|Year : 2021 | Volume
| Issue : 1 | Page : 23-32
Statistical optimization of lipase production in solid-state fermentation by Aspergillus tamarii NDA03a and application of the fermented solid as a biocatalyst for biodiesel production
Hanan M Ahmed1, Sayeda S Mohamed1, Maysa E Moharam2, Magda A El-bendary2, Hisham A Abd El-lateaf3, Hala A Amin1
1 Department of Chemistry of Natural and Microbial Products, National Research Centre, Dokki, Cairo, Egypt
2 Department of Microbial Chemistry, National Research Centre, Dokki, Cairo, Egypt
3 Department of Fats and Oils, National Research Centre, Dokki, Cairo, Egypt
|Date of Submission||16-Apr-2020|
|Date of Decision||16-May-2020|
|Date of Acceptance||26-Jun-2020|
|Date of Web Publication||26-Mar-2021|
Hanan M Ahmed
Department of Chemistry of Natural and Microbial Products, National Research Centre, 33 El-Behooth Street, Dokki, Giza 12622
Source of Support: None, Conflict of Interest: None
Background and objective Biodiesel, an attractive alternative fuel, is defined by the American Society for Testing and Materials (ASTM) as fatty acid methyl esters (FAME). Biodiesel is an ecofriendly fuel compared with many other transportation fuels. The aim of this study was to implement the statistical approaches for optimization of Aspergillus tamarii NDA03a mutant G lipase produced in solid-state fermentation (SSF), and then application of the dried fermented solid as a biocatalyst for biodiesel production from waste frying oil (WFO).
Materials and methods A. tamarii NDA03a mutant (3G) was previously selected as a good lipase producer. Five oil residue meals were evaluated in the presence of wheat bran (WB) for their potential as enzyme inducers and substrates for the production of 3G lipase by SSF. The best oil residue meal was selected and used in subsequent experiments. The fermented solid thus obtained was collected, lyophilized, and used as a biocatalyst for waste frying oil transesterification to FAME. To optimize SSF conditions for lipase production using 3G, a Plackett–Burman design was used at first to screen the critical factors from several process variables, and finally, a central composite design was applied to further estimate the relationship between the variables and response as well as optimize the levels. Response was measured in terms of FAME yield. To verify the adequacy and accuracy of the model, validation experiments were also carried out.
Results and conclusion The most favorable oil residue meal that enhances 3G lipase production by SSF was black cumin meal. Results of the Plackett–Burman design revealed that the factors contributing to the main effect were incubation temperature, incubation period, and moisture content. The optimal SSF conditions for lipase production were WB 10 g, black cumin meal 6% (w/w of WB), pH 8, temperature 28°C, moisture content 40%, molasses 1% (w/w of WB), and incubation period 3 days. Under these optimized conditions, produced FAME yield (65.55%) increased by 58% compared with the basal medium (41.46%). A good agreement between the experimental (65.55%) and predicted (65.03%) values was detected. The significance of this model was confirmed by its probability value and lack of fit (P<0.05) and clearly showed that the model was sufficient to describe the correlation between the FAME yield and the tested variables. The obtained results ascertained the success of response surface methodology as an efficient technique to optimize the lipase production in SSF and consequently the ability of application of the dried fermented solid as a biocatalyst for biodiesel production.
Keywords: central composite design, fermented solid, lipase, transesterification, waste frying oil
|How to cite this article:|
Ahmed HM, Mohamed SS, Moharam ME, El-bendary MA, Abd El-lateaf HA, Amin HA. Statistical optimization of lipase production in solid-state fermentation by Aspergillus tamarii NDA03a and application of the fermented solid as a biocatalyst for biodiesel production. Egypt Pharmaceut J 2021;20:23-32
|How to cite this URL:|
Ahmed HM, Mohamed SS, Moharam ME, El-bendary MA, Abd El-lateaf HA, Amin HA. Statistical optimization of lipase production in solid-state fermentation by Aspergillus tamarii NDA03a and application of the fermented solid as a biocatalyst for biodiesel production. Egypt Pharmaceut J [serial online] 2021 [cited 2022 Sep 29];20:23-32. Available from: http://www.epj.eg.net/text.asp?2021/20/1/23/312840
| Introduction|| |
Biodiesel, an attractive alternative fuel, is defined by the American Society for Testing and Materials as fatty acid methyl esters (FAME). Biodiesel is an ecofriendly fuel compared with many other transportation fuels. It is a biodegradable, renewable, carbon neutral, land nontoxic fuel . It can be used without any modification in diesel engines and can be blended with any ratio . FAME can be produced through esterification of free fatty acids or transesterification of glycerides in the presence of alcohol . Waste frying oil (WFO) is regarded as a potential alternative feedstock for biodiesel production . Its use for biodiesel production is very important to reduce the production cost of this cost-sensitive energy product and to eliminate the environment pollution and human health risk caused by WFO inappropriate disposal ,.
