Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> Pseudomonas aeruginosa AT10 produced a mixture of surface-active rhamnolipids when cultivated on mineral medium with waste free fatty acids as carbon source. The development of the production process to an industrial scale included the design of the culture medium. A 2 4 full factorial, central composite rotational design and response surface modelling method (RSM) was used to enhance rhamnolipid production by Pseudomonas aeruginosa AT10. The components that are critical for the process medium were the carbon source, the nitrogen source (NaNO 3 ), the phosphate content (K 2 HPO 4 /KH 2 PO 4 2:1) and the iron content (FeSO 4 7H 2 O). Two responses were measured, biomass and rhamnolipid production. The maximum biomass obtained was 12.06 g dm -3 DCW, when the medium contained 50 g dm -3 carbon source, 9 g dm -3 NaNO 3 , 7 g dm 3 phosphate and 13.7 mg dm -3 FeSO 4 7H 2 O. The maximum concentration of rhamnolipid, 18.7 g dm 3 , was attained in medium that contained 50 g dm -3 carbon source, 4.6 g dm 3 NaNO 3 , 1 g dm 3 phosphate and 7.4 mg dm 3 FeSO 4 7H 2 O. <s> BIB001 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> Optimization of the medium for biosurfactants production by probiotic bacteria (Lactococcus lactis 53 and Streptococcus thermophilus A) was carried out using response surface methodology. Both biosurfactants were proved to be growth-associated, thus the desired response selected for the optimization was the biomass concentration. The selected factors based on MRS medium for L. lactis 53 growth were peptone, meat extract, yeast extract, lactose, ammonium citrate and KH2PO4. For S. thermophilus A based on the M17 medium, the selected factors were peptone, meat extract, yeast extract, lactose, soya peptone and sodium glycerophosphate. The optimum MRS composition was found to be 38.6 g/l peptone, 43.0 g/l lactose, 10 g/l. meat extract, 5.g/l yeast extract, 1.08 g/l Tween-80(E), 2 g/l KH2PO4, 2 g/l CH3COONa, 2 g/l ammonium citrate, 0.2 g/l MgSO(4)center dot 7H(2)O and 0.05 g/l MnSO(4)center dot 4H(2)O. The optimized medium allowed a mass of produced biosurfactant (milligram per gram cell dry weight) 1.6 times higher compared to MRS standard medium. The optimum medium composition for growing S. thermophilus A consists of 5.0 g/l peptone, 5.7 g/l lactose, 5.0 g/l meat extract, 2.5 g/l yeast extract, 5.0 g/l soya peptone, 26.4 g/l sodium glycerophosphate, 0.5 g/l ascorbic acid and 0.25 g/l MgSO(4)center dot 7H(2)O. With the optimization procedure a biosurfactant mass recovery 2.1 times higher was achieved. The application of response surface methodology resulted in an enhancement in biosurfactants production. (C) 2005 Elsevier Ltd. All rights reserved. <s> BIB002 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> The nutritional medium requirement for biosurfactant production by Bacillus licheniformis K51 was optimized. The important medium components, identified by the initial screening method of Plackett-Burman, were H3PO4, CaCl2, H3BO3, and Na-EDTA. Box-Behnken response surface methodology was applied to further optimize biosurfactant production. The optimal concentrations for higher production of biosurfactants were (g/l): glucose, 1.1; NaNO3, 4.4; MgSO4 x 7H2O, 0.8; KCl, 0.4; CaCl2, 0.27; H3PO4, 1.0 ml/l; and trace elements (mg/l): H3BO3, 0.25; CuSO4, 0.6; MnSO4, 2.2; Na2MoO4, 0.5; ZnSO4, 6.0; FeSO4, 8.0; CoCl2, 1.0; and Na-EDTA, 30.0. Using this statistical optimization method, the relative biosurfactant yield as critical micelle dilution (CMD) was increased from 10x to 105x, which is ten times higher than the non-optimized rich medium. <s> BIB003 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> Abstract A newly isolated indigenous strain Pseudomonas aeruginosa J16 was able to produce a glycolipid biosurfactant (i.e., rhamnolipid). Analyses with 13 C NMR and mass spectrometry show that the biosurfactant consisted mainly of mono-rhamnolipid (RL1) and di-rhamnolipid (RL2). Since RL2 possesses higher surface activity than RLl and is of better applicability and commercial benefits, the present work attempted to enhance the production of RL2 over RL1 in P. aeruginosa J16 from the perspective of medium improvement. The results show that the abundance and composition of RL1 and RL2 varied with the type of carbon and nitrogen sources used. When glycerol and NH 4 Cl were used as the carbon and nitrogen sources, respectively, an RL2 production of 448.3 mg/L was obtained with a volumetric productivity of 4.67 mg/(L h) and a RL2/RL1 ratio of 4.22 (by mass). Replacing NH 4 Cl by (NH 4 ) 2 SO 4 , the RL2 concentration and volumetric productivity were improved to 2121 mg/L and 22.1 mg/(L h), respectively, with the RL2/RL1 ratio of 3.0. To further enhance RL2 production, statistical experimental design methodology was applied to optimize the culture medium composition favoring RL2 