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Process optimization and kinetic analysis of malic acid production from crude glycerol using Aspergillus niger
Bioresource Technology 281 (2019) 18–25
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Process optimization and kinetic analysis of malic acid production from crude glycerol using Aspergillus niger
T
⁎
J. Iyyappana, B. Bharathirajaa, , G. Baskarb, E. Kamalanabana a b
Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India Department of Biotechnology, St. Joseph’s College of Engineering, Chennai 600119, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Aspergillus niger Crude glycerol Optimization Malic acid Kinetic models
In the present work, optimization of crude glycerol fermentation to produce malic acid by using Aspergillus niger was investigated using response surface methodology and artificial neural network. Kinetic investigation of bioconversion of crude glycerol into malic acid using Aspergillus niger was studied using Monod, Mosser, and Haldane-Andrew models. Crude glycerol concentration, initial pH and yeast extract concentration were found to be significant compounds affecting malic acid production by Aspergillus niger. Both dry cell weight and malic acid titre were found decreased with increase in crude glycerol concentration. Haldane-Andrew model gave the best fit for the production of malic acid from crude glycerol with µmax of 0.1542 h−1. The maximum malic acid production obtained under optimum conditions was 92.64 + 1.54 g/L after 192 h from crude glycerol using Aspergillus niger.
1. Introduction Bioconversion of crude glycerol derived from biodiesel production into commercially important products have greater opportunity and incidentally, reduction in the usage of fossil fuels could be accomplished through the engagement of increased biodiesel production (Vivek et al., 2017). Crude glycerol generation from biodiesel industry has become a major concern for biodiesel industry since about 10 kg of crude glycerol is accumulated as waste for every 90 kg of biodiesel produced. Many researchers focused on proper utilization of crude glycerol by using appropriate microorganisms. However, the presence of impurities in the crude glycerol is the one the major difficulties associated with the proper utilization of crude glycerol by the microorganisms (Dikshit et al., 2018). Extensive research pertaining consumption of crude glycerol has to be engaged. Malic acid is one of the commercially platform chemicals used in food industries as food enhancers. Petroleum derived maleic acid is the raw material for the commercial malic acid production. The storage ability of malic acid was high due to less hygroscopic (Dai et al., 2018). Moreover many researchers are interested towards the microbial malic acid production due to aware of the estimated demand with the industrial importance. Microbial production of malic acid is the most effective and eco-friendly (Dai et al., 2018). Cost-effective direct malic acid production using fermentation is still an important difficulty (Chen
⁎
et al., 2019). By considering the favourable economical aspects, employment of low cost substrates is the foremost task for the huge malic acid production through fermentation. Recently microorganisms like Rhizopus delemar (Li et al., 2014), Aspergillus oryzae (Knuf et al., 2014), Saccharomyces cerevisiae (Chen et al., 2017) and Ustilago trichophora (Zambanini et al., 2017) were reported to be malic acid producers. Previously, Aspergillus niger were reported to produce malic acid from crude glycerol through adaptive evolution (Iyyappan et al., 2018a) and morphological control (Iyyappan et al., 2018b). Fermentation process relies on the excellence of nutrient compounds that are used for the production of various metabolites. Media formulation is an essential step, used for the prediction of the component that is required for attaining sufficient biomass and high yield of metabolite production (Vivek et al., 2018). The energy required for microorganisms for the production of organic acids can be supplied through various carbon sources. Supply of organic and inorganic nitrogen sources with the production medium ensures the microorganism growth (Wang et al., 2016). The important step in fermentation for the desired productivity of metabolites is optimization. Classical method and statistical method are the most commonly used methods of media optimization. Even though the classical method of optimization is a time consuming one, it is a basic step used for the identification of parameter levels. The most
Corresponding author. E-mail address: [email protected] (B. Bharathiraja).
https://doi.org/10.1016/j.biortech.2019.02.067 Received 9 January 2019; Received in revised form 11 February 2019; Accepted 12 February 2019 Available online 16 February 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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2.4. Process optimization of malic acid production
important variables that affect the productivity and microbial growth can be screened by using Plackett & Burman method (Plackett & Burman 1946). Further, response surface methodology (RSM) enables statistical prediction of the optimized level of variables with optimized productivity. Central composite design (CCD) is one of the RSM methods, used for the amplified production of commercially important enzymes, antibiotics and organic acids (Latha et al., 2017). Recently, the combination of response surface methodology with artificial neural network received greater interest among the researchers (WitekKrowiak et al., 2014). Application of optimization methods does not end with the fermentative production other potential disciplines including the production of biofuels (Baskar et al., 2018), extraction of valuable compounds from various sources (Shirzad et al., 2017) and biosorption of pollutants (Sen et al., 2018). Batch fermentation contains a limited amount of nutrients and maximum productivity of metabolites can be achieved at maximum microbial growth rate. Organic acids like succinic acid were successfully produced from substrates using batch fermentation. The relationship between biomass growth and product formation can be studied using batch fermentation (Carvalho et al., 2016). Influence of various substrate concentrations on microbial growth can be extensively studied by using batch fermentation. Commercialization of the model type has been always a critical process and inhibition of substrate concentration is an important phenomenon that affects development of the product establishment. Similarly, certain products showed an inhibitory effect on the microbial growth (Dikshit and Moholkar, 2016). Fermentation of ethanol from sugar using yeast is a good example. Even though continuous recovery of product from the fermentation broth enables to reduce product inhibition, detailed information about how the inhibition takes place is necessary (Ko et al., 2016). This can be analyzed with the help of various substrates and product inhibition kinetic models. In this present study, optimization of malic acid production was investigated from crude glycerol using response surface methodology and kinetic model analysis such as substrate inhibition kinetics and product inhibition kinetics were studied using morphologically controlled A. niger PJR1.
The design of experiment was used for the statistical optimization of process conditions to obtain high yield of malic acid from crude glycerol using A. niger PJR1. Plackett-Burman Design was particularly used to screen the fermentation variables that had significant effect on malic acid production. 2.4.1. Plackett-Burman design Plackett-Burman Design is a beneficial and extensively applied for screening the process parameters that has significant effects on organic acid production (Li et al., 2018). Totally, 20 experimental runs with the different combinations of independent variables were created using MINITAB Software version 18.1. All the experiments were carried out in triplicate. The mean of the produced malic acid was regarded as a response. The selected seven parameters with two different levels (−1 , +1) for the experiment were initial pH (X1) (5,7), temperature (°C) (X2) (20, 30), inoculum density × 104/mL (X3) (2.5, 3.5), crude glycerol concentration (g/L) (X4) (155, 165), yeast extract concentration (g/L) (X5) (1, 2), KH2PO4 concentration (g/L) (X6) (1, 2) and MgSO4 concentration (g/L) (X7) (0.1, 0.3). 2.4.2. Optimization using central composite design Central composite design (CCD) was performed to demonstrate the response surface nature and to identify the optimum value of the significant variables (Jose et al., 2018). Three variables, namely initial pH, crude glycerol concentration and yeast extract concentration were investigated in this model by using identified significant variables. These factors were studied at three different levels (-1, 0, +1) for the experiment were initial pH (A) (5, 6, 7), crude glycerol concentration (g/ L) (B) (155, 160, 165) and yeast extract concentration (g/L) (C) (1, 1.5, 2). Totally, 20 experimental runs with different combinations of significant variables were executed. All the experiments were performed in triplicate. The experimentally observed values were fitted to the following second order polynomial equation:
Y = βo + β1 A + β2 B + β3 C + β11 A2 + β22 B2 + β33 C 2 + β12 AB + β13 AC
2. Materials and methods
+ β23 BC 2.1. Materials
where, Y is the response (Malic acid titer), A, B and C are the independent variable namely initial pH, concentration of the crude glycerol (g/L) and yeast extract (g/L), respectively. β1, β2 and β3 are the linear coefficients, βo is the regression coefficient, β11, β22 and β33 are the quadratic coefficients and β12, β13 and β23 are the interaction coefficients. The developed second order polynomial model was analyzed for calculating ANOVA. R2 is the coefficient of determination that reflected the regression model quality. The statistical analysis of the developed model was performed using MINITAB software v18.1.
Transesterification of waste frying oils was performed according the method prescribed by Bharathiraja et al., (2014). The generated crude glycerol was characterized by using GC/MS analysis and the crude glycerol contained (%w/v): methanol (2.05 + 0.12%), glycerol (67.05 + 2.5%), methyl oleate (1.23 + 0.17%), water (27.54 + 2.98%), sodium oleate (1.05 + 0.23%) and sodium chloride (1.15 + 0.08%). 2.2. Growth and maintenance of Aspergillus niger
2.4.3. Artificial neural network A nonlinear mapping was generated using Artificial neural network (ANN) between input variables (initial pH, crude glycerol concentration and yeast extract concentration) and malic acid production. The experimental data used for CCD had been simulated using ANN. Recently, the combination of RSM with ANN had attracted a well-defined approach for optimizing the product (Witek-Krowiak et al., 2014). Radial basis function (RBF) network was employed for the optimization of malic acid production and contained a single hidden layer. The linear output layer is connected to the locally tuned units.
The strain of Aspergillus niger PJR1 was used in this study and was cultured on potato dextrose agar medium (Iyyappan et al., 2018a). The incubation temperature was maintained at 25 °C for 168 h. Further, the genetic stability of A. niger PJR1 were examined (Zhang et al., 2018) and was found to be admirable. 2.3. Seed culture and fermentation medium compositions Seed culture medium and seed culture conditions were maintained according to the method described by Iyyappan et al., (2018b). Fermentation medium was composed of crude glycerol 160 g/L, yeast extract 1.5 g/L, KH2PO4 1.5 g/L, CaCl2·H2O 1 mg/L and MgSO4·7H2O 0.2 g/L. The initial pH of 6.0 + 0.2 was adjusted using NaHCO3 8% (w/ v).
2.4.4. Experimental validation of predicted conditions The result of the central composite design was validated by conducting experiments based on the predicted conditions obtained through CCD (Li et al., 2018). Process parameters with their optimum level were obtained. All the experiments were performed four times. 19
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2.5. Kinetic model analysis of fungal growth and product inhibition
2017).