Lipase-catalyzed transesterification is an ecofriendly alternative to chemical process using acid or base catalyst owing to an improved selectivity, a nontoxic catalyst, a lower process temperature, pressure and waste, a simultaneous esterification of free fatty acids and transesterification of glycerides, and an easier separation of biodiesel and glycerol . However, the high cost of the lipase enzyme is the main obstacle for a commercially feasible enzymatic production of biodiesel fuel. To overcome this problem, production of lipase by solid-state fermentation (SSF), as well as usage of the dried fermented solid with active lipase activity as a biocatalyst instead of purified enzyme is a potential way to reduce enzyme production cost because the extra-extraction, purification, and immobilization steps are not necessary ,.
Among the evaluation of various fungi for their ability in lipase production, Aspergilli are devoted to being efficient and potential producers ,. Several renewable, cheap, and agro-industrial residues such as babassu cake, soybean, rice husk, and wheat bran (WB) have been used to produce lipase by Aspergillus species in SSF process ,,. Many advantages have been offered by SSF as compared with submerged fermentation like low production cost, high productivity, saving of water and energy, and the use of various agro-industrial residues as substrates .
To produce high-quality biodiesel products, an optimization strategy for lipase production in SSF process should be established. Response surface methodology (RSM) is an efficient and imperative tool for the optimization of various fermentation processes and multivariable systems . RSM is considered as a combination of statistical and mathematical protocols where no complex calculations are required to analyze the resulting data . Other advantages of using RSM are to search for relations between factors and the most suitable conditions for desirable response, and to be cost effective than traditional variation of one parameter at a time .
The use of dry fermented solid with lipase activity as a biocatalyst for biodiesel production has been reported by many researchers ,,. Aspergillus tamarii NDA03a mutant G (3G) has been recently identified as a potential producer of lipase, which effectively transesterifies WFO to FAME . Cultivation conditions determining the production of lipase by this mutant in SSF with WB as a substrate have not been studied. Hence, the present work aims to implement the statistical approaches for optimization of 3G lipase production in SSF and application of the obtained fermented solid as a biocatalyst for biodiesel production from WFO. The optimization process involved two steps: Plackett–Burman (PB) design and RSM considering FAME yield as a response for 3G lipase production ability.
| Materials and methods|| |
WFO was collected from local restaurants. WB was purchased from a local market in Egypt. Methyl heptadecanoate standard was purchased from Sigma-Aldrich Chemical Co. (St Louis, Missouri, USA). Methanol, hexane, diethyl ether, and acetic acid were purchased from Merck Chemical Co. (Darmstadt, Germany). potato dextrose agar (PDA) and medium ingredients were purchased from Fisher Scientific (Hampton, New Hampshire, USA). All other chemicals were of analytical grade.
Isolation, molecular identification, and mutation of Aspergillus isolate
A. tamarii NDA03a was previously isolated, identified, and selected as a good lipase producer . It was isolated by suspension of 5 g of a soil in Egypt (30° 08′ 25.6″ N 31° 16′ 20.6″ E) in 50-ml sterile physiological saline solution. Isolation was carried out using the pour plate dilution method  on PDA in which carbon source was replaced by olive oil. The plates were incubated at 28°C for 3–5 days. This isolate was molecularly identified using rRNA gene sequence ITS1 and ITS2 as A. tamarii NDA03a (Genbank Accession Number MK849615) as reported by Elhussiny et al. . This identified isolate was exposed to ethyl methanesulfonate for producing hyperlipolysis mutants . The mutagenesis process was carried out as follows: 10 ml of 200 mM ethyl methanesulfonate solution was applied to freshly prepared 5-day-old fungal spore suspension (7×108) for 1 h. Phosphate buffer pH 7, 0.2 M, was used to dilute the mixture to cease the mutagenesis process. Samples were cultivated on PDA at 28°C for 3–5 days. A. tamarii NDA03a mutant G (3G) was previously selected based on its high ability to transesterify WFO .
