الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 180 |  |  |
| | | from | 180 | | |
Abstract
Production of microbial β-carotene has received great attention because of their natural properties and broad application in cosmetics, pharmaceutical, food and feed industries due to its colorant and antioxidant properties
The objectives of this study were to make use of agro-industrial by-products, which cause water and air pollution, as substrate for the microbial production of β-carotene. After selecting the suitable by-products, the production process was optimized through modification of the physical and nutritional parameters influencing β-carotene production while employing statistical approach of Response Surface Methodology followed by scale-up of the fermentation process under the optimized conditions. The produced β-carotene was purified and its chemical structure was determined.
To achieve these objectives, the following steps were performed:
• Chemical analysis for agro-industrial by-products, which were rice bran, sugarcane molasses and sugarcane bagasse.
• Evaluation of β-carotene production by three strains, Rhodotorula glutinis ATCC 4054, Sphingomonas paucimobilis ATCC 10829 and Serratia marcescens ATCC 27117 from the agro-industrial by-products.
• Screening of sources and concentrations of carbon and nitrogen affecting β-carotene production by one-variable at-a-time approach.
• Optimization of β-Carotene Production by the best two strains as follows:
a- Statistical screening of physical and nutritional factors for β-carotene production using Plackett-Burman Design.
b- Central Composite Design of Response Surface Methodology (RSM) for optimization of nutritional and physical parameters for β-carotene production.
• Purification and structure determination of β-carotene produced by the selected strain after optimization in large scale fermenter.
Results can be summarized as follows:
1. Chemical analysis for agro-industrial-by products.
The highest fraction of cellulose (39.2 % w/w), hemicellulose (56.7 % w/w) was recorded in sugarcane bagasse and rice bran. The highest moisture content was recorded in molasses (28 % w/w), followed by sugarcane bagasse (12 % w/w), while the lowest was recorded in rice bran (10.7 % w/w). Rice bran contained the highest nitrogen content (2.5 % w/w) while molasses recorded the highest carbon content (20 % w/w) with protein (4.562% w/w). Sugarcane bagasse contained the lowest nitrogen and carbon content (0.217% w/w, 14 % w/w), respectively, with protein (1.356 % w/w).
2. Evaluation of β-carotene production by the selected strains from agro-industrial by-products.
- Rice bran was the best substrate for R. glutinis, producing 1.23 mg/kg rice after 4 days of incubation at 30oC.
- Molasses was the best substrate for Sph. paucimobilis, producing 0.78 mg/L after 3 days of incubation at 30oC.
- Molasses was the best substrate for S. marcescens, while the β-carotene production 1.1 mg/L on day 2 and yeast biomass 20 g/L.
3. Optimization of β-Carotene Production by R. glutinis.
3.1. Screening of carbon and nitrogen sources affecting β-Carotene production by R. glutinis using one-variable at-a-time approach.
- Maximum production of β-carotene by R. glutinis was 2.3 mg/kg, achieved by 18 g/L sucrose. Maximum production from glucose was 2 mg/kg rice bran, achieved at 10 g/L, and finally starch, at 1.36 g/L, produced β-carotene at 1mg/kg.
- Yeast extract at 5g/L and NH4Cl at 1g/L increased β-carotene production to 2.2 mg/kg, 1.8 mg/kg, respectively. These results showed that our isolate can accumulate β-carotene in the presence of both organic nitrogen source, that is yeast extract, and in-organic nitrogen source, that is NH4Cl.
3.2. Statistical screening of physical and nutritional factors for β-carotene production by R. glutinis using Plackett-Burman Design.
- The PB results indicated that there was a variation of pigment production in the twelve trials in the range from 0.44 to 3.40 mg/kg rice bran. Among the 8 factors, sucrose, yeast extract, NH4Cl, NaCl, MgSO4, KH2PO4, pH, inoculum size showed a positive sign of the effect on β-carotene production, all other factors shown a negative sign of the effect.
- The coefficient of correlation, R2, was found to be 0.9963, showing good fitness of the model.
- Factors with P-value lower than 0.05 were considered to have significant effects on the production of β-carotene, and were therefore selected for further optimization studies using CCD.
- Sucrose, with a P-value of (0.0024), was considered as the most significant factor, followed by pH (0.025), KH2PO4 (0.0366), and NaCl (0.0399), respectively.
- The Model F-value of 60.26 implies the model is significant. There is only a 1.64% chance that an F-value this large could occur due to noise.
3.3 Central composite design of Response Surface Methodology (RSM) for optimization of nutritional and physical parameters for β-carotene production by R. glutinis grown on rice bran.
- The quadratic effect for sucrose concentration (P value < 0.0001), NaCl concentration (0.0004), KH2PO4 concentration (0.0446) and pH (P value < 0.0001),) should to be significant on the model.
- The linear effect for KH2PO4 and pH were still considered as an important factor in this model (P<0.05).