2.5.1. Growth kinetic models The rate of organic acid production depends on the rate of microbial growth and utilization of carbon source. Various mathematical models had been utilized to find the correlation between substrate utilization and microbial growth. Substrate inhibition or product inhibition can terminate the microbial growth. It is due to the phenomenon of depletion of the medium components or formation of products that are toxic to microbial growth (Dikshit et al., 2017). If the accumulation of toxic product is occurred in the fermentation broth, the microbial growth will reduce and even will result in termination of microbial growth. Hence it is essential to study the kinetic behavior of the microorganism. Moreover, carbon source involved in this study is glycerol. Substrate utilization kinetics can be investigated through growth kinetic models. The Monod model, Moser model and Haldane–Andrews model were used to study the microbial growth kinetics.
2.5.2.1. Ghose model. Ghose model is one of the linear product inhibition model and which relates microbial specific growth rate with the effect of product concentration and threshold concentration of the product (Claret et al., 1993). The influence of product concentration on microbial growth is described by Ghose model Eq. (5),
μ=
2.5.2.2. Luong model. Luong model is the modified form of Ghosh model (Luong 1987) and correlates the product concentration and microbial specific growth rate with an empirical constant for product concentration terms.
μ=
μ=
The estimation of produced malic acid was made by performing High-performance Liquid chromatography (HPLC) (BioRad). HPX-87H column was operated with 5 mM sulphuric acid as mobile phase at 30 °C with a flow rate of 0.6 mL min−1 and detected with a UV–vis diode array detector at 230 nm. The known concentration of malic acid was prepared and analyzed for the preparation of standard curves by plotting known concentration of malic acid versus peak area versus to determine the unknown concentration of malic acid.
(3)
2.5.1.3. Haldane–Andrews model. Haldane–Andrews kinetic model was used to study substrate inhibition kinetics of crude glycerol on microbial growth (Dikshit and Moholkar, 2016). Initially, this model was proposed by Andrews (1968) and was described by the following Eq. (4),
3. Results and discussion
μmax S S2 KIS
(7)
2.6. Analytical methods
where n is the degree of inhibition.
S + KS +
μmax S −Po e Kip S + KS
where Kip is the product inhibition constant. The acquired experimental data was fitted to curve fitting option and the numerical values of these models were obtained by using MATLAB R2017a.
2.5.1.2. Moser model. Moser model is one of the modified forms of Model model, in which the role of substrate concentration on the inhibition of microbial growth is considered the power term (Rohit et al., 2018). Moser model is described by the following Eq. (3),
μ=
(6)
2.5.2.3. Aiba model. Aiba et al., (1968) developed a model relating the effect of product inhibition and microbial specific growth and mostly used for studies of the product inhibition phenomenon. This model includes another term known as product inhibition constant.
(2)
μmax S n KS + Sn
μmax S (1 − (Po/ Pm)α ) KS + S
where α is an empirical constant.
where, µ is the specific growth rate (h−1), µmax is the maximum specific growth rate (h−1), KS is the substrate saturation constant and S is the concentration of the substrate (g/L). The important assumption in Monod model is that the microbial specific growth rate is directly proportion to substrate concentration. Hence the phenomenon of microbial growth substrate inhibition due to higher substrate concentrations was not illustrated by this model.
μ=
(5)
where Pm is the threshold product concentration that is beyond which no biological activities occurred and Po is the initial product concentration in the fermentation medium (g/L).
2.5.1.1. Monod model. Monod proposed the growth kinetic model relating the specific growth rate with the growth limiting substrate concentration (Monod 1949). The relationship of Monod model is described by the following Eq. (2),
μ S μ = max KS + S
μmax S (1 − Po/ Pm) KS + S
3.1. Plackett–Burman experimental design (4)
Totally seven variables were selected for screening using PlackettBurman design. It exhibited totally 20 experimental runs for malic acid production with the combination of the two levels of each variable as shown in Table 1. The difference between the mean of the measurements made at the two levels of the variables resulted in the prediction of main effect of each parameter on malic acid production. Malic acid production obtained using PBD experiments showed variations from 61.74 to 74.32 g/L of response. The generated PBD was exposed to perform the statistical analysis by using MINITAB 18.1 and the calculation of the independent effect, p-value with confidence level and tvalue were performed. Table 2 summarizes the estimated coefficients, confidence level and t-value. It was clear that crude glycerol concentration (g/L) and initial pH showed highly significant probability value (p < 0.001) and yeast extract concentration (g/L) showed significant probability value (p < 0.05). Accordingly, initial pH was an important significant parameter on malic acid production, crude
where KIS is the substrate inhibition constant. 2.5.2. Product inhibition kinetic models The effect of product concentration on the growth of A. niger PJR1 and glycerol fermentation were investigated by changing the initial concentration of malic acid ranging from 0 g/L to 120 g/L in the fermentation broth at a fixed concentration of crude glycerol (160 g/L). Before the fermentation, malic acid (commercial) was additionally included to the fermentation medium. Concurrently, without the addition of malic acid in the fermentation medium was used as a control. Fermentation was performed and samples were withdrawn every 24 h. Amount of glycerol utilized and malic acid produced were analyzed. The observed values of glycerol utilization and malic acid production were fitted to various product inhibition models like Ghosh model, Luong model and Aiba model were used in this work (Dikshit et al., 20
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and crude glycerol concentration of 155 g/L as shown in Table 3. Furthermore, the maximum malic acid titer of 91.14 g/L was observed with the initial pH at 6, crude glycerol concentration of 160 g/L and yeast extract concentration of 1.5 g/L. The predicted malic acid production from crude glycerol by using A. niger PJR1 was fitted to developed regression model. Table 4 summarizes the ANOVA result for the response. As the calculated F value and p-value were 50.90 and < 0.001 (very low), the regression model was highly significant to represent the dependence of malic acid production on components studied. It was clear that all the three variables were significant. If the p-value was < 0.05, the respective terms were said to be significant. Whereas, p-value greater than 0.05 represented the respective terms are insignificant. The p < 0.05 for A, B, C, A2, B2 and C2 revealed that the linear and square effect of the variables were significant. But the interaction effect between initial pH, crude glycerol concentration and yeast extract concentration were not found to be significant. The p > 0.05 for AB, BC and AC revealed that the interaction effect of the variables were not significant. The coefficient of determination (R2) value of the model defines the measure of variability between the experimental variables and response values that are observed. The R2 value (0.9786) in the study was very close to 1, the model predicts the better response. The reliability and precision of the experiments could be evaluated through the coefficient of variation value. The adjusted R2 value was 0.9594, which was close to the R2 value and representing the maximal correlation among the experimental and predicted values. The probability value of lack of fit was insignificant, which illustrated that the quadratic model was valid for the present study. The equation pertaining the variables with coded units was represented in Eq. (8).
Table 1 Plackett-Burman experimental design for factors affecting malic acid production from crude glycerol using A. niger PJR1. Std. order
X1
X2
X3
X4
X5
X6
X7
Malic acid titer (Y) (g/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
7 7 5 5 7 7 7 7 5 7 5 7 5 5 5 5 7 7 5 5
20 30 30 20 20 30 30 30 30 20 30 20 30 20 20 20 20 30 30 20
3.5 2.5 3.5 3.5 2.5 2.5 3.5 3.5 3.5 3.5 2.5 3.5 2.5 3.5 2.5 2.5 2.5 2.5 3.5 2.5
165 165 155 165 165 155 155 165 165 165 165 155 165 155 165 155 155 155 155 155
1 2 2 1 2 2 1 1 2 2 2 2 1 2 1 2 1 1 1 1
1 1 2 2 1 2 2 1 1 2 2 2 2 1 2 1 2 1 1 1
0.1 0.1 0.1 0.3 0.3 0.1 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.1 0.3 0.1 0.3 0.1 0.1
71.12 70.24 61.54 62.27 71.15 74.25 77.25 71.18 61.87 70.23 61.74 74.32 62.21 65.31 62.34 65.93 76.94 76.41 66.34 66.17
Table 2 Estimated coefficients, calculated t value, p value and confidence level as per PB Experimental design for production of malic acid. Variable
Main Effect
Estimated co-efficient
t-value
p-value
Constant X1 X2 X3 X4 X5 X6 X7
– 9.737 −0.275 −0.595 −4.011 −1.565 −0.263 0.673
68.441 4.869 −0.138 −0.297 −2.005 −0.783 −0.132 0.337
282.15 20.07 −0.57 −1.23 −8.27 −3.23 −0.54 1.39
< 0.001 < 0.001 0.581 0.244 < 0.001 0.007 0.598 0.191
Y = 90.622 + 3.312A + 1.756B + 1.373C + 0.433AB − 0.377AC − 0.285BC − 7.741A2 − 2.851B2 − 2.836C 2
(8)
where Y is the malic acid titer (g/L), A is the initial pH, B is the crude glycerol concentration (g/L) and C is the concentration of yeast extract (g/L). The results of predicted malic acid titer using ANN were shown in Table 3. Least square optimization was performed by the Gauss-Newton technique for the model coefficients. The fitting of three variables had hidden notes of seven with 0.75 as the function of Gaussian basis. The fitted mean squared error percentage was found to be < 0.1%. The value of percentage of normalized mean squared error was found to be < 0.04%. It represented the in-significant stage of error of the experimental data. Usually, the production of organic acid production was depended on the initial pH of the production medium (Bohlmann et al., 1998). The increased production of organic acid was attained at the optimum pH level (Rymowicz and Lenart, 2003). Whereas, the carbon source was reported to be a highly significant variable for organic acid production using A. niger (Bahaloo-Horeh & Mousavi 2017). Initial pH, crude glycerol concentration and yeast extract concentration were found to be the most significant variable. The RSM regression model in Eq. (8) was solved to predict the optimum value of the variables using MINITAB 18.1. The predicted optimum value of variables was initial pH at 6.21, crude glycerol concentration of 161.56 g/L and yeast extract concentration of 1.6 g/L. Investigation of the interactions between the selected variables could result in the detailed analysis of the optimization process. A factor can correlate either with one variable or all the other variables by making the possibility of existence through a huge number of interactions. The response surface plots were generated to identify the interaction effect of these variables and to find the optimal value of each variable for achieving the maximum malic acid production. The response surface plot (Fig. 1a) represented the interaction effect of initial pH and crude glycerol concentration on malic acid production
glycerol was served as a carbon source and yeast extract was served as a nitrogen source. Initial pH, crude glycerol concentration (g/L) and yeast extract concentration (g/L) were identified as significant variables that affecting the production of malic acid and further considered for the optimization using three-level CCD for maximum production of malic acid. The R2 value, adjusted R2 value and predicted R2 value were found to be 0.9759, 0.9618 and 0.9330, respectively. The crude glycerol obtained from biodiesel production contained glycerol served as a carbon source for 1,3-dihyroxyacetone production using Gluconobacter oxydans (Dikshit et al., 2017). The microbial growth and malic acid production by A. niger was affected by the concentration of carbon source and initial pH of the medium (BahalooHoreh & Mousavi 2017). Similarly, nitrogen source was reported to be an important parameter for organic acid production by A. niger (Bari et al., 2009; Ozdal & Kurbanoglu 2018). 3.2. Optimization of fermentation variables for malic acid production using central composite design and artificial neural network The central composite design experiment was designed using the MINITAB 18.1. The production of malic acid was executed according to Table 3. The student’s t-test and F-test were performed using MINITAB 18.1 for the experimental malic acid production. The response data were analyzed in order to study the interactions between variables such as initial pH, yeast extract concentration and crude glycerol concentration by using central composite design. The effect of the generated quadratic model was determined and statistics like R2 value, lack of fit and F-values were calculated. The lowest malic acid titer value was 70.23 g/L with initial pH at 5, yeast extract concentration of 1 g/L 21
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Table 3 Three-level CCD in actual unit of factors with experimental and predicted malic acid titer value produced from crude glycerol using A. niger PJR1. Std. Order
Initial pH (A)
Crude glycerol concentration (B) (g/L)
Yeast extract concentration (C) (g/L)
Observed malic acid titer (g/L)
Predicted malic acid titer (CCD) (g/L)
Predicted malic acid titer (ANN) (g/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
5 7 5 7 5 7 5 7 5 7 6 6 6 6 6 6 6 6 6 6
155 155 165 165 155 155 165 165 160 160 155 165 160 160 160 160 160 160 160 160
1 1 1 1 2 2 2 2 1.5 1.5 1.5 1.5 1 2 1.5 1.5 1.5 1.5 1.5 1.5
70.23 76.35 74.87 82.13 74.43 78.45 77.34 83.68 78.23 87.61 88.31 87.31 86.12 89.53 91.14 90.45 90.15 90.74 90.87 90.23
70.52 77.04 73.74 81.98 74.59 79.60 76.67 83.41 79.57 86.19 86.02 89.53 86.41 89.16 90.62 90.62 90.62 90.62 90.62 90.62
70.71 77.41 73.91 81.05 74.08 79.87 76.10 82.97 79.13 85.83 86.64 89.11 86.01 88.93 90.44 90.44 90.44 90.44 90.44 90.44
reduced the production of malic acid. It was observed that the production of malic acid was high at near to the middle level of both yeast extract concentration and initial pH. The response surface plot (Fig. 1e) represents the interaction effect of crude glycerol concentration and yeast extract concentration on malic acid production by keeping the third variable initial pH as constant at the middle level. Fig. 1f represents the contour plot obtained and the interaction between yeast extract concentration and crude glycerol concentration was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as crude glycerol concentration and yeast extract concentration. It was observed that increase in yeast extract concentration and crude glycerol concentration near to their middle level increased malic acid titer. Further increase in crude glycerol concentration and yeast extract concentration from the middle level to higher level reduced the production of malic acid. It was observed that the production of malic acid was high at near to the middle level of both crude glycerol concentration and yeast extract concentration.