Preparation of oil residue meals
Sesame, almond, watercress, black mustard, and black cumin meals were prepared from Sesamum indicum, Prunus amygdalus, Eruca sativa, Brassica nigra, and Nigella sativa seeds, respectively, by oil extraction using hydraulic press extraction method. The extraction procedure was performed as follows: the dried seeds were milled to a fine powder. The samples of seed powder were wrapped in a thick heavy duty cloth, and then the oil extraction was carried out using simple hydraulic press with a maximum pressure of 3500 psi for 1 h at room temperature . The extracted oil was collected, and the extracted meals or cakes which remained after oil extraction from the seeds were milled into a fine powder and packed in polyethylene bags and stored in deep freezer at −20±2°C until analyzed. All extractions were performed in triplicate.
Chemical analysis of prepared meals
Moisture content, total lipids, crude protein (N×6.25), crude fiber, and ash of prepared oil residue meals were determined according to the methods outlined in the Association of Official Analytical Chemists . Total carbohydrate content was calculated by the difference in weights.
Lipase production by solid-state fermentation
Five oil residue meals were evaluated for their potential as enzyme inducers and substrates for 3G lipase production by SSF and application of produced fermented solid for WFO transesterification. Initially, 10 g of WB and 6% (w/w of WB) of each oil meal as a substrate were taken individually, moistened with distilled water, and autoclaved for 30 min. The medium was inoculated with 3G of 3-day-old fresh PDA culture and incubated at 30°C for 6 days under static conditions. Each fermentation test was repeated in triplicate. The best oil residue meal was selected and used in the subsequent experiments. The fermented matter thus obtained was collected, lyophilized, and used as a biocatalyst. The lipase activity in the fermented solid was evaluated by its ability to transesterify WFO to biodiesel (FAME).
Methanolysis of waste frying oil
Methanolysis of WFO was carried out in 100-ml Erlenmeyer flasks at 35°C with constant shaking at 250 rpm for 72 h. The reaction mixture consisted of 5 g WFO, 10% (w/w of WFO) dried fermented solid, 15% (w/w of WFO) 0.2 M Tris buffer (pH 7.5), and 3 : 1 methanol to WFO molar ratio (added stepwise to the reaction mixtures three times at 0, 24, and 48 h reaction time). WFO was previously emulsified with Tris buffer (pH 7.5) before the addition of the biocatalyst using ultrasonication. At the end of reaction time, fermented solid biocatalyst was separated from the reaction mixture by centrifugation at 10 000 rpm for 15 min. The upper oil phase containing esters was analyzed qualitatively by thin-layer chromatography  and quantitatively by capillary gas chromatography (GC) .
Analyses of fatty acid methyl esters
Thin-layer chromatography was performed on pre-coated silica gel plate (Merck, Kenilworth, New Jersey, USA). The plate was chromatographed for FAME with a solvent system of hexane : diethyl ether : acetic acid (80 : 20 : 1, v/v/v). The chromatograms were developed with iodine vapor. FAME in the oil phase was analyzed by an Agilent Technologies (Santa Clara, California, USA) 6890 N GC equipped with flame ionization detector and a fused silica capillary column (30 mm×0.32 mm×0.25 mm). The GC temperature condition was oven temperature of 210°C using helium as a carrier gas, flame ionization detector temperature of 250°C, and injector temperature of 250°C. Overall, 10 mg/ml of methyl heptadecanoate solution was used as an internal standard, and the FAME content expressed as a mass fraction in percent was calculated by the use of the equation 1. The peak identification was made by comparing the retention time between the sample and the standard compound.
Where ΣA=total peak area of FAME; AIS=peak area of internal standard (methyl heptadecanoate); CIS=concentration of the internal standard solution (mg/ml); VIS=volume of the internal standard solution used (ml); and m=mass of the sample (mg).
Experimental design for 3G lipase production by solid-state fermentation
A PB design was used at first to screen critical factors from several process variables, and finally, a central composite design (CCD) was applied to further estimate the relationship between the variables and response as well as optimize the levels. Response was measured in terms of FAME yield. The PB design with the name and level of the variables is shown in [Table 1]. Each independent variable is represented in two levels, high and low, which are denoted by +1 and −1, respectively. The design comprised 13 experiments with two replicates at the center point (0). Fermentation was carried out in duplication, and the average value was taken as the response. Usually, the variable with a P value of less than or equal to 0.05 was considered to have a significant effect on the response  and was selected for further CCD optimization.
