Biotransformation of Agricultural Wastes into Lovastatin and Optimization of a Fermentation Process Using Response Surface Methodology (RSM)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Induction of Mutation
2.2. Substrate Pretreatment
2.3. Preparation of Inoculum and Solid-State Fermentation
2.4. Lovastatin Extraction
2.5. Lovastatin Analysis
2.6. Design of Experiment (DOE)
2.7. NMR Spectroscopy
2.8. X-ray Diffraction (XRD)
3. Results and Discussion
3.1. The Outcome of the Design of the Experiment (DOE) for Lovastatin Production
3.2. Statistical Analysis of Lovastatin Production
3.3. In Terms of Actual Factors, the Final Equation of the Experiment
3.4. Analysis of Parameters on Production of Lovastatin by Response Surface Methodology
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Run | A: pH | B: Temp. (°C) | C: Inoculum Size (mL) | D: Inoculum Age (h) | E: Fermentation Time (h) | Lovastatin mg/L | |
---|---|---|---|---|---|---|---|
Actual | Predicted | ||||||
1 | 10 | 25 | 2 | 12 | 96 | 58.65 | 34.08 |
2 | 3 | 25 | 2 | 72 | 96 | 45.67 | 50.98 |
3 | 10 | 45 | 5 | 72 | 96 | 19.76 | 12.40 |
4 | 3 | 45 | 5 | 72 | 96 | 41.76 | 40.06 |
5 | 5.5 | 35 | 4 | 42 | 36 | 122.65 | 103.12 |
6 | 5.5 | 35 | 4 | 36 | 48 | 156.43 | 88.00 |
7 | 3 | 25 | 3 | 12 | 60 | 74.23 | 62.06 |
8 | 6.5 | 35 | 4 | 42 | 60 | 86.44 | 84.40 |
9 | 3 | 25 | 2.5 | 72 | 24 | 34.62 | 38.10 |
10 | 10 | 25 | 2.5 | 72 | 96 | 15.43 | 15.98 |
11 | 10 | 25 | 3 | 12 | 24 | 17.89 | 18.62 |
12 | 6.5 | 35 | 3 | 42 | 60 | 92.34 | 85.42 |
13 | 3 | 25 | 2 | 12 | 96 | 45.62 | 45.15 |
14 | 3 | 25 | 2 | 72 | 24 | 52.65 | 40.76 |
15 | 6.5 | 35 | 4 | 27 | 60 | 81.72 | 92.71 |
16 | 6.5 | 35 | 4 | 42 | 60 | 86.98 | 84.40 |
17 | 10 | 45 | 5 | 12 | 24 | 25.12 | 24.95 |
18 | 10 | 45 | 5 | 12 | 24 | 22.27 | 24.95 |
19 | 6.5 | 35 | 4 | 42 | 60 | 47.23 | 84.40 |
20 | 3 | 25 | 2 | 72 | 96 | 54.28 | 50.98 |
21 | 8.25 | 35 | 3 | 42 | 60 | 36.87 | 62.70 |
22 | 6.5 | 35 | 3 | 42 | 78 | 92.65 | 97.23 |
23 | 10 | 45 | 5 | 72 | 96 | 16.87 | 12.40 |
24 | 6.5 | 35 | 4 | 42 | 60 | 91.45 | 84.40 |
25 | 10 | 25 | 2 | 72 | 24 | 24.23 | 19.63 |
26 | 10 | 25 | 2 | 12 | 24 | 21.31 | 19.55 |
27 | 10 | 25 | 2 | 72 | 24 | 19.65 | 19.63 |
28 | 6.5 | 35 | 3.5 | 42 | 60 | 89.23 | 76.81 |
29 | 3 | 45 | 5 | 72 | 24 | 35.14 | 44.93 |
30 | 3 | 45 | 5 | 12 | 96 | 33.24 | 38.22 |
31 | 10 | 45 | 5 | 72 | 24 | 27.25 | 21.03 |
32 | 6.5 | 30 | 3.5 | 42 | 60 | 83.26 | 92.21 |
33 | 3 | 25 | 2.5 | 12 | 24 | 28.25 | 52.99 |
34 | 6.5 | 35 | 3 | 42 | 60 | 87.21 | 85.42 |
35 | 10 | 25 | 2 | 12 | 96 | 21.98 | 34.08 |
36 | 10 | 45 | 5 | 12 | 96 | 19.42 | 24.39 |
37 | 3 | 25 | 2 | 12 | 24 | 25.64 | 26.86 |
38 | 10 | 25 | 2 | 72 | 96 | 19.87 | 26.08 |
39 | 10 | 45 | 5 | 72 | 24 | 15.32 | 21.03 |
40 | 10 | 45 | 5 | 12 | 96 | 26.43 | 24.39 |
41 | 3 | 45 | 5 | 72 | 24 | 36.32 | 44.93 |
42 | 3 | 45 | 5 | 12 | 96 | 34.24 | 38.22 |
