The effective factors on phytase production by
The main storage form of phosphorus in many seeds is phytate (myoinositol hexakisphosphate). In cereals, legumes and Brassicae, phytate was collected in the seeds during maturation and accounts for 50–80% of total phosphorus [
Phytases (EC 3.1.3.8 and EC 3.1.3.26) belong to the family of histidine acid phosphatases, which catalyses the hydrolytic degradation of phytic acid and its salts (phytates) also, generally yielding inositol, inositol monophosphate, and inorganic phosphate [
Solid state fermentation (SSF) systems have generated much interest lately, because they offer some economical and practical advantages including higher product concentration, improved product recovery, very simple cultivation facilities, reduced wastewater output, lower capital investment, and lower plant operation cost [
The objective of this investigation was to identify the significant variables and further optimize their levels for phytase production by applying
A spore suspension of
For the phytase production, the wheat bran (WB) was used as a substrate. Five grams of the dried substrate were taken into a 250 mL Erlenmeyer flask. By adding tap water, the substrate moisture was adjusted to the required level. The substrate was sterilized at 121°C and 15 psi for 15 min, cooled, and inoculated with various amount of spore suspension of
Crude enzyme (phytase) was extracted by mixing the fermented substrate with a known amount of distilled water including 0.1% Tween-80 on a rotary shaker (180 rpm) for 1 h. The suspension was then centrifuged at 7000 rpm at 4°C for 20 min and the supernatant applied for enzyme assay [
Phytase activity was assayed by measuring the amount of inorganic phosphorus which was released from sodium phytate solution by using the method of B. F. Harland and J. Harland (1980). One unit of enzyme activity was described as the amount of phytase needed to release one micromole of inorganic phosphorus per minute under the assay conditions [
To identify the critical parameters required for increasing enzyme production Plackett-Burman, a two factorial design, was used by screening N variables in N+ 1 experiment [
Experimental variables at different levels used for the production of phytase by
Variables | Unit | Low level | High level |
---|---|---|---|
(−) | (+) | ||
Moisture (A) | % | 50 | 70 |
Temperature (B) | °C | 25 | 35 |
Particle size (C) | Mm | 0.2–0.6 | 1.0–1.4 |
Time (D) | Hour | 48 | 144 |
MgSO4 (E) | g/g dry substrate % | 0 | 0.3 |
KH2PO4 (F) | g/g dry substrate % | 0 | 0.01 |
(NH4)2SO4 (G) | g/g dry substrate % | 2 | 6 |
Glucose (H) | g/g dry substrate % | 5 | 15 |
Tween-80 (I) | g/g dry substrate % | 0 | 0.5 |
NaCl (J) | g/g dry substrate % | 0 | 0.3 |
Inoculum size (K) | mL | 0.5 | 2.5 |
Plackett-Burman experimental design for screening of significant process variables affecting phytase production.
Run order | Variables | Phytase activity (U/gds) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | G | H | I | J | K | Replication 1 | Replication 2 | |
1 | 1 | 1 | −1 | 1 | 1 | −1 | 1 | −1 | −1 | −1 | 1 | 15.0 | 13.3 |
2 | −1 | −1 | 1 | 1 | 1 | −1 | 1 | 1 | −1 | 1 | −1 | 13.6 | 11.5 |
3 | −1 | 1 | 1 | −1 | 1 | −1 | −1 | −1 | 1 | 1 | 1 | 0.2 | 0.0 |
4 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | 1.2 | 1.0 |
5 | 1 | −1 | 1 | 1 | −1 | 1 | −1 | −1 | −1 | 1 | 1 | 6.0 | 3.8 |
6 | 1 | 1 | −1 | 1 | −1 | −1 | −1 | 1 | 1 | 1 | −1 | 9.1 | 9.5 |
7 | 1 | −1 | −1 | −1 | 1 | 1 | 1 | −1 | 1 | 1 | −1 | 3.3 | 2.7 |
8 | 1 | −1 | 1 | −1 | −1 | −1 | 1 | 1 | 1 | −1 | 1 | 1.2 | 1.1 |
9 | −1 | −1 | −1 | 1 | 1 | 1 | −1 | 1 | 1 | −1 | 1 | 8.9 | 9.4 |
10 | −1 | 1 | 1 | 1 | −1 | 1 | 1 | −1 | 1 | −1 | −1 | 0.9 | 1.3 |
11 | 1 | 1 | 1 | −1 | 1 | 1 | −1 | 1 | −1 | −1 | −1 | 5.8 | 5.3 |
12 | −1 | 1 | −1 | −1 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 0.0 | 0.0 |
Central composite design (CCD) of response surface methodology (RSM) was applied to optimize the four most significant factors (glucose concentration, moisture, MgSO4 concentration, and time) identified by the Plackett-Burman design to increase phytase production. The statistical software “Design Expert 7.1” was used to produce and analyze the experimental design. The influence of each independent variable were studied at five different levels (−2, −1, 0, +1, and +2; Table
Experimental variables at different levels used for RSM approach.
Variables | Units | Symbol code | Levels | ||||
---|---|---|---|---|---|---|---|
−2 | −2 | 0 |
|
|
|||
Moisture | % (w/w) |
|
55 | 60 | 65 | 70 | 75 |
MgSO4 | g/g dry substrate % |
|
0.2 | 0.3 | 0.4 | 0.5 | 0.6 |
Glucose | g/g dry substrate % |
|
4 | 7.5 | 11 | 14.5 | 18 |
Time | Hours |
|
72 | 96 | 120 | 144 | 168 |
Exprimental design and the results of central composite design for phytase production.