To determine the optimum level of selected variables (temperature, moisture, and time) as shown in [Table 2], that were screened from the PB design and to investigate their interactions, RSM using CCD was applied. These variables were tested at three levels. An experimental design of 16 experiments was formulated. Response was measured in terms of FAME yield. To represent the graphical examination or descriptions of the experimental results, three-dimensional response surfaces were developed using JMP8 statistical software (SAS Institute Inc. Cary, North Carolina, USA). Depending on this design, the effect of the independent significant variables (P≤0.05) on the response (FAME yield) can be evaluated. These 3D plots were showing the relation between any two independent variables and response by maintaining the other independent variable at a constant value.
|Table 2 Selected variables and levels for central composite design experimental design|
Click here to view
| Results and discussion|| |
Effect of different oil meals on 3G lipase production in solid-state fermentation
Different oil extraction residues were tested as an inducer for the production of lipase in the presence of WB as a substrate/support material. WB was reported as the best substrate for lipase production by dos Santos et al. , as it contained 3.55% of lipid, rich in carbohydrates and fibers, and it could be used with dual function as a carbon source and as a physical support for fungal growth. Supplementation of SSF medium with oil extraction residues is attractive, as they are abundant, cheap, renewable, and nutrient sources (contain nitrogen, carbon, and minerals). Moreover, each residue could serve as a physical support and as an inducer for the production of lipase ,. As shown in [Table 3], some tested oil extraction residues supported WFO transesterification using 3G dried fermented solid to different degrees. The most favorable oil residue meal that enhance 3G lipase production in SSF was black cumin meal that resulted in the highest FAME yield of 41.46%. This could be attributed to its relatively high oil and carbohydrate contents as shown in [Table 4]. Black cumin meal could be served as an enzyme inducer and a carbon source for microbial growth . On the contrary, watercress and almond meals were accompanied by inferior FAME yields (27.77 and 25.38%, respectively) compared with the control (with no meals). This may be attributed to the inhibition effect caused by the high amounts of protein (42.9%) found in almond meal and high amounts of fiber found in watercress meal (21.77%). The carbohydrate contents were approximately similar among used oil meals; this indicated that the carbohydrate content was a nonsignificant factor. Another explanation was related to Halldorsson et al. , who reported that all of the lipases prefer the presence of more saturated fatty acids as substrates. As black cumin meal contains suitable amounts of saturated fatty acid (14.66 g/100 g of total fatty acid) as reported by Thilakarathna et al. , it could enhance lipase production, leading to the highest yield of FAME (41.46%). However, the watercress seed oil exhibited the greatest variety of fatty acids represented by the majority of unsaturated fatty acids such as linolenic acid (34%) and oleic acid (22%), followed by saturated fatty acids, such as palmitic 10.1%), stearic acids (2.9%), and arachidic acid (3.4%), which were presented in low amounts . Moreover, nuts are natural foods rich in monounsaturated or polyunsaturated fatty acids, and no saturated fatty acids are present .
|Table 3 Screening of different oil meals for 3G lipase production in solid-state fermentation|
Click here to view
Screening of significant variables by Plackett–Burman design
PB design was used to screen eight different medium components and fermentation conditions as 13 run experiments with two levels of each variable. The FAME yield response (dependent variable) of PB experimental design for 13 trials is given in [Table 5]. Based on the obtained results, the highest and lowest FAME yield of 43.71 and 6.39%, respectively, were observed in runs 10 and 13, respectively. The main effect of each variable on 3G lipase production in SSF is represented in [Figure 1]. Results revealed that the factors contributing to the main effect were incubation temperature, incubation period, and moisture content. The other factors (medium pH, substrate concentration, molasses concentration, and inoculum size) had insignificant effects on lipase production in SSF by 3G. As shown in [Figure 1], relatively high negative effects of the incubation temperature (15.25%) and moisture content (6.68%) were observed in the tested ranges, whereas fodder yeast, incubation time, and substrate concentration (black cumin meal) had low negative effects. On the contrary, the other factors (inoculum size and medium pH) had positive effects on FAME yield. This indicated that the moisture content must be lowered than 60% because a high concentration of moisture results in great decreases in the production of microbial metabolites in SSF and can cause agglomeration of medium particles and lead to oxygen transfer limitations . On the contrary, Nema et al.  reported a positive influence of the moisture content on lipase production by Aspergillus niger MTCC 872. The observed negative effect of the incubation temperature could be explained by the denaturation of the tertiary structure of the enzyme protein caused by excessive heat .