synthesis. Three key parameters (glycerol, (NH 4 ) 2 SO 4 , and MgSO 4 ·7H 2 O) were selected by two-level factorial design. Response surface methodology was then used to identify the optimal composition of the three key parameters, giving an optimal concentration of 0.38 M, 33.3 mM, and 577 μM for glycerol, (NH 4 ) 2 SO 4 , and MgSO 4 ·7H 2 O, respectively. With this optimal medium, the RL2 production could be markedly elevated to a maximum concentration of 3190 mg/L and a volumetric productivity of 44.3 mg/(L h), while the RL2/RL1 ratio maintained nearly constant at a value of 3.0. <s> BIB004 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> The production of biosurfactant from Rhodococcus spp. MTCC 2574 was effectively enhanced by response surface methodology (RSM). Rhodococcus spp. MTCC 2574 was selected through screening of seven different Rhodococcus strains. The preliminary screening experiments (one-factor at a time) suggested that carbon source: mannitol, nitrogen source: yeast extract and meat peptone and inducer: n-hexadecane are the critical medium components. The concentrations of these four media components were optimized by using central composite rotatable design (CCRD) of RSM. The adequately high R2 value (0.947) and F score 19.11 indicated the statistical significance of the model. The optimum medium composition for biosurfactant production was found to contain mannitol (1.6 g/L), yeast extract (6.92 g/L), meat peptone (19.65 g/L), n-hexadecane (63.8 g/L). The crude biosurfactant was obtained from methyl tert-butyl ether extraction. The yield of biosurfactant before and after optimization was 3.2 g/L of and 10.9 g/L, respectively. Thus, RSM has increased the yield of biosurfactant to 3.4-fold. The crude biosurfactant decreased the surface tension of water from 72 mN/m to 30.8 mN/m (at 120 mg L(-1)) and achieved a critical micelle concentration (CMC) value of 120 mg L(-1). <s> BIB005 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI(24))] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were approximately 3 and approximately 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were approximately 0.99 and approximately 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield. <s> BIB006 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> In order to reduce production cost of Pseudomonas aeruginosa T1, which has the ability of producing the biosurfactant when using crude oil as the sole carbon source, Three experimental methodologies (Plackett-Burman design (PBD), steepest ascent design (SAD), Central composite rotatable design (CCRD)) were applied to optimize the medium ingredients of the strain. In the verification experiment, the surface tension (ST) of strain T1 fermentation decreased from 29.93mN/m to 27.08mN/m after medium optimization. <s> BIB007 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> A potent biosurfactant-producing bacterial strain isolated from spoiled apples was identified by 16S rRNA as Pseudomonas aeruginosa MA01. Compositional analysis revealed that the extracted biosurfactant was composed of high percentages of lipid (66%, w/w) and carbohydrate (32%, w/w). The surface tension of pure water decreased gradually with increasing biosurfactant concentration to 32.5 mN m(-1) with critical micelle concentration (CMC) value of 10.1 mg L(-1). The Fourier transform infrared spectrum of extracted biosurfactant confirmed the glycolipid nature of this natural product. Response surface methodology (RSM) was employed to optimize the biosynthesis medium for the production of MA01 biosurfactant. Nineteen carbon sources and 11 nitrogen sources were examined, with soybean oil and sodium nitrate being the most effective carbon and nitrogen sources on biosurfactant production, respectively. Among the organic nitrogen sources examined, yeast extract was necessary as a complementary nitrogen source for high production yield. Biosurfactant production at the optimum value of fermentation processing factor (15.68 g/L) was 29.5% higher than the biosurfactant concentration obtained before the RSM optimization (12.1 g/L). A central composite design algorithm was used to optimize the levels of key medium components, and it was concluded that two stages of optimization using RSM could increase biosurfactant production by 1.46 times, as compared to the values obtained before optimization. <s> BIB008 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> Great emphasis has recently been given to the environmental impacts caused by chemical surfactants due to their toxicity and difficulty in being degraded in the environment [1]. Increasing environmental concerns, the advance in biotechnology and the emergence of more stringent laws have led to biosurfactants being a potential alternative to the chemical surfac‐ tants available on the market [2, 3]. Although biosurfactants have promising use in bioreme‐ diation processes, their