Run |
|
|
|
|
Phytase activity (U/gds) | |
---|---|---|---|---|---|---|
Observed | Predicted | |||||
1 | −1 | −1 | −1 | −1 | 22.4 | 21.95 |
2 | −1 | −1 | −1 | 1 | 20.6 | 21.39 |
3 | −1 | −1 | 1 | −1 | 19.4 | 19.52 |
4 | −1 | −1 | 1 | 1 | 21.6 | 22.00 |
5 | −1 | 1 | −1 | −1 | 21.5 | 21.35 |
6 | −1 | 1 | −1 | 1 | 20.0 | 20.79 |
7 | −1 | 1 | 1 | −1 | 18.7 | 18.92 |
8 | −1 | 1 | 1 | 1 | 21.2 | 21.40 |
9 | 1 | −1 | −1 | −1 | 18.4 | 18.52 |
10 | 1 | −1 | −1 | 1 | 16.9 | 17.20 |
11 | 1 | −1 | 1 | −1 | 17.0 | 16.74 |
12 | 1 | −1 | 1 | 1 | 18.0 | 18.47 |
13 | 1 | 1 | −1 | −1 | 18.2 | 18.32 |
14 | 1 | 1 | −1 | 1 | 16.8 | 17.00 |
15 | 1 | 1 | 1 | −1 | 17.0 | 16.54 |
16 | 1 | 1 | 1 | 1 | 17.3 | 18.27 |
17 | −2 | 0 | 0 | 0 | 20.3 | 19.76 |
18 | 2 | 0 | 0 | 0 | 13.5 | 13.19 |
19 | 0 | −2 | 0 | 0 | 23.3 | 22.98 |
20 | 0 | 2 | 0 | 0 | 22.7 | 22.18 |
21 | 0 | 0 | −2 | 0 | 22.0 | 21.56 |
22 | 0 | 0 | 2 | 0 | 20.8 | 20.39 |
23 | 0 | 0 | 0 | −2 | 15.7 | 16.49 |
24 | 0 | 0 | 0 | 2 | 19.3 | 17.66 |
25 | 0 | 0 | 0 | 0 | 24.4 | 23.87 |
26 | 0 | 0 | 0 | 0 | 24.0 | 23.87 |
27 | 0 | 0 | 0 | 0 | 23.2 | 23.87 |
28 | 0 | 0 | 0 | 0 | 23.9 | 23.87 |
The statistical model was validated considering phytase production under the optimum conditions predicted by the model in shake-flasks level and phytase activity was determined as expressed above.
Table
Results of regression analysis for Plackett-Burman design.
Term | Coefficient |
|
|
---|---|---|---|
Intercept | 5.17 | 33.89 | <0.0001 |
Moisture | 1.17 | 7.67 | <0.0001 |
Temperature | −0.14 | −0.90 | 0.3852 |
Particle size | −0.95 | −6.20 | <0.0001 |
Fermentation time | 3.35 | 21.98 | <0.0001 |
MgSO4 | 2.25 | 14.72 | <0.0001 |
KH2PO4 | −1.22 | −8.0 | <0.0001 |
(NH4)2SO4 | 0.15 | 1.01 | 0.3323 |
Glucose | 1.11 | 7.29 | <0.0001 |
Tween-80 | −1.20 | −7.89 | <0.0001 |
NaCl | −0.20 | −1.28 | 0.2236 |
Inoculum size | −0.26 | −1.72 | 0.1110 |
The four significant factors influencing phytase production were further optimized using RSM design. Considering the Plackett-Burman results, particle size of the substrate was kept constant at its lower level, that is, 0.2–0.6 mm. The mean predicted and observed responses are illustrated in Table
Analysis of variance (ANOVA) for the quadratic model.
Source | Sum of squares | df |
|
Prob > |
---|---|---|---|---|
Model | 211.1123 | 10 | 127.256 | <0.0001 |
|
64.68167 | 1 | 389.8933 | <0.0001 |
|
0.96 | 1 | 5.786764 | 0.0286 |
|
2.041667 | 1 | 12.30692 | 0.0029 |
|
0.023048 | 1 | 0.138928 | 0.7142 |
|
0.5625 | 1 | 3.390682 | 0.0842 |
|
9.3025 | 1 | 56.07434 | <0.0001 |
|
73.18313 | 1 | 441.139 | <0.0001 |
|
1.381663 | 1 | 8.328495 | 0.0108 |
|
9.678248 | 1 | 58.33931 | <0.0001 |
|
65.78006 | 1 | 396.5143 | <0.0001 |
Residual | 2.654333 | 16 | ||
Lack of fit | 1.906833 | 13 | 0.58868 | 0.7837 |
Pure error | 0.7475 | 3 | ||
| ||||
Total | 213.7667 | 26 |
The ANOVA and model coefficients are illustrated in Table
To identify the levels of each variable for maximum phytase production, three-dimensional response surface plots were constructed by plotting the response (phytase production) on the
Contour plot to study the effect of moisture (%) and time (h) on the phytase production (U/gds) at MgSO4 and glucose coded level of zero.
Contour plot to study the effect of glucose (%) and time (h) on the phytase production (U/gds) at MgSO4 (%) and moisture (%) coded level of zero.
Contour plot to study the effect of moisture (%) and MgSO4 (%) on the phytase production (U/gds) at time (h) and glucose (%) coded level of zero.
Figure
The response surface curve for the interaction of glucose and fermentation time is shown in Figure
According to Figures
In order to obtain the maximum activity of phytase, the optimization of the model was performed applying RSM autoanalysis software by setting the maximum phytase activity value
In the present work, we demonstrated the optimization of