|Table 5 Experimental design for evaluation of factors affecting 3G lipase production in solid-state fermentation|
Click here to view
|Figure 1 The main effect of each variable on 3G lipase production in SSF.|
Click here to view
The other factors like fodder yeast and substrate concentration (black cumin meal) had low negative effects (−2.48 and −0.77%, respectively) on lipase production assayed by FAME yields; hence, they were maintained at their low levels. However, a slight positive effect of molasses (0.86%) on FAME yield was observed ([Figure 1]); thus, it was maintained at its high level. Molasses, a by-product of the sugar industry, is presenting characteristics such as low cost, abundance, and easy storage at room temperature. So, it was used as a carbon source in the SSF medium. These results were similar to those obtained by Melissa et al.  who found that higher molasses concentrations did not further increase enzyme production but also did not repress enzyme synthesis. Moreover, Vasiee et al.  found that molasses had a slightly high positive effect on lipase production.
Based on PB results, the insignificant variables were ignored and the values of the incubation temperature, moisture content, and incubation time were selected for the further study by a CCD to attain their optimal levels.
Response surface methodology using central composite design
Once the ranges of the three independent variables, that is, incubation temperature, moisture content, and incubation period, were selected through the PB screening, a 3-factor-3-level CCD was employed to estimate the relationship between the variables and response (FAME yield) as well as optimize their levels. The highest FAME of 66.99% was observed at run 8 ([Table 6]). As shown in [Figure 2], the predicted and experimental values were significant, which suggested that the model gave a good fit.
|Table 6 Experimental plan for optimization of lipase production in solid-state fermentation using central composite design|
Click here to view
|Figure 2 Actual by predicted plot CCD design for 3G lipase production in SSF.|
Click here to view
Model fitting and analysis of variance
Analysis of variance (ANOVA) was used to evaluate the significance of the quadratic polynomial model . So, the results obtained from CCD were then analyzed by standard ANOVA, as shown in [Table 7]. The model R2 value of 0.87 indicates that the statistical model can explain 87% of variability in the response (a value of R2>0.75 indicated the correctness of the model). Colla et al.  studied optimization of lipase production under submerged fermentation by filamentous fungi using RSM and found that a coefficient of determination (R2) was 0.89 and an adjusted coefficient of determination (R2 adjusted) was 0.86. Moreover, the higher model F value (24.91), small P value of 0.0019, and lower lack of fit (7.68) indicated the accuracy of the model and implied its significance in describing the relation between the FAME yield and the given variables ([Figure 3]). These results are similar to those obtained by Kaushik et al.  and Ebrahimi et al. , who indicated that the high F value and small low P value indicated the accuracy of the model.
|Figure 3 Contour and 3D diagram of the effect of (a) incubation temperature and moisture content, (b) moisture content and incubation period, and (c) incubation temperature and incubation period on FAME yield.|
Click here to view
The P value is used as a tool to investigate the significance of each regression coefficient . So, the significance of each parameter that was evaluated by the P value is listed in [Table 8]. The most significant parameters (P<0.05) were the incubation time (X5), square of incubation time (X5)2, and the interactive effect of the incubation time (X5), and moisture content (X4). Meanwhile, the other terms were insignificant (P>0.05). For example, the effect of moisture content (X4) only is very negligible. Furthermore, the interactions of X3 (temperature) with X4 (moisture content) and X3 (temperature) with X5 (incubation time) have a positive interaction.
|Table 8 Regression analysis of factors for 3G lipase production in solid-state fermentation|
Click here to view
Moreover, the model matched with a full second-order polynomial equation as described by equation 2:
Where X3 is temperature, X4 is moisture content, X5 is incubation time, and Y is the response.
The largest coefficient with negative value was given by (X5)2. This means that the long incubation period, the less lipase production in SSF, and consequently, low FAME yield is detected. So, 3-day incubation period was the best, as shown in runs 3, 7, 8, 9, and 10, in which these optimized dried fermented solids gave a high range of FAME (61.25–66.99%) at 28°C, and 40–60% moisture contents. So, the incubation period (X5) was the most significant variable, as it had the largest coefficient and its P value of 0.055.
The effect of solid-state fermentation variables on lipase production in solid-state fermentation
Three-dimensional curves presented in [Figure 3]a–c show the effects of independent SSF variables on lipase production in SSF as represented by FAME yield. [Figure 3]a represents the contour plot and response surface curves of the effect of the incubation temperature and moisture content on FAME yield, where the incubation period was constant. Initially, FAME yield had raised with the increase in both the incubation temperature and moisture content. However, this rising profile was stopped and converted to a descending one at incubation temperatures higher than 28°C and moisture content higher than 40%.