industrial scale production is currently difficult due to high rawmaterial costs, high processing costs and low manufacturing output [3]. As a result, the current research challenges are to increase the yield and to reduce the cost of raw materials [4]. <s> BIB009 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> The main objective of this paper is to optimize biosurfactant production by Pseudomonas aeruginosa 2297 with statistical approaches. Biosurfactant production from P. aeruginosa 2297 was carried out with different carbon sources, and maximum yield was achieved with sawdust followed by groundnut husk and glycerol. The produced biosurfactant has showed active emulsification and surface-active properties. From the kinetic growth modeling, the specific growth rate was calculated on sawdust and it was 1.12 day−1. The maximum estimated value of product yield on biomass growth (Yp/x) was 1.02 g/g. The important medium components identified by the Plackett–Burman method were sawdust and glycerol along with culture parameter pH. Box–Behnken response surface methodology was applied to optimize biosurfactant production. The obtained experimental result concludes that Box–Behnken designs are very effective statistical tools to improve biosurfactant production. These results may be useful to develop a high efficient production process of biosurfactant. In addition, this type of kinetic modeling approach may constitute a useful tool to design and scaling-up of bioreactors for the production of biosurfactant. <s> BIB010 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> Abstract Rhamnolipid biosurfactant production by a novel Pseudomonas aeruginosa strain KVD-HR42 was optimized with statistical approaches. The produced biosurfactant showed surface active properties with stable emulsification activities. Based on the results of Plackett–Burman design, first-order polynomial model was developed and the following significant variables were determined viz., Karanja oil, sodium nitrate and pH. Response surface methodology experimental design was performed by Box–Behnken design to study the concentration of each component. The response plots resulted in the following optimized conditions; Karanja oil (23.85 g/L) sodium nitrate (9.17 g/L) and pH (7.8) which yielded an average biosurfactant production of 5.90±2.1 g/L at 48 h, and 37 °C temperature. The statistical approach resulted in enhanced biosurfactant production. The biosurfactant showed excellent emulsion forming capabilities and could reduce the surface tension to 30.14 mN/m at a CMC value of 100 mg/L. The biosurfactant was found to be stable at extreme conditions of temperature, pH and NaCl concentrations. Additionally surface active nature of the crude biosurfactant was demonstrated using oil displacement assay with a clearance zone of 19.26±0.23 cm 2 . Our results signify that the biosurfactant has a great industrial potential as a cleansing agent at adverse environmental conditions and provide better alternative to synthetic surfactants. <s> BIB011 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> The Plackett–Burman design and the Box–Behnken design, statistical methodologies, were employed for the optimization lipase and biosurfactant production by Ochrobactrum intermedium strain MZV101 in an identical broth medium for detergent applications. Environmental factor pH determined to be most mutual significant variables on production. A high concentration of molasses at high temperature and pH has a negative effect on lipase and biosurfactant production by O. intermedium strain MZV101. The chosen mathematical method of medium optimization was sufficient for improving the industrial production of lipase and biosurfactant by bacteria, which were respectively increased 3.46- and 1.89-fold. The duration of maximum production became 24 h shorter, so it was fast and cost-saving. In conclusion, lipase and biosurfactant production by O. intermedium strain MZV101 in an identical culture medium at pH 10.5–11 and 50–60 °C, with 1 g/L of molasses, seemed to be economical, fast, and effective for the enhancement of yield percentage for use in detergent applications. <s> BIB012 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Mixed Strategies <s> In this study, the biosurfactants (Bs) production of two Serratia marcescens strains (SM3 and its isogenic SMRG-5 strain) was improved and the tenso-active agents were purified and characterized. A 23 factorial design was used to evaluate the effect of nitrogen and carbon sources on the surface tension (ST) reduction and emulsion index (EI24 ) of the produced Bs. Optimum Bs production by SM3 was achieved at high concentrations of carbon and nitrogen, reducing ST to 26.5 ± 0.28 dynes/cm, with an EI24 of 79.9 ± 0.2%. Meanwhile, the best results for SMRG-5 were obtained at low concentrations, reducing the ST to 25.2 ± 0.2 dynes/cm, with an EI24 of 89.7 ± 0.28%. The optimal conditions for Bs production were scaled up in a 2-L reactor, yielding 4.8 and 5.2 g/L for SM3 and SMRG-5, respectively. Gas Chromatography-Mass Spectrometry (GC-MS) analysis revealed the presence of two different lipopeptides (hidrofobic fractions: octadecanoic and hexadecanoic acid for SM3 and SMRG5, respectively). Both strains were capable of benzo [a] pyrene removal (59% after 72 H of culture). <s> BIB013
