Neomycin production of Streptomyces fradiae NCIM was optimized by using response surface methodology RSM , which is powerful mathematical approach comprehensively applied in the optimization of solid state fermentation processes. In the first step of optimization, with Placket-Burman design, ammonium chloride, sodium nitrate, L-histidine, and ammonium nitrate were established to be the crucial nutritional factors affecting neomycin production significantly.
In the second step, a 2 4 full factorial central composite design and RSM were applied to determine the optimal concentration of significant variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. The optimum values for the important nutrients for the maximum were obtained as follows: ammonium chloride 2.
The determination coefficient R 2 was 0. Neomycin is a topical antibacterial antibiotic with low toxicity.
This antibiotic is employed for treating a variety of bacterial infections including diseases of skin, wound injury, and tuberculosis. This antibiotic is also used in veterinary practice, in storage tanks of petroleum fuels where it prevents the formation of sludge, and in rubber tree plantations where it increases the flow of latex by preventing bacterial infection of tapping wounds [ 1 ]. Neomycin is a bacteriostatic compound active against Gram-positive, Gram-negative, and acid-fast bacteria [ 2 ].
Neomycin was first isolated from Streptomyces fradiae [ 2 ]. Extensive literature is available on neomycin production by Actinomycetes spp. Traditionally, neomycin has been produced by submerged fermentation SmF.
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In recent years, however, the solid-state fermentation SSF processes have been increasingly applied for the production of this antibiotic [ 6 ].
SSF compared to SmF is more simple, requires lower capital, has superior productivity, reduced energy requirement, simpler fermentation media, and absence of rigorous control of fermentation parameters, uses less water and produces lower wastewater, has easier control of bacterial contamination, and requires low cost for downstream processing [ 7 , 8 ].
In the SSF process, the solid substrate not only supplies the nutrients to the culture, but also serves as harborage for the production strains.
The moisture content of the medium changes during fermentation as a result of evaporation and metabolic activities and thus optimum moisture level of the substrate is therefore most important [ 9 , 10 ].
One of the largest oil cakes abundantly produced in India is coconut oil cake from the edible oil extraction industries. Coconut oil cake is dried and marketed as a component of animal feed, but since the selling price of the products is low and the evaporation of water from this waste consumes large amounts of heat, the production of dried coconut oil cake feed is valuable only as a waste disposal method, with marginal economic benefits [ 11 ].
Their composition varies depending on their variety, growing condition, and extraction methods [ 12 ]. Several processes and products have been reported that utilize coconut oil cake as a raw material. Medium optimization was approached either classically [ 6 ] from two factorial Placket-Burman Design to multifactorial design response surface methodology [ 17 ], are accessible for overcoming such limitations.
In the multifactorial design, the experiments involved are 2 n , where n is the number of nutritional factors. Optimization of solid fermentation media is generally done at a two-step level, where initially the effects of the nutrients on product formation are screened and a few parameters are selected and optimized [ 18 ].
The optimal design of fermentation media is a considerable aspect to be sensible in the advancement of solid state fermentation processes. In the particular case of antibiotics, the interaction between growth metabolism and product secretion is critically affected by growth limiting nutrient concentrations.
Designs of experimental techniques are very beneficial tools for this purpose, as they can accommodate statistical models with a relatively small number of experiments. From these models, the relative effect of various factors studied can be determined and their optimal concentration measured for a given target such as maximal antibiotic production. The present work was directed at optimization of medium components, which have been initiated to play very capital role in decorating the neomycin production, with the accommodate of Placket Burman design and central composite design.
In the introductory step of optimization, four carbon sources, four amino acids, four minerals, and four inorganic nitrogen sources were evaluated to arrange optimal carbon and inorganic sources, amino acids, and minerals.
Then, a Placket-Burman design was exploited to describe which components of the media had significant effects on neomycin production; subsequently, a central composite design was employed to optimize the nutritional factors, which had significantly altered neomycin production. The results were analyzed by response surface analysis.
The initial pH of the media was adjusted to 7.
The age of the organism in the slant growth, seed age, and inoculum levels were maintained as described previously [ 5 ]. Coconut oil cake was obtained from Kollam, Kerala, India, and was chopped by a chopper into small pieces, dried, and ground in a hammer mill. These ground materials were then separated by sieves into particles of desired size moderate corse size and were used in media.
Neomycin production was carried out using coconut oil cake as the basic solid substrate unless otherwise stated. The initial pH of the growth media was pH 7. Both initial pH and moisture content were measured in preliminary experiments. To each flask, 2. Neomycin was eluted at 4. Limit of detection LOD was 0. These nutrients were, respectively, added in to the flask with coconut oil cake as a basal medium. In multifarious cases, there are an ample number of nutritional factors, which require to be identified for their consideration to the conditional variable of attraction.
The most glandular advance would be to very those nutritional factors in a central composite design, that is, to attempt all feasible combinations of settings.
Full factorial design requires 2 N N denotes number of nutritional factors experiments. This would achieve good optimization condition, barring that the account of essential runs in the experiments observations will amplify geometrically.
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In our case, eleven variables to be examined, it requires 2 11 experiments, which is very ample and time consuming. There will be an enormous cut-down in total experiments. In this part, the preferred minerals copper sulphate and zinc sulphate , amino acids L-histidine, L-glutamic acid, and L-asparatic acid , carbon soluble starch, maltose, and glucose , and inorganic nitrogen sources sodium nitrate, ammonium nitrate, and ammonium chloride were optimized Table 1.