[Figure 3]b shows the significant interaction between the moisture content and incubation period and response where the incubation temperature was constant. By increasing the incubation period to more than 3 days and moisture content to more than 40 or 45%, there was an observed decline in FAME yield. [Figure 3]c showed the interaction between the independent variables (incubation temperature and incubation period) and response, where the moisture content is constant. These variables had a slightly positive effect on FAME yield. Maximum FAME yield ∼60% was produced upon increasing the incubation period up to 3 days, with an increase in the incubation temperature between 28 and 30°C.
The contour plot and response surface curves of the effect of each two independent variables and the response (FAME yield) ([Figure 3]a–c) suggested that FAME yield declined at incubation temperature above 28°C, incubation period higher than 3 days, and moisture content more than 40%. This means that the temperature of 31°C was not suitable for the growth of 3G and consequently bad lipase production, leading to the low yield of FAME. Moreover, heat creates water condensation which returned to the fermented solid causing heterogeneity in the solid substrate. Mohseni et al.  reported that maximum lipase activity by A. niger from agricultural residues was achieved after 4 days of inoculation. However, Mahadik et al.  has achieved highly active lipase by A. niger strain after 5 days of incubation. Beyond this period (3 days), lower enzyme production was obtained probably owing to the decrease of the required nutrient substance for the growth of the microorganism. Moreover, at high moisture content (50 and 60%), the FAME yield decreased, which could be attributed to the reduction of lipase production ability in SSF as a result of oxygen transfer limitations  or reduction of surface to volume ratio of solid material  caused by agglomeration of medium particles in SSF at high moisture content. In contrast to our results, the optimum initial moisture content for lipase production from wheat Rawa by Aspergillus sp. was 80% .
The verification experiment was done by five random set of experiments using the predicted optimal conditions for 3G lipase production in SSF, whereas the basal SSF medium was used as a control ([Table 9]). According to these results, there was a good agreement between the experimental and predicted values. The maximum FAME yield of 65.55% was obtained at moisture content of 40%, temperature of 28°C, and after 3 days of incubation period. The two steps of optimization resulted in a formula of the following fermentation conditions: 10 g WB, black cumin meal 6%, molasses 1%, adjusted at initial pH 8 with moisture content of 40%, inoculated by 2 disc/10 g WB, and incubated at 28°C for 3 days. Under SSF with these optimized conditions, FAME yield by 3G fermented solid increased by ∼58% (1.58 times) compared with the basal medium (41.46%). These results were close to those obtained by Vasiee et al.  who optimized the cultivation conditions for lipase production from rice flour through PB design and RSM by Bacillus cereus, which was 1.83 times more than the nonoptimal conditions.
|Table 9 Validation of central composite design for 3G lipase production in solid-state fermentation|
Click here to view
| Conclusion|| |
The application of dry fermented solids containing naturally immobilized enzymes as catalysts in synthesis reactions is one of the biotechnological interests in the field of biotechnology. So, A. tamarii NDA03a mutant G (3G) fermented solid containing lipase used as a biocatalyst for biodiesel production by transesterification of WFO.
An optimization study for the production of 3G lipase by SSF was undergone using PB design and RSM. The obtained results ascertained the success of RSM as an efficient technique to optimize 3G lipase production in SSF and consequently the ability of application of obtained dried fermented solid for biodiesel production. Incubation temperature of 28°C, moisture content of 40%, and incubation period of 3 days were the optimum conditions, which increased lipase production in SSF by 1.58 times as compared with the basal medium (41.46%) as indicated by FAME yield. The ANOVA results showed that the most significant variables affected the FAME yield were the interaction between the incubation period and incubation temperature and the square of the incubation period showing P value of 0.0169 and 0.0187, respectively. A good agreement between the experimental (65.55%) and predicted (65.03%) values was detected. A suitable coefficient of determination (R2=0.87) clearly showed that the model was highly significant and sufficient to describe the correlation between the FAME yield and the tested variables.
This work was supported by the National Research Center of Egypt under grant (2016–2019) (project no. 11050101).
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Yaakob Z, Mohammad M, Alherbawi M, Alam Z, Sopian K. Overview of the production of biodiesel from waste cooking oil. Renew Sust Energ Rev 2013; 18:184–193.