Table 4 Analysis of variance for quadratic model of response surface for malic acid production. Source
Degrees of freedom
Sum of Squares
Mean Square
F-value
p-value
Regression A B C AB AC BC A2 B2 C2 Residual Error Lack-of-Fit Pure Error Total
9 1 1 1 1 1 1 1 1 1 10 5 5 19
855.761 109.693 30.835 18.851 1.496 1.140 0.650 164.785 22.351 22.117 18.680 17.934 0.746 874.442
95.085 109.693 30.835 18.851 1.496 1.140 0.650 164.785 22.351 22.117 1.868 3.587 0.149
50.90 58.72 16.51 10.09 0.80 0.61 0.35 88.21 11.97 11.84
< 0.001 < 0.001 0.002 0.010 0.392 0.453 0.568 < 0.001 0.006 0.006
24.04
0.072
by keeping the third variable yeast extract concentration as constant at the middle level (1.5 g/L). Fig. 1b represented the contour plot obtained and the interaction between crude glycerol concentration and initial pH was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as initial pH and crude glycerol concentration. It was observed that the increase in crude glycerol concentration and initial pH near to their middle level increased the production of malic acid. Further increase in initial pH and crude glycerol concentration from the middle level to higher level reduced the production of malic acid. It was observed that malic acid production was high at near to the middle level of both initial pH and crude glycerol concentration. The response surface plot (Fig. 1c) represents the interaction effect of yeast extract concentration and initial pH on malic acid production by keeping the third variable crude glycerol concentration as constant at the middle level (160 g/L). Fig. 1d represents the contour plot obtained and the interaction between initial pH and yeast extract concentration was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as initial pH and yeast extract concentration. It was observed that increase in initial pH and yeast extract concentration near to their middle level increased the production of malic acid. Further increase in yeast extract concentration and initial pH from the middle level to higher level
3.3. Experimental validation for the predicted conditions The experiments were repeated four times under optimal conditions to confirm the competence of the equation of the response surface. The observed and predicted values were found to be 92.41 and 91.32, respectively. The standard deviation of observed malic acid titer was found to be 1.74 g/L. The R2 value between observed and predicted was 0.98. The predicted values of malic acid titer from the equation of regression model and ANN were closely related to agree with the observed experimental value. Thus, the validity was proved. 3.4. Kinetics of substrate inhibition on cell growth Growth studies of A. niger PJR1 was performed under the optimum level of the variables that was obtained through statistical design. The evaluated kinetic parameters of three microbial growth models relating to specific growth rate to substrate concentration of crude glycerol were shown in Table 5. Interestingly, elevated specific growth rate was observed with the substrate concentration up to about 160 g/L after which decrease in specific growth rate was detected. The highest specific growth rate was observed at the concentration of crude glycerol about 160 g/L. The data fitted to Monod model Moser model and HaldaneAndrews model for different crude glycerol concentration versus 22
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Fig. 1. 3D response surface plots showing interactions between (a) initial pH and yeast extract concentration, (c) initial pH and yeast extract concentration, (e) crude glycerol concentration and yeast extract concentration, Contour plots showing interactions between (b) initial pH and yeast extract concentration, (d) initial pH and yeast extract concentration, (f) crude glycerol concentration and yeast extract concentration.
Table 5 Kinetic parameters of microbial growth models and product inhibition models. Model
µmax (h−1)
Ks (g/L)
KI,S (g/L)
Pm (g/L)
Kip (g/L)
Correlation coefficient (R2)
Root mean square value
Monod Moser Haldane –Andrews Ghosh Luong Aiba
0.1444 0.1674 0.1542 0.182 0.1454 0.2869
26.11 77.1 89.7 30.95 25.22 120.7
– – 6.219 – – –
– – – 118 114.7 –
– – – – – 66
0.8317 0.8485 0.9284 0.9768 0.9825 0.8771
0.01867 0.01879 0.01292 0.0085 0.00738 0.01955
23
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Fig. 2. Result of fitting of Monod, Moser and Haldane-Andrews model to data of crude glycerol concentration versus specific growth rate.
Fig. 3. The value of α for Luong model was found to be 1.22. The R2 value of Ghose model and Aiba model were found to be 0.9768 and 0.8771, respectively. Among the three models, Aiba model showed the maximum specific growth value with the product inhibition constant value of 66. Luong model showed the maximum specific growth value of 0.1454 h−1 and the estimated threshold product concentration was 114.7 g/L. The calculated empirical constant value found to be 1.22. Ghose model showed the maximum specific growth value of 0.182 h−1 and the estimated threshold concentration of product was about 118 g/ L. Kim et al., (2016) reported that product inhibition kinetic model could be applied to 2,3 – butanediol production by using Klebsiella oxytoca. A detailed knowledge on product inhibition on the microbial growth could assist in the fermentative malic acid production. As a summary, crude glycerol fermentation with the phenomenon of product inhibition could be related to the growth of A. niger PJR1. Liu et al., (2017) reported that about 165 g/L of malic acid was produced using A. oryzae from glucose. About 134 g/L of malic acid was reported to produce using U. trichophora from glycerol (Zambanini et al. 2017). Importantly, West (2015) reported that the fungal strain of A. niger ATCC 12486 produced about 23 g/L of malic acid. However, in this study, malic acid produced from crude glycerol was 92.64 + 1.54 g/L by using morphologically controlled A. niger PJR1 under optimized conditions. The amount of malic acid produced in this study was higher than the value of malic acid produced (69 g/L) from xylose by using A. oryzae (Knuf et al., 2014). This indicated that morphologically controlled A. niger PJR1 has the potential to produce malic
specific growth rate were shown in Fig. 2. The value of n for Moser model was found to be 0.7295. Among the three microbial growth kinetic models, Haldane-Andrew model was found to be best fit with R2 value of 0.9284 with the root mean square value of 0.01292. Under optimized condition, A. niger PJR1 was able to grow on crude glycerol concentration of about 160 g/L. Similarly, when A. niger was cultivated on wheat bran as a substrate, different growth kinetic models were used to depict the growth behavior of A. niger (Augustine et al., 2015). Haldane-Andrew model was reported to be the best model when crude glycerol was exploited for dihydroxyacetone production using Gluconobacter oxydans MTCC 904 (Dikshit and Moholkar, 2016). As a summary, fermentation of glycerol with the phenomenon of substrate inhibition could be related to the growth of A. niger PJR1.
3.5. Kinetics of product inhibition on cell growth In order to examine the effect of malic acid on the growth of A. niger PJR1, the batch fermentations were performed. The fermentation media contained different initial malic acid concentration at optimum conditions. The calculated specific growth rate was plotted against different initial malic acid concentration. The values of estimated parameters of all models were listed in Table 5. Among the three product inhibition model, Luong model was established to be best fit with R2 value of 0.9825 with root mean square value of 0.00738. The data fitted to Ghose model, Luong model and Aiba model for different initial malic acid concentration versus specific growth rate were shown in
Fig. 3. Result of fitting of Ghose, Luong and Aiba model to data of initial malic acid concentration versus specific growth rate. 24
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acid by utilizing crude glycerol without any pre-treatment. The successive employment of optimization methods had improved malic acid production from crude glycerol by using A. niger PJR1. When compared with other substrates, fermentation of crude glycerol to malic acid could serve cost effective method using A. niger. The processes involved in this study could be predominantly suitable for the scale up process of malic acid production. Moreover, A. niger is the cell factory of organic acid production and A. niger PJR1 could be a promising microorganism for the production of malic acid.