43 | 6.5 | 35 | 4 | 42 | 60 | 45.24 | 84.40 |
44 | 3 | 45 | 4.5 | 12 | 24 | 36.31 | 25.95 |
45 | 4.75 | 35 | 3.5 | 42 | 60 | 51.23 | 54.16 |
46 | 6.5 | 40 | 4 | 42 | 60 | 84.34 | 75.43 |
47 | 6.5 | 35 | 3 | 42 | 42 | 96.54 | 108.44 |
48 | 3 | 45 | 5 | 72 | 96 | 44.23 | 40.06 |
49 | 6.5 | 35 | 3 | 42 | 60 | 97.24 | 85.42 |
50 | 3 | 45 | 5 | 12 | 24 | 34.78 | 35.02 |
Source | Std. Dev. | R2 | Adjusted R2 | Predicted R2 | PRESS |
---|---|---|---|---|---|
Linear | 32.96 | 0.0775 | −0.0274 | −0.1209 | 58,070.93 |
2FI | 22.05 | 0.6810 | 0.5403 | −0.0546 | 54,635.52 |
Quadratic | 20.16 | 0.7724 | 0.6154 | 0.0652 | 48,429.31 |
Cubic | 13.35 | 0.9415 | 0.8314 | * |
Source | Sum of Squares | df | Mean Square | F-Value | p-Value |
---|---|---|---|---|---|
Model | 40,014.02 | 20 | 2000.70 | 4.92 | <0.0001 |
A-pH | 2075.87 | 1 | 2075.87 | 5.11 | 0.0315 |
B-Temp. | 0.0385 | 1 | 0.0385 | 0.0001 | 0.9923 |
C-Inoculum size | 3.85 | 1 | 3.85 | 0.0095 | 0.9232 |
D-Inoculum age | 6.09 | 1 | 6.09 | 0.0150 | 0.9034 |
E-Fermentation time | 160.32 | 1 | 160.32 | 0.3943 | 0.5350 |
AB | 1615.27 | 1 | 1615.27 | 3.97 | 0.0557 |
AC | 1518.02 | 1 | 1518.02 | 3.73 | 0.0632 |
AD | 313.81 | 1 | 313.81 | 0.7718 | 0.3869 |
AE | 21.98 | 1 | 21.98 | 0.0540 | 0.8178 |
BC | 352.49 | 1 | 352.49 | 0.8669 | 0.3595 |
BD | 499.79 | 1 | 499.79 | 1.23 | 0.2767 |
BE | 497.40 | 1 | 497.40 | 1.22 | 0.2778 |
CD | 495.23 | 1 | 495.23 | 1.22 | 0.2788 |
CE | 398.65 | 1 | 398.65 | 0.9804 | 0.3303 |
DE | 104.51 | 1 | 104.51 | 0.2570 | 0.6160 |
A2 | 1724.85 | 1 | 1724.85 | 4.24 | 0.0485 |
B2 | 235.83 | 1 | 235.83 | 0.5800 | 0.4525 |
C2 | 198.20 | 1 | 198.20 | 0.4875 | 0.4906 |
D2 | 2.90 | 1 | 2.90 | 0.0071 | 0.9333 |
E2 | 1335.99 | 1 | 1335.99 | 3.29 | 0.0802 |
Residual | 11,791.56 | 29 | 406.61 | ||
Lack of Fit | 8773.69 | 13 | 674.90 | 3.58 | 0.0090 |
Pure Error | 3017.88 | 16 | 188.62 | ||
Cor Total | 51,805.58 | 49 |
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Javed, S.; Azeem, M.; Mahmood, S.; Al-Anazi, K.M.; Farah, M.A.; Ali, S.; Ali, B. Biotransformation of Agricultural Wastes into Lovastatin and Optimization of a Fermentation Process Using Response Surface Methodology (RSM). Agronomy 2022, 12, 2848. https://doi.org/10.3390/agronomy12112848
Javed S, Azeem M, Mahmood S, Al-Anazi KM, Farah MA, Ali S, Ali B. Biotransformation of Agricultural Wastes into Lovastatin and Optimization of a Fermentation Process Using Response Surface Methodology (RSM). Agronomy. 2022; 12(11):2848. https://doi.org/10.3390/agronomy12112848
Chicago/Turabian StyleJaved, Sadia, Muhammad Azeem, Saqib Mahmood, Khalid Mashay Al-Anazi, Mohammad Abul Farah, Sajad Ali, and Baber Ali. 2022. "Biotransformation of Agricultural Wastes into Lovastatin and Optimization of a Fermentation Process Using Response Surface Methodology (RSM)" Agronomy 12, no. 11: 2848. https://doi.org/10.3390/agronomy12112848