The design matrix Table 2 was developed using an SAS package trial version 9. Plackett-Burman experimental design for the evaluation of nutritional factors affecting neomycin production by Streptomyces fradiae NCIM Each independent nutritional factor was set at two level, that is, high and low level Table 1.
The high level of each independent nutritional factor was set far-adequately from the low level to ascertain which nutrients of the media have influential effect on the neomycin production.
After the identification of the nutrients touching the neomycin production significantly, a CCD was adopted to optimize the appreciable nutrients ammonium chloride, sodium nitrate, L-histidine, and ammonium nitrate , which were preferred through Placket-Burman design. The experiment was designed by SAS package, trial version 9. The experiments were performed in duplicate and the mean values are taken for analysis.
A second-order polynomial, 1 , which admits all interaction terms, was used to calculate the predicted response:. The variable X i was coded as x i according to the following:.
An SAS package, trial version 9. The F -test was occupied to calculate the statistical significance of the quadratic polynomial. The multiple coefficient of correlation R and the determination coefficient of correlation R 2 were calculated to estimate the production of the regression equation. In the introductory step of optimization, the preferred nutrients were added to coconut oil cake separately.
The complementary nutrient really advances concentration of neomycin of Streptomyces fradiae NCIM Results indicated that they almost completely stimulated the production of neomycin at eighth day of incubation.
L-arginine did not affect neomycin yield Figure 2. Almost all the carbon sources tested increased the neomycin production at eighth day of incubation.
Sucrose reduced the production of neomycin Figure 3. Supplementation with various inorganic nitrogen sources gave a significantly higher neomycin yield at day 8 of incubation period.
Based on the above experiments, soluble starch, maltose, glucose, copper sulphate, zinc sulphate, L-asparatic acid, L-glutamic acid, L-histidine, sodium nitrate, ammonium chloride, and ammonium nitrate were selected for statistical optimization.
Eleven different nutritional factors including soluble starch, maltose, glucose, copper sulphate, zinc sulphate, L-asparatic acid, L-glutamic acid, L-histidine, sodium nitrate, ammonium chloride, and ammonium nitrate were sheltered for their effect on neomycin production using the Plackett-Burman design.
The independent variables examined and their settings are shown in Table 1. This change reflects the account of medium optimization to accomplish higher productivity. The analysis of the data from the Plackett-Burman experiments involved a first-order main effects model. The main effects of the nutritional examined factors on the neomycin production were measured and conferred graphically in Figure 5.
On the basis of the analysis of the estimate of the eleven variables after day 8 of incubation, ammonium chloride and sodium nitrate showed large positive effect on neomycin production.
L-histidine and ammonium nitrate showed large negative effect on neomycin production. Soluble starch, glucose, sucrose, maltose, zinc sulphate, manganese sulphate, ferrous sulphate, copper sulphate, L-arginine, L-asparatic acid, and L-glutamic acid have a slight effect on neomycin productivity. Figure 5 shows the ranking of nutritional factor estimate in a Pareto chart.
The Pareto chart displays the magnitude of each nutritional factor estimate and it is a convenient way to view the results of a Plackett-Burman design. According to Plackett-Burman design experiment, four significant variables X 10 ammonium chloride , X 9 sodium nitrate , X 8 L-histidine , and X 11 ammonium nitrate were preferred for advance optimization using central composite design RSM to calculate the several optimal values.
The coded values of the variables at various levels in central composite design RSM analysis were given in Table 3. Response results shown in Table 4 were analyzed using SAS package, trial version 9. The t -test and P values were used to determine the effect of each nutritional factor on neomycin production Table 6. The fit of the model was checked by the coefficient of determination R 2 , which was measured to be 0. The model can be shown as follows:.
The significance of each estimate was determined by P values which are listed in Table 5.
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Among the model terms, the linear effects of sodium nitrate and L-histidine were more significant than other nutritional factors. These suggest that the concentrations of sodium nitrate and L-histidine were a direct relationship with the neomycin production. The estimate for quadratic effect of sodium nitrate was more significant neomycin production.
The interaction between ammonium chloride and L-histidine, ammonium chloride and ammonium nitrate, sodium nitrate and L-histidine, and L-histidine and ammonium nitrate had significant influence on neomycin production.
Generally, the measured F value should be several times greater than tabulated F value if the model is admirable prediction of experimental results and the estimated nutritional factors effects are actual.
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Consequently, these results indicate a good adequacy in the models for neomycin yield. Three-dimensional surface plot of neomycin production as function of ammonium chloride X 10 and L-histidine X 8 ammonium nitrate and sodium nitrate were kept at zero level.
Three-dimensional surface plot of neomycin production as function of ammonium chloride X 10 and ammonium nitrate X 11 L-histidine and sodium nitrate were kept at zero level. The 3D surface plots of the neomycin production with respect to the concentrations of ammonium chloride and L-histidine are shown in Figure 6.
The neomycin production increased with the increase concentration of ammonium chloride and L-histidine when sodium nitrate and ammonium nitrate were held at zero level.
Figure 7 showed effect of sodium nitrate concentration and L-histidine concentration on neomycin production when ammonium chloride and ammonium nitrate were held at zero level.
It can be observed that an increase of neomycin production with increase in concentration of L-histidine and further increase in concentration leads to decrease neomycin production and increase in concentration of sodium nitrate with increases the neomycin production and further increase in concentration leads to decrease neomycin production.