Dikshit, P.K., Padhi, S.K., Moholkar, V.S., 2017. Process optimization and analysis of product inhibition kinetics of crude glycerol fermentation for 1,3–dihydroxyacetone production. Bioresour. Technol. 244, 362–370. Dikshit, P.K., Kharmawlong, G.J., Moholkar, V.S., 2018. Investigations in sonication–induced intensification of crude glycerol fermentation to dihydroxyacetone by free and immobilized Glucono bacteroxydans. Bioresour. Technol. 256, 302–311. Iyyappan, J., Baskar, G., Bharathiraja, B., Saravanathamizhan, R., 2018b. Malic acid production from biodiesel derived crude glycerol using morphologically controlled Aspergillus niger in batch fermentation. Bioresour. Technol. 269, 393–399. Iyyappan, J., Bharathiraja, B., Baskar, G., Jayamuthunagai, J., Barathkumar, S., Anna Shiny, R., 2018a. Malic acid production by chemically induced Aspergillus niger MTCC 281 mutant from crude glycerol. Bioresour. Technol. 251, 264–267. Jose, A.A., Anusree, G., Pandey, A., Binod, P., 2018. Production optimization of poly-γglutamic acid by Bacillus amyloliquefaciens under solid state fermentation using soy hull as substrate. Ind. J. Biotechnol. 17, 44–49. Kim, D.K., Park, J.M., Song, H., Chang, Y.K., 2016. Kinetic modeling of substrate and product inhibition for 2,3-butanediol production by Klebsiella oxytoca. Biochem. Eng. J. 114, 94–100. Knuf, C., Nookaew, I., Remmers, I., Khoomrung, S., Brown, S., Berry, A., Nielsen, J., 2014. Physiological characterization of the high malic acid-producing Aspergillus oryzae strain 2103a 68. Appl. Microbiol. Biotechnol. 98, 3517–3527. Ko, J.K., Um, Y., Woo, H.M., Kim, K.H., Lee, S.M., 2016. Ethanol production from lignocellulosic hydrolysates using engineered Saccharomyces cerevisiae harboring xylose isomerase-based pathway. Bioresour. Technol. 209, 290–296. Latha, S., Sivaranjani, G., Dhanasekaran, D., 2017. Response surface methodology: a nonconventional statistical tool to maximize the throughput of Streptomyces species biomass and their bioactive metabolites. Crit. Rev. Microbiol. 43, 567–582. Li, C., Gao, S., Wang, H., Li, X., Ong, K.L., Yang, X., Lin, C.S.K., 2018. Succinic acid production using a glycerol-based medium by an engineered strain of Yarrowia lipolytica: statistical optimization and preliminary economic feasibility study. Biochem. Eng. J. 137, 305–313. Li, X.J., Liu, Y., Yang, Y., Zhang, H., Wang, H.L., Wu, Y., Zhang, M., Sun, T., Cheng, J.S., Wu, X.F., Pan, L.J., Jiang, S.T., Wu, H.W., 2014. High levels of malic acid production by the bioconversion of corn straw hydrolyte using an isolated Rhizopus delemar strain. Biotechnol. Bioproc. Eng. 19, 478–492. Liu, J., Xie, Z.P., Shin, H.D., Li, J.H., Du, G.C., Chen, J., Liu, L., 2017. Rewiring the reductive tricarboxylic acid pathway and L-malate transport pathway of Aspergillus oryzae for overproduction of L-malate. J. Biotechnol. 253, 1–9. Luong, J.M.Y., 1987. Kinetics of ethanol inhibition in alcohol fermentation. Biotechnol. Bioeng. 29, 242–248. Monod, J., 1949. The growth of bacterial cultures. Ann. Rev. Microbiol. 3, 371–394. Ozdal, M., Kurbanoglu, E.B., 2018. Citric acid production by Aspergillus niger from agroindustrial by-products: molasses and chicken feather peptone. Waste Biomass Valorization. https://doi.org/10.1007/s12649-018-0240-y. Plackett, R.L., Burman, J.P., 1946. The design of optimum multifactorial experiments. Biometric 33, 305–325. Rohit, S.G., Jyoti, P.K., Subbi, R.R.T., Naresh, M., Senthilkumar, S., 2018. Kinetic modeling of hyaluronic acid production in palmyra palm (Borassus flabellifer) based medium by Streptococcus zooepidemicus MTCC 3523. Biochem. Eng. J. 137, 284–293. Rymowicz, W., Lenart, D., 2003. Oxalic acid production from lipids by a mutant of Aspergillus niger at different pH. Biotechnol. Lett. 25, 955–958. Sen, S., Nandi, S., Dutta, S., 2018. Application of RSM and ANN for optimization and modeling of biosorption of chromium (VI) using cyanobacterial biomass. Appl. Water Sci. 148 (8). https://doi.org/10.1007/s13201-018-0790-y. Shirzad, H., Niknam, V., Taheri, M., Ebrahimzadeh, H., 2017. Ultrasound-assisted extraction process of phenolic antioxidants from Olive leaves: a nutraceutical study using RSM and LC–ESI–DAD–MS. J. Food Sci. Technol. 54 (8), 2361–2371. Vivek, N., Sindhu, R., Madhavan, A., Anju, A.J., Castro, E., Faraco, V., Pandey, A., Binod, P., 2017. Recent advances in the production of value added chemicals and lipids utilizing biodiesel industry generated crude glycerol as a substrate –m aspects, challenges and possibilities: an overview. Bioresour. Technol. 239, 507–517. Vivek, N., Ashok Pandey, A., Binod, P., 2018. An efficient aqueous two phase systems using dual inorganic electrolytes to separate 1,3-propanediol from the fermented broth. Bioresour. Technol. 254, 239–246. Wang, Y., Song, X., Zhang, Y., Wang, B., Zou, X., 2016. Effects of nitrogen availability on polymalic acid biosynthesis in the yeast-like fungus Aureobasidium pullulans. Microb. Cell Fact. 15, 146. West, T.P., 2015. Fungal biotransformation of crude glycerol into malic acid. Z. Naturforsch. 70 ((5–6)c), 165–167. Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A., Pokomeda, K., 2014. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresour. Technol. 160, 150–160. Zambanini, T., Tehrani, H.H., Geiser, E., Sonntag, K.C., Buescher, M.J., Meurer, G., Wierckx, N., Blank, M.L., 2017. Metabolic engineering of Ustilago trichophora TZ1 for improved malic acid production. Metab. Eng. Commun. 4, 12–21. Zhang, N., Jiang, J.C., Yang, J., Wei, M., Zhao, J., Xu, H., Xie, J.C., Tong, Y.J., Yu, L., 2018. Citric Acid Production from Acorn Starch by Tannin Tolerance Mutant Aspergillus niger AA120. Appl. Biochem. Biotechnol. https://doi.org/10.1007/ s12010-018-2902-4.
4. Conclusion The present study revealed that initial pH, crude glycerol concentration and yeast extract concentration were significant variables involved in the production of malic acid using Aspergillus niger PJR1. The RSM combined ANN exposed that under initial pH at 6.21, crude glycerol concentration of 161.56 g/L and yeast extract concentration of 1.6 g/L the morphologically controlled A. niger PJR 1 produced 92.61 + 1.86 g/L of malic acid. Among the three substrate inhibition kinetic models, Haldane-Andrew model was found to be best fit and among the three product inhibition kinetic models, Luong model gave the best fit. The knowledge on optimization and biokinetic analysis would reduce the cost of malic acid production from crude glycerol. Acknowledgement This study was financially supported by Science and Engineering Research Board (SERB), India (File No. EEQ/2017/000200). The author thank DST-SERB, India for granting financial support for this work. References Aiba, S., Shoda, M., Nagatami, M., 1968. Kinetics of product inhibition in alcohol fermentation. Biotechnol. Bioeng. 10, 845–864. Andrews, J.F., 1968. A mathematical model for the continuous culture of microorganisms utilizing inhibitory substance. Biotechnol. Bioeng. 10, 707–723. Augustine, A., Joseph, I., Raj, P.R., David, N.S., 2015. Growth kinetic profiles of Aspergillus niger S14 a mangrove isolate and Aspergillus oryzae NCIM 1212 in solid state fermentation. Ind. J. Fish. 62, 100–106. Bahaloo-Horeh, N., Mousavi, S.M., 2017. Enhanced recovery of valuable metals from spent lithium-ion batteries through optimization of organic acids produced by Aspergillus niger. Waste Manage. 60, 666–679. Bari, M.N., Alam, M.Z., Muyibi, S.A., Jamal, P., 2009. Improvement of production of citric acid from oil palm empty fruit bunches: optimization of media by statistical experimental designs. Bioresour. Technol. 100, 3113–3120. Baskar, G., Selvakumari, I.A., Aiswarya, R., 2018. Biodiesel production from castor oil using heterogeneous Ni doped ZnO nanocatalyst. Bioresour. Technol. 250, 793–798. Bharathiraja, B., Chakravarthy, M., Ranjith Kumar, R., Yuvaraj, D., Jayamuthunagai, J., Praveen Kumar, R., Palani, S., 2014. Biodiesel production using chemical and biological methods – a review of process, catalyst, acyl acceptor, source and process variables. Renew. Sustainable Energy Rev. 38, 368–382. Bohlmann, J.T., Cameselle, C., Nunez, M.J., Lema, J.M., 1998. Oxalic acid production by Aspergillus niger. Part II. Optimization of fermentation with milk whey as carbon source. Bioproc. Eng. 19 (5), 337–342. Carvalho, M., Roca, C., Reis, M.A.M., 2016. Improving succinic acid production by Actinobacillus succinogenes from raw industrial carob pods. Bioresour. Technol. 218, 491–497. Chen, Z., Liu, G., Zhang, J., Bao, J., 2019. A preliminary study on L-lysine fermentation from lignocellulose feedstock and techno-economic evaluation. Bioresour. Technol. 271, 196–201. Chen, X.L., Wang, Y.C., Dong, X.X., Hu, G.P., Liu, L.M., 2017. Engineering rTCA pathway and C4-dicarboxylate transporter for L-malic acid production. Appl. Microbiol. Biotechnol. 101, 4041–4052. Claret, C., Bories, A., Soucaille, P., 1993. Inhibitory effect of dihydroxyacetone on Gluconobacter oxydans: kinetic aspects and expression by mathematical equations. J. Ind. Microbiol. 11, 105–112. Dai, Z., Zhou, H., Zhang, S., Gu, H., Yang, Q., Zhang, W., Dong, W., Ma, J., Fang, Y., Jiang, M., Xin, F., 2018. Current advance in biological production of malic acid using wild type and metabolic engineered strains. Bioresour. Technol. 258, 345–353. Dikshit, P.K., Moholkar, V.S., 2016. Kinetic analysis of dihydroxyacetone production from crude glycerol by immobilized Gluconobacter oxydans MTCC 904. Bioresour. Technol. 216, 948–957.