Most researchers who have carried out studies on Bs production optimization or enhancement of Bs biochemical characteristics have considered the limitation of using only one statistical approach. The following examples review the use of more than one statistical design, generally involving prior screening of significant factors followed by medium optimization and improved Bs production yields. Abalos et al. BIB001 optimized a mixture of rhamnolipids produced by Pseudomonas aeruginosa, using a 2 4 full factorial design and CCD for the response surface modeling method (RSM). These authors determined that the carbon source (refined soybean oil), nitrogen source (NaNO 3 ), potassium phosphate salts (mono and dibasic), and FeSO 4 were the factors that significantly affected Bs production. Under optimum culture conditions, Bs production reached 18.7 gdm −1 . They concluded that at high concentrations of the medium components, biomass was favored and that Bs production was favored when medium components were low (except for the carbon source). Another very interesting study was carried out by Abbasi et al. BIB008 . Their strategy for finding the optimum conditions for maximum Bs production by P. aeruginosa was complete. Since the factors affecting Bs production were unknown, they carried out preliminary experiments to determine the factors that may have had an effect on Bs production. First, they evaluated the carbon, nitrogen, and physicochemical parameters based on single parameter experiments (traditional approach). Overall, 19 carbon sources and 11 nitrogen sources were screened. Once the most effective carbon and nitrogen sources were determined, Bs production was optimized in two stages using RSM approaches. First, the authors carried out a 2 4 factorial design and BBD, with the factors selected from the preliminary data. Afterwards, the CCD was implemented, based on the key medium components for Bs production; previously determined in the BBD. Analysis of this study showed that soybean oil and NaNO 3 were the most effective carbon and nitrogen sources. However, yeast extract (used as a complementary nitrogen source) was vital for maximum Bs production. Among the physicochemical factors, only temperature and shaking had a significant effect on Bs production; pH and inoculum size had no significant effect. RSM optimization of medium components resulted in an increase in Bs by 47% yielding 12.6 gL −1 , as compared to the best yield using the single factor approach (8.6 gL −1 ). This Bs reduced the ST to 32.5 mNm −1 and presented a CMC of 10.1 mgL −1 . Tian et al. BIB007 carried out a simple but very effective study optimizing P. aeruginosa Bs production and reducing production costs. They employed a PBD, followed by a Steepest Ascent Design (SAD) and a CCD. Out of the 10 factors analyzed, only NaCl, (NH 4 ) 2 SO 4 , and crude oil were significant, and thus, they were used for further optimization. At the end of the study, ST was reduced to 27.08 mNm −1 . Kumar et al. BIB010 also applied a PBD to determine the factors that had an effect on Bs production by P. aeruginosa 2297. Sawdust, glycerol, and pH were defined as the important factors and their concentrations were optimized using a 2 3 factorial BBD (RSM) to optimize Bs production. Groundnut husk, groundnut oil, and inoculum size had no effect on Bs production. In this particular study ST was reduced to 39.11 mNm −1 . In the same way, Deepika et al. BIB011 used different statistical designs for optimization of rhamnolipid production by P. aeruginosa KVD-HR42. This Bs was of interest since like other rhamnolipids it showed high stability under extreme conditions of temperature, pH, and NaCl, as well as an attractive CMC. As in other cases, a PDD design was used to determine the significant factors (Karanja oil, NaNO 3 and pH). Afterwards, RSM was performed using a BBD. After optimization, Bs production yield was 5.9 gL −1 after only 48 h, at 37 • C. Rhamnolipid production by P. aeruginosa J16 was optimized by a mixture of experimental designs BIB004 . First, different nutrients were screened to determine which ones had significant effects on Bs production. Afterwards, a SAD was used, and finally the RSM. From a total of 9 carbon sources (glycerol, methanol, ethanol, mannitol, glucose, sucrose, starch, soybean oil, and sunflower oil) glycerol and glucose had the most significant effect, followed by soybean oil and sunflower oil. Bs yield oscillated between 506 and 678 mgL −1 . P. aeruginosa