Narwal SK, Gupta R. Biodiesel production by transesterification using immobilized lipase. Biotechnol Lett 2013; 35:479–490.
Fjerbaek L, Christensen KV, Norddahl B. A review of the current state of biodiesel production using enzymatic transesterification. Biotechnol Bioeng 2009; 102:1298–1395.
Chen G, Ying M, Li W. Enzymatic conversion of waste cooking oils into alternative fuel-biodiesel. Appl Biochem Biotechnol 2006; 132:911–921.
Hashemizadeh SN, Tavakoli O, Tabandeh F, Karkhane AA, Forghanipour ZA. A comparative study of immobilized fermented solid and commercial lipase as a biocatalyst for biodiesel production from soybean oil. Sweden: World Renewable Energy Congress; 2011. 311–318
Botton V, Piovan L, Meier HF, Mitchell DA, Cordova J, Krieger N. Optimization of biodiesel synthesis by esterification using a fermented solid produced by Rhizopus microsporuson
sugarcane bagasse. Bioprocess Biosyst Eng 2018; 41:573–583.
Saxena RK, Davidson WS, Sheoran A, Giri B. Purification and characterization on an alkaline thermostable lipase from Aspergillus carneus
. Process Biochem 2003; 39:239–247.
Kaushik R, Ruchi GM, Pritesh G, Saurabh S, Luciano S, Parmar VS, Saxena RK. Optimization of lipase production from Aspergillus terreus
by response surface methodology and its potential for synthesis of partial glycerides under solvent free conditions. Indian J Microbiol 2010; 50:456–462.
Mahadik ND, Puntambekar US, Bastawde KB, Khire JM, Gokhale DV. Production of acidic lipase by Aspergillus niger
in solid state fermentation. Process Biochem 2002; 38:715–721.
Gutarra MLE, Godoy MG, Maugeri F, Rodrigues MI, Denise MGF, Castilho LR. Production of an acidic and thermostable lipase of the mesophilic fungus Penicillium simplicissimum
by solid-state fermentation. Bioresour Technol 2009; 100:5249–5254.
Colla LM, Rizzardi J, Pinto MH, Reinehr CO, Bertolin TE, Costa JAV. Simultaneous production of lipases and biosurfactants by submerged and solid-state bioprocesses. Biores Technol 2010; 101:8308–8314.
dos Santos RR, Macedo MLN, Damaso MCT, Passos J, da Silva L, Santos LO. Lipase production by Aspergillus niger
11T53A14 in wheat bran using experimental design methodology. J Food Nutr Res 2014; 2:659–663.
Kareem SO, Adio OQ, Osho MB, Banjo TT, Omeike SO. Optimization of biodiesel production from spent cooking oil by fungal lipase using response surface methodology. Niger J Biotechnol 2018; 35:25–33.
Mumtaz MW, Adnan A, Anwar F, Hamid M, Muhammad AR, Farooq A, Umer R. Response surface methodology: an emphatic tool for optimized biodiesel production using rice bran and sunflower oils. Energies 2012; 5:3307–3328.
Montgomery DC. Design and analysis of experiments: response surface method and designs. Hoboken, NJ, USA: John Wiley & Sons; 2005.
Aguieiras ECG, de Barros DSN, Fernandez-Lafuente R, Freire DMG. Production of lipases in cottonseed meal and application of the fermented solid as biocatalyst in esterification and transesterificationreactions. Renew Energy 2019; 130:574–581.
Todeschini JKP, Aguieiras ECG, Castro AM, Langone MAP, Freire DMG, Rodrigues RC. Synthesis of butyl esters via ultrasound-assisted transesterification of macauba (Acrocomia aculeata
) acid oil using a biomass-derived fermented solid as biocatalyst. J Mol Cataly B Enzyme 2016; 133:213–219.
Elhussiny NI, Khattab AA, El-Refai HA, Mohamed SS, Shetaia YM, Amin HA. Assessment of waste frying oil transesterification capacities of local isolated Asperigilli
species and mutants. Mycoscience 2020; 61:136–144.
Warcup JH. The soil-plate method for isolation of fungi from soil. Nature 1950; 166:117–118.
Ustun G, Kent L, Cekin N, Civelekoglu H. Investigation of the technological properties of black cumin (Nigella sativa
) seed oil. J Am Oil Chem Soc 1990; 67:958–960.
AOAC. Official methods of analysis association of official analytical chemists. 17th ed. Washington, D.C: AOAC International; 2000.
