Monacolin K; Monascus; Solid state fermentation; Statistical parameter analysis
Year: 2022 PMID: 35239075 PMCID: PMC8894543 DOI: 10.1186/s13568-022-01368-z
Source DB: PubMed Journal: AMB Express ISSN: 2191-0855 Impact factor: 3.298
Fig. 1Influences of initial moisture (a) and pH (b) on the production of Monacolin K
Fig. 2Influences of different carbon sources (a) and glucose content (b), nitrogen sources (c) and peptone content (d) on the yield of Monacolin K
Fig. 3Influences of different bran content (a) and rice grain size (b) on Monacolin K
Fig. 4Influences of inoculation amount (a), media amount (b), fermentation temperature (c) and time (d) on the yield of Monacolin K
Plackett-Burman experiment analysis results of single-factor experiments
| Source | Sum of squares | Degree of freedom | Mean squares | F-value | Prob > F |
|---|---|---|---|---|---|
| Model | 67.66 | 7 | 9.67 | 15.75 | 0.0091** |
| X1 | 23.49 | 1 | 23.49 | 38.27 | 0.0035** |
| X2 | 7.76 | 1 | 7.76 | 12.64 | 0.0237* |
| X3 | 0.45 | 1 | 0.45 | 0.74 | 0.4390 |
| X4 | 19.33 | 1 | 19.33 | 31.49 | 0.0050** |
| X5 | 1.53 | 1 | 1.53 | 2.5 | 0.1891 |
| X6 | 13.00 | 1 | 13.00 | 21.18 | 0.0100** |
| X7 | 2.09 | 1 | 2.09 | 3.14 | 0.1381 |
Box-Benhnken experiment results for significant factors
| Source | Coefficient | Sum of squares | df | Mean squares | F-value | p-value |
|---|---|---|---|---|---|---|
| Model | 14.40 | 117.92 | 9 | 13.10 | 95.79 | < 0.0001** |
| A | 0.85 | 5.73 | 1 | 5.73 | 41.88 | 0.0003** |
| B | − 0.26 | 0.53 | 1 | 0.53 | 3.88 | 0.0896 |
| C | 1.50 | 17.97 | 1 | 17.97 | 131.38 | < 0.0001** |
| AB | 0.82 | 2.72 | 1 | 2.72 | 19.90 | 0.0029** |
| AC | 0.55 | 1.22 | 1 | 1.22 | 8.93 | 0.0203* |
| BC | 0.62 | 1.54 | 1 | 1.54 | 11.24 | 0.0122* |
| A2 | − 3.26 | 44.64 | 1 | 44.64 | 326.39 | < 0.0001** |
| B2 | − 0.88 | 3.25 | 1 | 3.25 | 23.77 | 0.0018** |
| C2 | − 2.80 | 33.04 | 1 | 33.04 | 241.55 | < 0.0001** |
| Residual | 0.96 | 7 | 0.14 | |||
| Lack of fit | 0.49 | 3 | 0.16 | 1.39 | 0.3673 | |
| Pure error | 0.47 | 4 | 0.12 | |||
| Core total | 118.88 | 16 |
Fig. 5Interaction between bran content and media amount at a fixed initial water of 50%: (a) Contour plot, (b) 3D Surface plot
Fig. 6Interaction between media amount and initial moisture at a fixed bran content of 5%: (a) Contour plot and (b) 3D Surface plot
Fig. 7Interaction between bran content and initial moisture at a fixed media amount of 60 g: (a) Contour plot and (b) 3D Surface plot
Comparison of the present method with recent studies
| Strain | Fermentation parameters | MK production | References |
|---|---|---|---|
|
| Nutrients: NH4Cl, MgSO4 7H2O | 6.24 | Kanpiengjai et al. ( |
|
| Inorganic salts of divalent metal cations | 9.57 | Lin et al. ( |
| Amount of added water | 0.75 | Oh et al. ( | |
IF-RPD 4046 | initial moisture and white rice | 1.33 | Saithong et al. ( |
| Chinese medicines | 3.6 | Peng et al. ( | |
|
| Using millet as substrate | 7.12 | Zhang et al. ( |
| Mutation and temperature variation | 8.44 | Huang et al. ( | |
| Multi-factors | 14.54 | Our method* |