25
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Process optimization and kinetic analysis of malic acid production from crude glycerol using Aspergillus niger
T
⁎
J. Iyyappana, B. Bharathirajaa, , G. Baskarb, E. Kamalanabana a b
Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India Department of Biotechnology, St. Joseph’s College of Engineering, Chennai 600119, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Aspergillus niger Crude glycerol Optimization Malic acid Kinetic models
In the present work, optimization of crude glycerol fermentation to produce malic acid by using Aspergillus niger was investigated using response surface methodology and artificial neural network. Kinetic investigation of bioconversion of crude glycerol into malic acid using Aspergillus niger was studied using Monod, Mosser, and Haldane-Andrew models. Crude glycerol concentration, initial pH and yeast extract concentration were found to be significant compounds affecting malic acid production by Aspergillus niger. Both dry cell weight and malic acid titre were found decreased with increase in crude glycerol concentration. Haldane-Andrew model gave the best fit for the production of malic acid from crude glycerol with µmax of 0.1542 h−1. The maximum malic acid production obtained under optimum conditions was 92.64 + 1.54 g/L after 192 h from crude glycerol using Aspergillus niger.
1. Introduction Bioconversion of crude glycerol derived from biodiesel production into commercially important products have greater opportunity and incidentally, reduction in the usage of fossil fuels could be accomplished through the engagement of increased biodiesel production (Vivek et al., 2017). Crude glycerol generation from biodiesel industry has become a major concern for biodiesel industry since about 10 kg of crude glycerol is accumulated as waste for every 90 kg of biodiesel produced. Many researchers focused on proper utilization of crude glycerol by using appropriate microorganisms. However, the presence of impurities in the crude glycerol is the one the major difficulties associated with the proper utilization of crude glycerol by the microorganisms (Dikshit et al., 2018). Extensive research pertaining consumption of crude glycerol has to be engaged. Malic acid is one of the commercially platform chemicals used in food industries as food enhancers. Petroleum derived maleic acid is the raw material for the commercial malic acid production. The storage ability of malic acid was high due to less hygroscopic (Dai et al., 2018). Moreover many researchers are interested towards the microbial malic acid production due to aware of the estimated demand with the industrial importance. Microbial production of malic acid is the most effective and eco-friendly (Dai et al., 2018). Cost-effective direct malic acid production using fermentation is still an important difficulty (Chen
⁎
et al., 2019). By considering the favourable economical aspects, employment of low cost substrates is the foremost task for the huge malic acid production through fermentation. Recently microorganisms like Rhizopus delemar (Li et al., 2014), Aspergillus oryzae (Knuf et al., 2014), Saccharomyces cerevisiae (Chen et al., 2017) and Ustilago trichophora (Zambanini et al., 2017) were reported to be malic acid producers. Previously, Aspergillus niger were reported to produce malic acid from crude glycerol through adaptive evolution (Iyyappan et al., 2018a) and morphological control (Iyyappan et al., 2018b). Fermentation process relies on the excellence of nutrient compounds that are used for the production of various metabolites. Media formulation is an essential step, used for the prediction of the component that is required for attaining sufficient biomass and high yield of metabolite production (Vivek et al., 2018). The energy required for microorganisms for the production of organic acids can be supplied through various carbon sources. Supply of organic and inorganic nitrogen sources with the production medium ensures the microorganism growth (Wang et al., 2016). The important step in fermentation for the desired productivity of metabolites is optimization. Classical method and statistical method are the most commonly used methods of media optimization. Even though the classical method of optimization is a time consuming one, it is a basic step used for the identification of parameter levels. The most
Corresponding author. E-mail address: [email protected] (B. Bharathiraja).
https://doi.org/10.1016/j.biortech.2019.02.067 Received 9 January 2019; Received in revised form 11 February 2019; Accepted 12 February 2019 Available online 16 February 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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2.4. Process optimization of malic acid production
important variables that affect the productivity and microbial growth can be screened by using Plackett & Burman method (Plackett & Burman 1946). Further, response surface methodology (RSM) enables statistical prediction of the optimized level of variables with optimized productivity. Central composite design (CCD) is one of the RSM methods, used for the amplified production of commercially important enzymes, antibiotics and organic acids (Latha et al., 2017). Recently, the combination of response surface methodology with artificial neural network received greater interest among the researchers (WitekKrowiak et al., 2014). Application of optimization methods does not end with the fermentative production other potential disciplines including the production of biofuels (Baskar et al., 2018), extraction of valuable compounds from various sources (Shirzad et al., 2017) and biosorption of pollutants (Sen et al., 2018). Batch fermentation contains a limited amount of nutrients and maximum productivity of metabolites can be achieved at maximum microbial growth rate. Organic acids like succinic acid were successfully produced from substrates using batch fermentation. The relationship between biomass growth and product formation can be studied using batch fermentation (Carvalho et al., 2016). Influence of various substrate concentrations on microbial growth can be extensively studied by using batch fermentation. Commercialization of the model type has been always a critical process and inhibition of substrate concentration is an important phenomenon that affects development of the product establishment. Similarly, certain products showed an inhibitory effect on the microbial growth (Dikshit and Moholkar, 2016). Fermentation of ethanol from sugar using yeast is a good example. Even though continuous recovery of product from the fermentation broth enables to reduce product inhibition, detailed information about how the inhibition takes place is necessary (Ko et al., 2016). This can be analyzed with the help of various substrates and product inhibition kinetic models. In this present study, optimization of malic acid production was investigated from crude glycerol using response surface methodology and kinetic model analysis such as substrate inhibition kinetics and product inhibition kinetics were studied using morphologically controlled A. niger PJR1.
The design of experiment was used for the statistical optimization of process conditions to obtain high yield of malic acid from crude glycerol using A. niger PJR1. Plackett-Burman Design was particularly used to screen the fermentation variables that had significant effect on malic acid production. 2.4.1. Plackett-Burman design Plackett-Burman Design is a beneficial and extensively applied for screening the process parameters that has significant effects on organic acid production (Li et al., 2018). Totally, 20 experimental runs with the different combinations of independent variables were created using MINITAB Software version 18.1. All the experiments were carried out in triplicate. The mean of the produced malic acid was regarded as a response. The selected seven parameters with two different levels (−1 , +1) for the experiment were initial pH (X1) (5,7), temperature (°C) (X2) (20, 30), inoculum density × 104/mL (X3) (2.5, 3.5), crude glycerol concentration (g/L) (X4) (155, 165), yeast extract concentration (g/L) (X5) (1, 2), KH2PO4 concentration (g/L) (X6) (1, 2) and MgSO4 concentration (g/L) (X7) (0.1, 0.3). 2.4.2. Optimization using central composite design Central composite design (CCD) was performed to demonstrate the response surface nature and to identify the optimum value of the significant variables (Jose et al., 2018). Three variables, namely initial pH, crude glycerol concentration and yeast extract concentration were investigated in this model by using identified significant variables. These factors were studied at three different levels (-1, 0, +1) for the experiment were initial pH (A) (5, 6, 7), crude glycerol concentration (g/ L) (B) (155, 160, 165) and yeast extract concentration (g/L) (C) (1, 1.5, 2). Totally, 20 experimental runs with different combinations of significant variables were executed. All the experiments were performed in triplicate. The experimentally observed values were fitted to the following second order polynomial equation:
Y = βo + β1 A + β2 B + β3 C + β11 A2 + β22 B2 + β33 C 2 + β12 AB + β13 AC
2. Materials and methods
+ β23 BC 2.1. Materials
where, Y is the response (Malic acid titer), A, B and C are the independent variable namely initial pH, concentration of the crude glycerol (g/L) and yeast extract (g/L), respectively. β1, β2 and β3 are the linear coefficients, βo is the regression coefficient, β11, β22 and β33 are the quadratic coefficients and β12, β13 and β23 are the interaction coefficients. The developed second order polynomial model was analyzed for calculating ANOVA. R2 is the coefficient of determination that reflected the regression model quality. The statistical analysis of the developed model was performed using MINITAB software v18.1.
Transesterification of waste frying oils was performed according the method prescribed by Bharathiraja et al., (2014). The generated crude glycerol was characterized by using GC/MS analysis and the crude glycerol contained (%w/v): methanol (2.05 + 0.12%), glycerol (67.05 + 2.5%), methyl oleate (1.23 + 0.17%), water (27.54 + 2.98%), sodium oleate (1.05 + 0.23%) and sodium chloride (1.15 + 0.08%). 2.2. Growth and maintenance of Aspergillus niger
2.4.3. Artificial neural network A nonlinear mapping was generated using Artificial neural network (ANN) between input variables (initial pH, crude glycerol concentration and yeast extract concentration) and malic acid production. The experimental data used for CCD had been simulated using ANN. Recently, the combination of RSM with ANN had attracted a well-defined approach for optimizing the product (Witek-Krowiak et al., 2014). Radial basis function (RBF) network was employed for the optimization of malic acid production and contained a single hidden layer. The linear output layer is connected to the locally tuned units.
The strain of Aspergillus niger PJR1 was used in this study and was cultured on potato dextrose agar medium (Iyyappan et al., 2018a). The incubation temperature was maintained at 25 °C for 168 h. Further, the genetic stability of A. niger PJR1 were examined (Zhang et al., 2018) and was found to be admirable. 2.3. Seed culture and fermentation medium compositions Seed culture medium and seed culture conditions were maintained according to the method described by Iyyappan et al., (2018b). Fermentation medium was composed of crude glycerol 160 g/L, yeast extract 1.5 g/L, KH2PO4 1.5 g/L, CaCl2·H2O 1 mg/L and MgSO4·7H2O 0.2 g/L. The initial pH of 6.0 + 0.2 was adjusted using NaHCO3 8% (w/ v).
2.4.4. Experimental validation of predicted conditions The result of the central composite design was validated by conducting experiments based on the predicted conditions obtained through CCD (Li et al., 2018). Process parameters with their optimum level were obtained. All the experiments were performed four times. 19
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2.5. Kinetic model analysis of fungal growth and product inhibition
2017).