Kuepethkaew S, Sangkharak K, Benjakul S, Klomklao S. Optimized synthesis of biodiesel using lipase from Pacific white shrimp (Litopenaeus vannamei
) hepatopancreas. Renew Energy 2017; 104:139–147.
Rattanaphra D, Harvey AP, Thanapimmetha A, Srinophakun P. Kinetic of myristic acid esterification with methanol in the presence of triglycerides over sulfated zirconia. Renew Energy 2011; 36:2679–2686.
Ali CH, Qureshi AS, Mbadinga SM, Liu JF, Yang SZ, Mu BZ. Biodiesel production from waste cooking oil using onsite produced purified lipase from Pseudomonas aeruginosa FW_SH-1: central composite design approach. Renew Energy 2017; 109:93–100.
Salihu A, Alam MZ, AbdulKarim MI, Salleh HM. Lipase production: an insight in the utilization of renewable agricultural residues. Resour Recov Conserv 2012; 58:36–44.
Oliveira F, Souza CE, Peclat VROL, Salgado JM, Ribeiro BD, Coelho MAZ et al.
Optimization of lipase production by Aspergillus ibericus
from oil cakes and its application in esterification reactions. Food Bioprod Process 2017; 102:268–277.
Halldorsson A, Kristinsson B, Gudmundur GH Lipase selectivity toward fatty acids commonly found in fish oil. Eur J Lipid Sci Technol 2004; 106:79–87.
Thilakarathna RCN, Madhusankha GDMP, Navaratne SB. Determination of composition of fatty acid profile of Ethiopian and Indian black cumin oil (Nigella sativa
). Int J Food Sci Nutr 2018; 3:1–3.
Diwakar BT, Dutta PK, Lokesh BR, Naidu KA. Physicochemical properties of garden cress (Lepidium sativum
L.) seed oil. J Am Oil Chem Soc 2010; 87:539–548.
Ros E, Mataix J. Fatty acid composition of nuts − implications for cardiovascular health. Br J Nutr 2006; 96:29–35.
Mitchell D, Berovic M, Krieger N. Overview of solid state bioprocessing. Biotechnol Annu Rev 2002; 8:183–225.
Nema A, Patnala SH, Mandari V, Kota S, Santhosh KD. Production and optimization of lipase using Aspergillus niger
MTCC 872 by solid-state fermentation. Bull Natl Res Centre 2019; 43:82–89.
Helal SE, Hemmat MA, Khadiga AA, Mervat GH, Mahmoud MA. Evaluation of factors affecting the fungal lipase producing using one factor at a time approach and response surface methodology. Egypt J Microbiol 2017; 52:1–16.
Melissa LEG, Elisa DCC, Leda RC, Denise MGF, Geraldo LSJR. Lipase production by solid-state fermentation. Cultivation conditions and operation of tray and packed-bed bioreactors. Appl Biochem Biotechnol 2005; 121:105–116.
Vasiee A, Behbahani A, Yazdi FT. Optimization of the production conditions of the lipase produced by Bacillus cereus
from rice flour through Plackett-Burman Design (PBD) and response surface methodology (RSM). Microb Pathog 2016; 101:36–43.
Colla LM, Primaz AL, Silvia B, Raquel AL, de Lima MI, Christian OR et al.
Surface response methodology for the optimization of lipase production under submerged fermentation by filamentous fungi. Braz J Microbiol 2016; 47:461–467.
Ebrahimi S, Ghasem DN, Fatemeh A. Transesterification of waste cooking sunflower oil by porcine pancreas lipase using response surface methodology for biodiesel production. Appl Food Biotechnol 2017; 4:203–210.
Gupta J, Madhu A, Dalai AK. Optimization of biodiesel production from mixture of edible and non edible vegetable oils. Biocatal Agric Biotechnol 2016; 8:112–120.
Mohseni S, Ghasem DN, Zahra V, Soleiman M. Solid state fermentation of agricultural residues for lipase production in a tray-bioreactor. World Appl Sci J 2012; 16:1034–1039.
Manan MA, Webb C. Design aspects of solid state fermentation as applied to microbial bioprocessing. J Appl Biotechnol Bioeng 2017; 4:511–532.
Adinarayana K, Raju KVVSNB, Zargar MI, Devi RB, Lakshmi PJ, Ellaiah P. Optimization of process parameters for production of lipase in solid-state fermentation by newly isolated Aspergillus species. Indian J Biotechnol 2004; 3:65–69.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]