- The polynomial model for β-carotene production (Y) was regressed by only considering the significant terms (P<0.05) as shown in the following equation:
Y = 3.14 + 0.22C + 0.24D -0.52A2 - 0.37B2 -0.18C2- 0.65D2
- The regression equation obtained from analysis of variance (ANOVA) with the R2 value (multiple correlation coefficients) of 0.8855 revealed that the model should to be fitness.
- The ”Lack of Fit F-value” of 1.81 implies the Lack of Fit is not significant relative to the pure error. There is a 26.65% chance that a ”Lack of Fit F-value” this large could occur due to noise. Non-significant lack of fit is good.
- The optimum conditions were Sucrose (18.6 g/L), NaCL (0.66g/L), KH2PO4 (1.01 g/L), and pH at 5.4 with a predicted β-carotene production of 3.24 mg/kg rice bran.
4. Optimization of β-Carotene production by S. marcescens
4.1. Screening of carbon and nitrogen sources affecting β-carotene production by S. marcescens using one-variable at-a-time approach
The sucrose and lactose as carbon sources produced maximum β-carotene at 2.5g/L. The best of the carbon sources was sucrose, producing 1.9 mg/L of β-carotene, while lactose and starch produced 1.6 and 1.2 mg/kg, respectively. On the other hand, as nitrogen source, beef extract at 7 g/l, peptone at 8g/L and KNO3 at 6g/L increased β-carotene production to 1.9, 1.8 and 1.2 mg/L, respectively
4.2. Statistical screening of physical and nutritional factors for β-carotene production by S. marcescens ATCC 27117 on molasses using Plackett-Burman Design.
- The PB results indicated that there was a variation of pigment production in the twelve trials in the range from 0.86 to 2.45 mg /kg β-carotene production. Among the 6 factors, sucrose, lactose, peptone, beef extract, pH, inoculum size showed a positive sign of the effect on β-carotene production, all other factors shown a negative sign of the effect.
-The coefficient of correlation, R2, was found to be 0.9829, showing good fitness of the model.
- Factors with P value lower than 0.05 were considered to have significant effects on the production of β-carotene, and were therefore selected for further optimization studies using CCD. Value of pH, with a P value of (<0.0001), was considered as the most significant factor, followed by sucrose (0.0016) and peptone (0.0406).
- The Model F-value of 47.93 implies the model is significant. There is only a 0.03% chance that an F-value this large could occur due to noise.
4.3. Central composite design of Response Surface Methodology (RSM) for optimization of nutritional and physical parameters by production.
- The quadratic effect of pH value (P value =0.0005), peptone (P value = 0.0062) and sucrose concentration (P value = 0.0008) should to be significant on the model.
- The linear effect for peptone and pH were still considered as an important factor in this model (P<0.05) than sucrose concentration (P value = 0.2012).
- The polynomial model for β-carotene production (Y) was regressed by only considering the significant terms (P<0.05) as shown in the following equation:
Y = 2.29+ 0.36 B+ 0.42C - 0.39 A2 - 0.28 B2 - 0.42 C2
-The regression equation obtained from analysis of variance (ANOVA) with the R2 value (multiple correlation coefficients) of 0.9210 revealed that the model should to be fitness.
-The ”Lack of Fit F-value” of 3.88 implies the Lack of Fit is not significant relative to the pure error. There is a 10.66% chance that a ”Lack of Fit F-value” this large could occur due to noise. Non-significant lack of fit is good.
-The optimum conditions were Sucrose (2.5 g/L), peptone (7.8 g/L) and pH at 6.7 with a maximum predicted β-carotene production of 2.51 mg/L molasses and actual 2.24 mg/L.
5. Purification and structure determination of β-carotene from Rhodotorula glutinis after optimization in large scale fermenter.
- The TLC results showed the presence of β-carotene with Rf value equal to 0.95
- The data of peak areas plotted against the corresponding concentrations and the amounts of pigment was calculated using the respective calibration curves.
The concentration of β-carotene of the sample can be calculated from the following equation:
y=539.47x+813.18, R² = 0.9336
where y = area of the sample, so we can calculate X by: 18767=539.47X+813. 18, X = 33.28 μg/ml of sample
- Quantitative analysis by HPLC showed that natural β-carotene had purity 68.2084 % compared with standard. Result reveals that the concentration of β-carotene 89.06 mg/100g yeast cell.
- GC/MS showed that the sample contain pigment as beta-carotene and other compounds and the retention time of β-carotene 24.93 minute with peak area 0.03%. from WILEY9 software’s which used as MS library, the molecular weight of beta-carotene 536 and the molecular formula C40H56.
- Several absorbance peaks ranging from 4000 cm-1 to 500 cm-1 showed by FTIR. Those peaks from its analysis through IR chart confirmed the presence of the following functional groups in a compound.
-Proton magnetic resonance (1H NMR) may, in the hands of experts, serve as the single spectroscopic method for complete structural determination of a pure carotenoid through the number of hydrogen atom.