2.5.1. Growth kinetic models The rate of organic acid production depends on the rate of microbial growth and utilization of carbon source. Various mathematical models had been utilized to find the correlation between substrate utilization and microbial growth. Substrate inhibition or product inhibition can terminate the microbial growth. It is due to the phenomenon of depletion of the medium components or formation of products that are toxic to microbial growth (Dikshit et al., 2017). If the accumulation of toxic product is occurred in the fermentation broth, the microbial growth will reduce and even will result in termination of microbial growth. Hence it is essential to study the kinetic behavior of the microorganism. Moreover, carbon source involved in this study is glycerol. Substrate utilization kinetics can be investigated through growth kinetic models. The Monod model, Moser model and Haldane–Andrews model were used to study the microbial growth kinetics.
2.5.2.1. Ghose model. Ghose model is one of the linear product inhibition model and which relates microbial specific growth rate with the effect of product concentration and threshold concentration of the product (Claret et al., 1993). The influence of product concentration on microbial growth is described by Ghose model Eq. (5),
μ=
2.5.2.2. Luong model. Luong model is the modified form of Ghosh model (Luong 1987) and correlates the product concentration and microbial specific growth rate with an empirical constant for product concentration terms.
μ=
μ=
The estimation of produced malic acid was made by performing High-performance Liquid chromatography (HPLC) (BioRad). HPX-87H column was operated with 5 mM sulphuric acid as mobile phase at 30 °C with a flow rate of 0.6 mL min−1 and detected with a UV–vis diode array detector at 230 nm. The known concentration of malic acid was prepared and analyzed for the preparation of standard curves by plotting known concentration of malic acid versus peak area versus to determine the unknown concentration of malic acid.
(3)
2.5.1.3. Haldane–Andrews model. Haldane–Andrews kinetic model was used to study substrate inhibition kinetics of crude glycerol on microbial growth (Dikshit and Moholkar, 2016). Initially, this model was proposed by Andrews (1968) and was described by the following Eq. (4),
3. Results and discussion
μmax S S2 KIS
(7)
2.6. Analytical methods
where n is the degree of inhibition.
S + KS +
μmax S −Po e Kip S + KS
where Kip is the product inhibition constant. The acquired experimental data was fitted to curve fitting option and the numerical values of these models were obtained by using MATLAB R2017a.
2.5.1.2. Moser model. Moser model is one of the modified forms of Model model, in which the role of substrate concentration on the inhibition of microbial growth is considered the power term (Rohit et al., 2018). Moser model is described by the following Eq. (3),
μ=
(6)
2.5.2.3. Aiba model. Aiba et al., (1968) developed a model relating the effect of product inhibition and microbial specific growth and mostly used for studies of the product inhibition phenomenon. This model includes another term known as product inhibition constant.
(2)
μmax S n KS + Sn
μmax S (1 − (Po/ Pm)α ) KS + S
where α is an empirical constant.
where, µ is the specific growth rate (h−1), µmax is the maximum specific growth rate (h−1), KS is the substrate saturation constant and S is the concentration of the substrate (g/L). The important assumption in Monod model is that the microbial specific growth rate is directly proportion to substrate concentration. Hence the phenomenon of microbial growth substrate inhibition due to higher substrate concentrations was not illustrated by this model.
μ=
(5)
where Pm is the threshold product concentration that is beyond which no biological activities occurred and Po is the initial product concentration in the fermentation medium (g/L).
2.5.1.1. Monod model. Monod proposed the growth kinetic model relating the specific growth rate with the growth limiting substrate concentration (Monod 1949). The relationship of Monod model is described by the following Eq. (2),
μ S μ = max KS + S
μmax S (1 − Po/ Pm) KS + S
3.1. Plackett–Burman experimental design (4)
Totally seven variables were selected for screening using PlackettBurman design. It exhibited totally 20 experimental runs for malic acid production with the combination of the two levels of each variable as shown in Table 1. The difference between the mean of the measurements made at the two levels of the variables resulted in the prediction of main effect of each parameter on malic acid production. Malic acid production obtained using PBD experiments showed variations from 61.74 to 74.32 g/L of response. The generated PBD was exposed to perform the statistical analysis by using MINITAB 18.1 and the calculation of the independent effect, p-value with confidence level and tvalue were performed. Table 2 summarizes the estimated coefficients, confidence level and t-value. It was clear that crude glycerol concentration (g/L) and initial pH showed highly significant probability value (p < 0.001) and yeast extract concentration (g/L) showed significant probability value (p < 0.05). Accordingly, initial pH was an important significant parameter on malic acid production, crude
where KIS is the substrate inhibition constant. 2.5.2. Product inhibition kinetic models The effect of product concentration on the growth of A. niger PJR1 and glycerol fermentation were investigated by changing the initial concentration of malic acid ranging from 0 g/L to 120 g/L in the fermentation broth at a fixed concentration of crude glycerol (160 g/L). Before the fermentation, malic acid (commercial) was additionally included to the fermentation medium. Concurrently, without the addition of malic acid in the fermentation medium was used as a control. Fermentation was performed and samples were withdrawn every 24 h. Amount of glycerol utilized and malic acid produced were analyzed. The observed values of glycerol utilization and malic acid production were fitted to various product inhibition models like Ghosh model, Luong model and Aiba model were used in this work (Dikshit et al., 20
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and crude glycerol concentration of 155 g/L as shown in Table 3. Furthermore, the maximum malic acid titer of 91.14 g/L was observed with the initial pH at 6, crude glycerol concentration of 160 g/L and yeast extract concentration of 1.5 g/L. The predicted malic acid production from crude glycerol by using A. niger PJR1 was fitted to developed regression model. Table 4 summarizes the ANOVA result for the response. As the calculated F value and p-value were 50.90 and < 0.001 (very low), the regression model was highly significant to represent the dependence of malic acid production on components studied. It was clear that all the three variables were significant. If the p-value was < 0.05, the respective terms were said to be significant. Whereas, p-value greater than 0.05 represented the respective terms are insignificant. The p < 0.05 for A, B, C, A2, B2 and C2 revealed that the linear and square effect of the variables were significant. But the interaction effect between initial pH, crude glycerol concentration and yeast extract concentration were not found to be significant. The p > 0.05 for AB, BC and AC revealed that the interaction effect of the variables were not significant. The coefficient of determination (R2) value of the model defines the measure of variability between the experimental variables and response values that are observed. The R2 value (0.9786) in the study was very close to 1, the model predicts the better response. The reliability and precision of the experiments could be evaluated through the coefficient of variation value. The adjusted R2 value was 0.9594, which was close to the R2 value and representing the maximal correlation among the experimental and predicted values. The probability value of lack of fit was insignificant, which illustrated that the quadratic model was valid for the present study. The equation pertaining the variables with coded units was represented in Eq. (8).
Table 1 Plackett-Burman experimental design for factors affecting malic acid production from crude glycerol using A. niger PJR1. Std. order
X1
X2
X3
X4
X5
X6
X7
Malic acid titer (Y) (g/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
7 7 5 5 7 7 7 7 5 7 5 7 5 5 5 5 7 7 5 5
20 30 30 20 20 30 30 30 30 20 30 20 30 20 20 20 20 30 30 20
3.5 2.5 3.5 3.5 2.5 2.5 3.5 3.5 3.5 3.5 2.5 3.5 2.5 3.5 2.5 2.5 2.5 2.5 3.5 2.5
165 165 155 165 165 155 155 165 165 165 165 155 165 155 165 155 155 155 155 155
1 2 2 1 2 2 1 1 2 2 2 2 1 2 1 2 1 1 1 1
1 1 2 2 1 2 2 1 1 2 2 2 2 1 2 1 2 1 1 1
0.1 0.1 0.1 0.3 0.3 0.1 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.1 0.3 0.1 0.3 0.1 0.1
71.12 70.24 61.54 62.27 71.15 74.25 77.25 71.18 61.87 70.23 61.74 74.32 62.21 65.31 62.34 65.93 76.94 76.41 66.34 66.17
Table 2 Estimated coefficients, calculated t value, p value and confidence level as per PB Experimental design for production of malic acid. Variable
Main Effect
Estimated co-efficient
t-value
p-value
Constant X1 X2 X3 X4 X5 X6 X7
– 9.737 −0.275 −0.595 −4.011 −1.565 −0.263 0.673
68.441 4.869 −0.138 −0.297 −2.005 −0.783 −0.132 0.337
282.15 20.07 −0.57 −1.23 −8.27 −3.23 −0.54 1.39
< 0.001 < 0.001 0.581 0.244 < 0.001 0.007 0.598 0.191
Y = 90.622 + 3.312A + 1.756B + 1.373C + 0.433AB − 0.377AC − 0.285BC − 7.741A2 − 2.851B2 − 2.836C 2
(8)
where Y is the malic acid titer (g/L), A is the initial pH, B is the crude glycerol concentration (g/L) and C is the concentration of yeast extract (g/L). The results of predicted malic acid titer using ANN were shown in Table 3. Least square optimization was performed by the Gauss-Newton technique for the model coefficients. The fitting of three variables had hidden notes of seven with 0.75 as the function of Gaussian basis. The fitted mean squared error percentage was found to be < 0.1%. The value of percentage of normalized mean squared error was found to be < 0.04%. It represented the in-significant stage of error of the experimental data. Usually, the production of organic acid production was depended on the initial pH of the production medium (Bohlmann et al., 1998). The increased production of organic acid was attained at the optimum pH level (Rymowicz and Lenart, 2003). Whereas, the carbon source was reported to be a highly significant variable for organic acid production using A. niger (Bahaloo-Horeh & Mousavi 2017). Initial pH, crude glycerol concentration and yeast extract concentration were found to be the most significant variable. The RSM regression model in Eq. (8) was solved to predict the optimum value of the variables using MINITAB 18.1. The predicted optimum value of variables was initial pH at 6.21, crude glycerol concentration of 161.56 g/L and yeast extract concentration of 1.6 g/L. Investigation of the interactions between the selected variables could result in the detailed analysis of the optimization process. A factor can correlate either with one variable or all the other variables by making the possibility of existence through a huge number of interactions. The response surface plots were generated to identify the interaction effect of these variables and to find the optimal value of each variable for achieving the maximum malic acid production. The response surface plot (Fig. 1a) represented the interaction effect of initial pH and crude glycerol concentration on malic acid production
