Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <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> Effect of Carbon and Nitrogen Sources <s> Effects of various factors on growth and biosurfactant production by Bacillus subtilis MTCC 2423 were studied. Sucrose (2%) and potassium nitrate (0.3%) were the best carbon and nitrogen sources. The addition of various metal supplements (magnesium, calcium, iron, and trace elements) greatly affected growth and biosurfactant production. The effect of the metal cations, used together, is greater than when they are used individually. The biosurfactant production increased considerably (almost double) by addition of metal supplements. Very high concentrations of metal supplements, however, inhibited biosurfactant production. Amino acids such as aspartic acid, asparagine, glutamic acid, valine, and lysine increased the final yield of biosurfactant by about 60%. The organism could produce biosurfactant at 45°C and within the pH range of 4.5–10.5. The biosurfactant was thermostable and pH stable (from 4.0 to 12.0). The capability of the organism to produce biosurfactant under thermophilic, alkaliphilic, and halophilic conditions makes it a suitable candidate for field applications. Infrared, nuclear magnetic resonance, and mass spectroscopy studies showed the surfactant to be identical to surfactin. <s> BIB002 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <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> BIB003 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <s> Aims: To screen and identify biosurfactant producers from petroleum-contaminated soil; to use response surface methodology (RSM) for medium optimization to enhance biosurfactant production; and to study the properties of the newly obtained biosurfactant towards pH, temperature and salinity. ::: Methods and Results: We successfully isolated three biosurfactant producers from petroleum-contaminated soil and identified them through 16S rRNA sequence analysis, which exhibit the highest similarities to Acinetobacter beijerinckii (100%), Kocuria marina (99%) and Kineococcus marinus (99%), respectively. A quadratic response model was constructed through RSM designs, leading to a 57AE5% increase of the growth-associated biosurfactant production ::: by Acinetobacter sp. YC-X 2 with an optimized medium: beef extract ::: 3AE12 g l)1 ; peptone 20AE87 g l)1 ; NaCl 1AE04 g l)1 ; and n-hexadecane 1AE86 g l)1. Biosurfactant produced by Acinetobacter sp. YC-X 2 retained its properties during exposure to a wide range of pH values (5–11), high temperatures (up to 121C) and high salinities [up to 18% (w⁄ v) Na+ and Ca2+], which was more sensitive to Ca2+ than Na+ . ::: Conclusions: Two novel biosurfactant producers were isolated from petroleumcontaminated soil. Biosurfactant from Acinetobacter sp. YC-X 2 has good properties to a wide range of pH, high temperature and high salinity, and its production was optimized successfully through RSM. <s> BIB004 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <s> Aim ::: ::: To improve biosurfactant production economics by the utilization of potential low-cost materials. ::: ::: ::: ::: Methods and Results ::: ::: In an attempt to utilize cost-effective carbon sources in the fermentative production of biosurfactants, various pure and waste frying oils were screened by a standard biosurfactant producing strain. Considering the regional significance, easy availability and the economical advantages, waste frying coconut oil was selected as the substrate for further studies. On isolation of more competent strains that could use waste frying coconut oil efficiently as a carbon source, six bacterial strains were isolated on cetyltrimethyl ammonium bromide–methylene blue agar plate, from a soil sample collected from the premises of a coconut oil mill. Among these, Pseudomonas aeruginosa D was selected as the potential producer of rhamnolipid. Spectrophotometric method, TLC, methylene blue active substance assay, drop collapse technique, surface tension measurement by Du Nouy ring method and emulsifying test confirmed the rhamnolipid producing ability of the selected strain and various process parameters were optimized for the production of maximum amount of biosurfactant. Rhamnolipid components purified and separated by ethyl acetate extraction, preparative silica gel column chromatography, HPLC and TLC were characterized by fast atom bombardment mass spectrometry as a mixture of dirhamnolipids and