was not able to grow on methanol, sucrose, and starch. Then, five nitrogen sources (NH BIB009 Cl, NH 4 NO 3 , (NH 4 ) 2 SO 4 , urea, NaNO 3 ) with glucose and glycerol were evaluated by the one-factor-at-a-time. This design uses one numerical factor and three levels for a lineal model, five levels for a quadratic model, seven levels for a cubic model, plus some replicated points (Design Expert 7, Stat-Ease, Inc., Minneapolis, MN, USA). The highest yield (2121 mgL −1 ) was obtained with glycerol and (NH 4 ) 2 SO 4 . Glycerol, (NH 4 ) 2 SO 4 and MgSO 4 ·7H 2 O were selected based on the previous PBD for optimization by SAD and RSM (BBD) producing 3037 and 3089 mgL −1 , respectively. These values were obtained in intermediate levels of the three components used. In another study, Bs production by Bacillus licheniformis K51 was optimized by initially screening 14 factors components BIB003 . CaCl 2 , H 3 PO 4 , H 3 BO 3 , CuSO 4 , ZnSO 4 , CoCl 2 , and Na-EDTA were found to be significant, while glucose, NaNO 3 , MgSO 4 ·7H 2 O, KCl, MnSO 4 , and Na 2 MoO 4 were not. Subsequently, a BBD was implemented to optimize Bs production. The most significant factors with a positive effect (CoCl 2 and H 3 PO 4 ) and with a negative effect (H 3 BO 3 and Na-EDTA) were used for the design. The RSM showed that for the interaction between CaCl 2 and H 3 PO 4 , Bs yield was higher at higher concentrations of these components, while for the interaction between H 3 BO 3 and Na-EDTA the effect was the opposite. Bs production was increased 10-fold. These two strategies (PBD and followed by a BBD) were implemented to screen and optimize the most important factors lipase and Bs production in Ochrobactrum intermedium BIB012 . Like with the other examples, the factors that had the greatest effect on production (determined by the PBD) were used for optimization. In this particular study, out of 11 factors (both nutritional and physical), only pH, temperature, and molasses were the significant factors. These authors improved Bs production 1.89-fold. Production time was also shorter, making production more feasible. The nature of the Bs was not mentioned. A mixture of statistical designs have been used for increasing the production of Bs (Sophorolipids) in proteobacteria like Lactococcus lactis and Strepococcus thermophilus BIB002 . The designs used were Fractional Factorial Design (FFD) (2 6-2 ), SAD, and CCD. Six major components of the MRS (DeMan, Rogosa and Sharpe) medium (peptone, meat extract, yeast extract, lactose, ammonium citrate, and KH 2 PO 4 ) and M17 medium (peptone, meat extract, yeast extract, lactose, soya peptone, and sodium gylcerophosphate) were evaluated. Bacterial growth was directly related to Bs production, thus, the optimum conditions for biomass production were used for Bs production. Lactose and peptone were the factors that mostly influenced cell growth. Bs production was maybe stimulated under limiting conditions in the stationary phase. After optimization, Bs production increased 1.8 and 2.1-fold for Lactococcus lactis and Strepococcus thermophilus, respectively. Mutalik et al. BIB005 enhanced Bs production of Rhodococcus spp. MTCC 2574 by combining traditional and statistical methodologies. Firstly, seven Rhodococcus strains were screened for Bs production (using the one-at-a-time approach) and one was selected. Mannitol, yeast extract, and meat peptone were used as the carbon and nitrogen sources. Additionally, n-hexadecane was used as an inducer. Afterwards, the most important factors were selected for optimization using a CCRD. Bs production yield increased 3.4-fold with initial and final values of 3.2 and 10.9 gL −1 , respectively. ST tension was reduced from 72 to 30 mNm −1 . The authors argued that mannitol could have acted as a primary source for initial bacterial growth, while hexadecane may have been used in the latter stages of growth. Recently, Rosas-Galván et al. BIB013 studied the production Bs by Serratia marcescens SM3 and its isogenic SMRG5 strain. The effect of the carbon and nitrogen sources on the production of two different lipopeptides (octadecanoic and hexadecanoic acid for SM3 and SMRG5, respectively) were evaluated. Glycerol was the carbon source studied while (NH 4 ) 2 SO 4 and peptone casein were the nitrogen sources evaluated. The statistical approach used was a 2 3 factorial design followed by the RSM. An interesting observation from this study was that the same factors evaluated had varying effects on the two S. marcescens strains. Bs production in SM3 (represented by a reduction in ST and increase in EI BIB006 ) was favored at high concentrations of glycerol and peptone, while the opposite was reported for the isogenic strain. After the application of the statistical designs, ST was reduced to 26.5 and 25.2 mNm −1 , for SM3 and SMRG5, respectively. Likewise, the EI 24 reported were 79.9 and 89.7%, for SM3 and SMRG5.