glycerol was served as a carbon source and yeast extract was served as a nitrogen source. Initial pH, crude glycerol concentration (g/L) and yeast extract concentration (g/L) were identified as significant variables that affecting the production of malic acid and further considered for the optimization using three-level CCD for maximum production of malic acid. The R2 value, adjusted R2 value and predicted R2 value were found to be 0.9759, 0.9618 and 0.9330, respectively. The crude glycerol obtained from biodiesel production contained glycerol served as a carbon source for 1,3-dihyroxyacetone production using Gluconobacter oxydans (Dikshit et al., 2017). The microbial growth and malic acid production by A. niger was affected by the concentration of carbon source and initial pH of the medium (BahalooHoreh & Mousavi 2017). Similarly, nitrogen source was reported to be an important parameter for organic acid production by A. niger (Bari et al., 2009; Ozdal & Kurbanoglu 2018). 3.2. Optimization of fermentation variables for malic acid production using central composite design and artificial neural network The central composite design experiment was designed using the MINITAB 18.1. The production of malic acid was executed according to Table 3. The student’s t-test and F-test were performed using MINITAB 18.1 for the experimental malic acid production. The response data were analyzed in order to study the interactions between variables such as initial pH, yeast extract concentration and crude glycerol concentration by using central composite design. The effect of the generated quadratic model was determined and statistics like R2 value, lack of fit and F-values were calculated. The lowest malic acid titer value was 70.23 g/L with initial pH at 5, yeast extract concentration of 1 g/L 21
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Table 3 Three-level CCD in actual unit of factors with experimental and predicted malic acid titer value produced from crude glycerol using A. niger PJR1. Std. Order
Initial pH (A)
Crude glycerol concentration (B) (g/L)
Yeast extract concentration (C) (g/L)
Observed malic acid titer (g/L)
Predicted malic acid titer (CCD) (g/L)
Predicted malic acid titer (ANN) (g/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
5 7 5 7 5 7 5 7 5 7 6 6 6 6 6 6 6 6 6 6
155 155 165 165 155 155 165 165 160 160 155 165 160 160 160 160 160 160 160 160
1 1 1 1 2 2 2 2 1.5 1.5 1.5 1.5 1 2 1.5 1.5 1.5 1.5 1.5 1.5
70.23 76.35 74.87 82.13 74.43 78.45 77.34 83.68 78.23 87.61 88.31 87.31 86.12 89.53 91.14 90.45 90.15 90.74 90.87 90.23
70.52 77.04 73.74 81.98 74.59 79.60 76.67 83.41 79.57 86.19 86.02 89.53 86.41 89.16 90.62 90.62 90.62 90.62 90.62 90.62
70.71 77.41 73.91 81.05 74.08 79.87 76.10 82.97 79.13 85.83 86.64 89.11 86.01 88.93 90.44 90.44 90.44 90.44 90.44 90.44
reduced the production of malic acid. It was observed that the production of malic acid was high at near to the middle level of both yeast extract concentration and initial pH. The response surface plot (Fig. 1e) represents the interaction effect of crude glycerol concentration and yeast extract concentration on malic acid production by keeping the third variable initial pH as constant at the middle level. Fig. 1f represents the contour plot obtained and the interaction between yeast extract concentration and crude glycerol concentration was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as crude glycerol concentration and yeast extract concentration. It was observed that increase in yeast extract concentration and crude glycerol concentration near to their middle level increased malic acid titer. Further increase in crude glycerol concentration and yeast extract concentration from the middle level to higher level reduced the production of malic acid. It was observed that the production of malic acid was high at near to the middle level of both crude glycerol concentration and yeast extract concentration.
Table 4 Analysis of variance for quadratic model of response surface for malic acid production. Source
Degrees of freedom
Sum of Squares
Mean Square
F-value
p-value
Regression A B C AB AC BC A2 B2 C2 Residual Error Lack-of-Fit Pure Error Total
9 1 1 1 1 1 1 1 1 1 10 5 5 19
855.761 109.693 30.835 18.851 1.496 1.140 0.650 164.785 22.351 22.117 18.680 17.934 0.746 874.442
95.085 109.693 30.835 18.851 1.496 1.140 0.650 164.785 22.351 22.117 1.868 3.587 0.149
50.90 58.72 16.51 10.09 0.80 0.61 0.35 88.21 11.97 11.84
< 0.001 < 0.001 0.002 0.010 0.392 0.453 0.568 < 0.001 0.006 0.006
24.04
0.072
by keeping the third variable yeast extract concentration as constant at the middle level (1.5 g/L). Fig. 1b represented the contour plot obtained and the interaction between crude glycerol concentration and initial pH was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as initial pH and crude glycerol concentration. It was observed that the increase in crude glycerol concentration and initial pH near to their middle level increased the production of malic acid. Further increase in initial pH and crude glycerol concentration from the middle level to higher level reduced the production of malic acid. It was observed that malic acid production was high at near to the middle level of both initial pH and crude glycerol concentration. The response surface plot (Fig. 1c) represents the interaction effect of yeast extract concentration and initial pH on malic acid production by keeping the third variable crude glycerol concentration as constant at the middle level (160 g/L). Fig. 1d represents the contour plot obtained and the interaction between initial pH and yeast extract concentration was exhibited by elliptical contour plot. The predicted malic acid titer was importantly influenced by the selected factors such as initial pH and yeast extract concentration. It was observed that increase in initial pH and yeast extract concentration near to their middle level increased the production of malic acid. Further increase in yeast extract concentration and initial pH from the middle level to higher level
3.3. Experimental validation for the predicted conditions The experiments were repeated four times under optimal conditions to confirm the competence of the equation of the response surface. The observed and predicted values were found to be 92.41 and 91.32, respectively. The standard deviation of observed malic acid titer was found to be 1.74 g/L. The R2 value between observed and predicted was 0.98. The predicted values of malic acid titer from the equation of regression model and ANN were closely related to agree with the observed experimental value. Thus, the validity was proved. 3.4. Kinetics of substrate inhibition on cell growth Growth studies of A. niger PJR1 was performed under the optimum level of the variables that was obtained through statistical design. The evaluated kinetic parameters of three microbial growth models relating to specific growth rate to substrate concentration of crude glycerol were shown in Table 5. Interestingly, elevated specific growth rate was observed with the substrate concentration up to about 160 g/L after which decrease in specific growth rate was detected. The highest specific growth rate was observed at the concentration of crude glycerol about 160 g/L. The data fitted to Monod model Moser model and HaldaneAndrews model for different crude glycerol concentration versus 22
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Fig. 1. 3D response surface plots showing interactions between (a) initial pH and yeast extract concentration, (c) initial pH and yeast extract concentration, (e) crude glycerol concentration and yeast extract concentration, Contour plots showing interactions between (b) initial pH and yeast extract concentration, (d) initial pH and yeast extract concentration, (f) crude glycerol concentration and yeast extract concentration.
Table 5 Kinetic parameters of microbial growth models and product inhibition models. Model
µmax (h−1)
Ks (g/L)
KI,S (g/L)
Pm (g/L)
Kip (g/L)
Correlation coefficient (R2)
Root mean square value
Monod Moser Haldane –Andrews Ghosh Luong Aiba
0.1444 0.1674 0.1542 0.182 0.1454 0.2869
26.11 77.1 89.7 30.95 25.22 120.7
– – 6.219 – – –
– – – 118 114.7 –
– – – – – 66
0.8317 0.8485 0.9284 0.9768 0.9825 0.8771
0.01867 0.01879 0.01292 0.0085 0.00738 0.01955
23
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Fig. 2. Result of fitting of Monod, Moser and Haldane-Andrews model to data of crude glycerol concentration versus specific growth rate.
Fig. 3. The value of α for Luong model was found to be 1.22. The R2 value of Ghose model and Aiba model were found to be 0.9768 and 0.8771, respectively. Among the three models, Aiba model showed the maximum specific growth value with the product inhibition constant value of 66. Luong model showed the maximum specific growth value of 0.1454 h−1 and the estimated threshold product concentration was 114.7 g/L. The calculated empirical constant value found to be 1.22. Ghose model showed the maximum specific growth value of 0.182 h−1 and the estimated threshold concentration of product was about 118 g/ L. Kim et al., (2016) reported that product inhibition kinetic model could be applied to 2,3 – butanediol production by using Klebsiella oxytoca. A detailed knowledge on product inhibition on the microbial growth could assist in the fermentative malic acid production. As a summary, crude glycerol fermentation with the phenomenon of product inhibition could be related to the growth of A. niger PJR1. Liu et al., (2017) reported that about 165 g/L of malic acid was produced using A. oryzae from glucose. About 134 g/L of malic acid was reported to produce using U. trichophora from glycerol (Zambanini et al. 2017). Importantly, West (2015) reported that the fungal strain of A. niger ATCC 12486 produced about 23 g/L of malic acid. However, in this study, malic acid produced from crude glycerol was 92.64 + 1.54 g/L by using morphologically controlled A. niger PJR1 under optimized conditions. The amount of malic acid produced in this study was higher than the value of malic acid produced (69 g/L) from xylose by using A. oryzae (Knuf et al., 2014). This indicated that morphologically controlled A. niger PJR1 has the potential to produce malic
specific growth rate were shown in Fig. 2. The value of n for Moser model was found to be 0.7295. Among the three microbial growth kinetic models, Haldane-Andrew model was found to be best fit with R2 value of 0.9284 with the root mean square value of 0.01292. Under optimized condition, A. niger PJR1 was able to grow on crude glycerol concentration of about 160 g/L. Similarly, when A. niger was cultivated on wheat bran as a substrate, different growth kinetic models were used to depict the growth behavior of A. niger (Augustine et al., 2015). Haldane-Andrew model was reported to be the best model when crude glycerol was exploited for dihydroxyacetone production using Gluconobacter oxydans MTCC 904 (Dikshit and Moholkar, 2016). As a summary, fermentation of glycerol with the phenomenon of substrate inhibition could be related to the growth of A. niger PJR1.