monorhamnolipids. The rhamnolipid homologues detected were Rha-Rha-C10-C10, Rha-C12-C10 and Rha-C10-C8/Rha-C8-C10. ::: ::: ::: ::: Conclusion ::: ::: These results indicated the possibility of waste frying coconut oil to be used as a very effective alternate substrate for the economic production of rhamnolipid by a newly isolated Ps. aeruginosa D. ::: ::: ::: ::: Significance and Impact of the Study ::: ::: Results of this study throws light on the alternate use of already used cooking oil as high-energy source for producing a high value product like rhamnolipid. This would provide options for the food industry other than the recycling and reuse of waste frying oils in cooking and also furthering the value of oil nuts. <s> BIB005 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <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> BIB006 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Effect of Carbon and Nitrogen Sources <s> A Pseudomonas aeruginosa AK6U strain produced rhamnolipid biosurfactants to variable extents when grown on MgSO4 or organosulfur compounds as sulfur sources and glucose as a carbon source. Organosulfur cultures produced much higher biosurfactants amounts compared to the MgSO4 cultures. The surface tension of the growth medium was reduced from 72 mN/m to 54 and 30 mN/m in cultures containing MgSO4 and 4,6-dimethyldibenzothiophene (4,6-DM-DBT), respectively. AK6U cultures produced different rhamnolipid congener profiles depending on the provided sulfur source. The dibenzothiophene (DBT) culture produced more diverse and a higher number of rhamnolipid congeners as compared to the DBT-sulfone and MgSO4 cultures. The number of mono-rhamnolipid congeners in the DBT culture was also higher than that detected in the DBT-sulfone and MgSO4 cultures. Di-rhamnolipids dominated the congener profiles in all the analyzed cultures. The sulfur source can have a profound impact on the quality and quantity of the produced biosurfactants. <s> BIB007
For Bs production, the culture media and culture conditions are extremely important for feasible Bs production. The use of adequate nutrients (carbon and nitrogen sources) directly influences the Bs metabolism, and thus, production BIB001 BIB007 . Apart from directly participating in cell growth and reproduction, the carbon and nitrogen sources are used as building blocks for Bs biosynthesis. For example, oily substrates such as coconut oil and soybean oil are used for the biosynthesis of lipidic tails of glycolipids and lipopeptides BIB005 . While sugars such as rhamnose form part of the polar head of glycolipids. Amino acids have also been found to positively increase Bs production in the case of surfactin and iturin, both of which have polar heads formed of amino acids BIB002 . Different nutrient sources may regulate Bs synthesis by induction or catabolic repression. Immiscible substrates such as different oils and hydrocarbons have been used as Bs production inducers  BIB003 . Catabolic repression has been observed in hydrocarbon degrading bacterial strains that do not produce surface-active molecules in the presence of carbon sources like glucose and organic acids. This can be explained by the fact that hydrocarbon uptake is promoted by Bs production. While sugars such as glucose stimulate biomass growth, and generally Bs are produced in the stationary phase  . Although Bs production has been observed to be highest in the stationary phase of microorganisms such as Bacillus sp. and P. aeruginosa BIB006 , production has also been detected in the exponential phase of bacteria such as Acinetobacter sp. (when the bacteria reached the stationary phase, Bs production had already reached its maximum); this has also been reported for Rhodococcus sp. and Aspergillus sp. strains BIB004 . Seeing that the carbon and nitrogen sources are vital for Bs biosynthesis, apart from selecting and analyzing the strains of interest, choosing adequate carbons and nitrogen sources is of utmost importance for efficient Bs production. Hence, the importance for the optimization of Bs production using experimental designs. The criteria for choosing adequate carbon and nitrogen sources depends on the producing strain and on the nature of the Bs of interest. In the event of the possibility of using numerous carbon and nitrogen sources, traditional screening or statistical design are implemented. Not only are the significant factors determined, but the levels (concentrations) and relevant interactions are inferred.