3.5. Kinetics of product inhibition on cell growth In order to examine the effect of malic acid on the growth of A. niger PJR1, the batch fermentations were performed. The fermentation media contained different initial malic acid concentration at optimum conditions. The calculated specific growth rate was plotted against different initial malic acid concentration. The values of estimated parameters of all models were listed in Table 5. Among the three product inhibition model, Luong model was established to be best fit with R2 value of 0.9825 with root mean square value of 0.00738. The data fitted to Ghose model, Luong model and Aiba model for different initial malic acid concentration versus specific growth rate were shown in
Fig. 3. Result of fitting of Ghose, Luong and Aiba model to data of initial malic acid concentration versus specific growth rate. 24
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acid by utilizing crude glycerol without any pre-treatment. The successive employment of optimization methods had improved malic acid production from crude glycerol by using A. niger PJR1. When compared with other substrates, fermentation of crude glycerol to malic acid could serve cost effective method using A. niger. The processes involved in this study could be predominantly suitable for the scale up process of malic acid production. Moreover, A. niger is the cell factory of organic acid production and A. niger PJR1 could be a promising microorganism for the production of malic acid.
Dikshit, P.K., Padhi, S.K., Moholkar, V.S., 2017. Process optimization and analysis of product inhibition kinetics of crude glycerol fermentation for 1,3–dihydroxyacetone production. Bioresour. Technol. 244, 362–370. Dikshit, P.K., Kharmawlong, G.J., Moholkar, V.S., 2018. Investigations in sonication–induced intensification of crude glycerol fermentation to dihydroxyacetone by free and immobilized Glucono bacteroxydans. Bioresour. Technol. 256, 302–311. Iyyappan, J., Baskar, G., Bharathiraja, B., Saravanathamizhan, R., 2018b. Malic acid production from biodiesel derived crude glycerol using morphologically controlled Aspergillus niger in batch fermentation. Bioresour. Technol. 269, 393–399. Iyyappan, J., Bharathiraja, B., Baskar, G., Jayamuthunagai, J., Barathkumar, S., Anna Shiny, R., 2018a. Malic acid production by chemically induced Aspergillus niger MTCC 281 mutant from crude glycerol. Bioresour. Technol. 251, 264–267. Jose, A.A., Anusree, G., Pandey, A., Binod, P., 2018. Production optimization of poly-γglutamic acid by Bacillus amyloliquefaciens under solid state fermentation using soy hull as substrate. Ind. J. Biotechnol. 17, 44–49. Kim, D.K., Park, J.M., Song, H., Chang, Y.K., 2016. Kinetic modeling of substrate and product inhibition for 2,3-butanediol production by Klebsiella oxytoca. Biochem. Eng. J. 114, 94–100. Knuf, C., Nookaew, I., Remmers, I., Khoomrung, S., Brown, S., Berry, A., Nielsen, J., 2014. Physiological characterization of the high malic acid-producing Aspergillus oryzae strain 2103a 68. Appl. Microbiol. Biotechnol. 98, 3517–3527. Ko, J.K., Um, Y., Woo, H.M., Kim, K.H., Lee, S.M., 2016. Ethanol production from lignocellulosic hydrolysates using engineered Saccharomyces cerevisiae harboring xylose isomerase-based pathway. Bioresour. Technol. 209, 290–296. Latha, S., Sivaranjani, G., Dhanasekaran, D., 2017. Response surface methodology: a nonconventional statistical tool to maximize the throughput of Streptomyces species biomass and their bioactive metabolites. Crit. Rev. Microbiol. 43, 567–582. Li, C., Gao, S., Wang, H., Li, X., Ong, K.L., Yang, X., Lin, C.S.K., 2018. Succinic acid production using a glycerol-based medium by an engineered strain of Yarrowia lipolytica: statistical optimization and preliminary economic feasibility study. Biochem. Eng. J. 137, 305–313. Li, X.J., Liu, Y., Yang, Y., Zhang, H., Wang, H.L., Wu, Y., Zhang, M., Sun, T., Cheng, J.S., Wu, X.F., Pan, L.J., Jiang, S.T., Wu, H.W., 2014. High levels of malic acid production by the bioconversion of corn straw hydrolyte using an isolated Rhizopus delemar strain. Biotechnol. Bioproc. Eng. 19, 478–492. Liu, J., Xie, Z.P., Shin, H.D., Li, J.H., Du, G.C., Chen, J., Liu, L., 2017. Rewiring the reductive tricarboxylic acid pathway and L-malate transport pathway of Aspergillus oryzae for overproduction of L-malate. J. Biotechnol. 253, 1–9. Luong, J.M.Y., 1987. Kinetics of ethanol inhibition in alcohol fermentation. Biotechnol. Bioeng. 29, 242–248. Monod, J., 1949. The growth of bacterial cultures. Ann. Rev. Microbiol. 3, 371–394. Ozdal, M., Kurbanoglu, E.B., 2018. Citric acid production by Aspergillus niger from agroindustrial by-products: molasses and chicken feather peptone. Waste Biomass Valorization. https://doi.org/10.1007/s12649-018-0240-y. Plackett, R.L., Burman, J.P., 1946. The design of optimum multifactorial experiments. Biometric 33, 305–325. Rohit, S.G., Jyoti, P.K., Subbi, R.R.T., Naresh, M., Senthilkumar, S., 2018. Kinetic modeling of hyaluronic acid production in palmyra palm (Borassus flabellifer) based medium by Streptococcus zooepidemicus MTCC 3523. Biochem. Eng. J. 137, 284–293. Rymowicz, W., Lenart, D., 2003. Oxalic acid production from lipids by a mutant of Aspergillus niger at different pH. Biotechnol. Lett. 25, 955–958. Sen, S., Nandi, S., Dutta, S., 2018. Application of RSM and ANN for optimization and modeling of biosorption of chromium (VI) using cyanobacterial biomass. Appl. Water Sci. 148 (8). https://doi.org/10.1007/s13201-018-0790-y. Shirzad, H., Niknam, V., Taheri, M., Ebrahimzadeh, H., 2017. Ultrasound-assisted extraction process of phenolic antioxidants from Olive leaves: a nutraceutical study using RSM and LC–ESI–DAD–MS. J. Food Sci. Technol. 54 (8), 2361–2371. Vivek, N., Sindhu, R., Madhavan, A., Anju, A.J., Castro, E., Faraco, V., Pandey, A., Binod, P., 2017. Recent advances in the production of value added chemicals and lipids utilizing biodiesel industry generated crude glycerol as a substrate –m aspects, challenges and possibilities: an overview. Bioresour. Technol. 239, 507–517. Vivek, N., Ashok Pandey, A., Binod, P., 2018. An efficient aqueous two phase systems using dual inorganic electrolytes to separate 1,3-propanediol from the fermented broth. Bioresour. Technol. 254, 239–246. Wang, Y., Song, X., Zhang, Y., Wang, B., Zou, X., 2016. Effects of nitrogen availability on polymalic acid biosynthesis in the yeast-like fungus Aureobasidium pullulans. Microb. Cell Fact. 15, 146. West, T.P., 2015. Fungal biotransformation of crude glycerol into malic acid. Z. Naturforsch. 70 ((5–6)c), 165–167. Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A., Pokomeda, K., 2014. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresour. Technol. 160, 150–160. Zambanini, T., Tehrani, H.H., Geiser, E., Sonntag, K.C., Buescher, M.J., Meurer, G., Wierckx, N., Blank, M.L., 2017. Metabolic engineering of Ustilago trichophora TZ1 for improved malic acid production. Metab. Eng. Commun. 4, 12–21. Zhang, N., Jiang, J.C., Yang, J., Wei, M., Zhao, J., Xu, H., Xie, J.C., Tong, Y.J., Yu, L., 2018. Citric Acid Production from Acorn Starch by Tannin Tolerance Mutant Aspergillus niger AA120. Appl. Biochem. Biotechnol. https://doi.org/10.1007/ s12010-018-2902-4.
4. Conclusion The present study revealed that initial pH, crude glycerol concentration and yeast extract concentration were significant variables involved in the production of malic acid using Aspergillus niger PJR1. The RSM combined ANN exposed that under initial pH at 6.21, crude glycerol concentration of 161.56 g/L and yeast extract concentration of 1.6 g/L the morphologically controlled A. niger PJR 1 produced 92.61 + 1.86 g/L of malic acid. Among the three substrate inhibition kinetic models, Haldane-Andrew model was found to be best fit and among the three product inhibition kinetic models, Luong model gave the best fit. The knowledge on optimization and biokinetic analysis would reduce the cost of malic acid production from crude glycerol. Acknowledgement This study was financially supported by Science and Engineering Research Board (SERB), India (File No. EEQ/2017/000200). The author thank DST-SERB, India for granting financial support for this work. References Aiba, S., Shoda, M., Nagatami, M., 1968. Kinetics of product inhibition in alcohol fermentation. Biotechnol. Bioeng. 10, 845–864. Andrews, J.F., 1968. A mathematical model for the continuous culture of microorganisms utilizing inhibitory substance. Biotechnol. Bioeng. 10, 707–723. Augustine, A., Joseph, I., Raj, P.R., David, N.S., 2015. Growth kinetic profiles of Aspergillus niger S14 a mangrove isolate and Aspergillus oryzae NCIM 1212 in solid state fermentation. Ind. J. Fish. 62, 100–106. Bahaloo-Horeh, N., Mousavi, S.M., 2017. Enhanced recovery of valuable metals from spent lithium-ion batteries through optimization of organic acids produced by Aspergillus niger. Waste Manage. 60, 666–679. Bari, M.N., Alam, M.Z., Muyibi, S.A., Jamal, P., 2009. Improvement of production of citric acid from oil palm empty fruit bunches: optimization of media by statistical experimental designs. Bioresour. Technol. 100, 3113–3120. Baskar, G., Selvakumari, I.A., Aiswarya, R., 2018. Biodiesel production from castor oil using heterogeneous Ni doped ZnO nanocatalyst. Bioresour. Technol. 250, 793–798. Bharathiraja, B., Chakravarthy, M., Ranjith Kumar, R., Yuvaraj, D., Jayamuthunagai, J., Praveen Kumar, R., Palani, S., 2014. Biodiesel production using chemical and biological methods – a review of process, catalyst, acyl acceptor, source and process variables. Renew. Sustainable Energy Rev. 38, 368–382. Bohlmann, J.T., Cameselle, C., Nunez, M.J., Lema, J.M., 1998. Oxalic acid production by Aspergillus niger. Part II. Optimization of fermentation with milk whey as carbon source. Bioproc. Eng. 19 (5), 337–342. Carvalho, M., Roca, C., Reis, M.A.M., 2016. Improving succinic acid production by Actinobacillus succinogenes from raw industrial carob pods. Bioresour. Technol. 218, 491–497. Chen, Z., Liu, G., Zhang, J., Bao, J., 2019. A preliminary study on L-lysine fermentation from lignocellulose feedstock and techno-economic evaluation. Bioresour. Technol. 271, 196–201. Chen, X.L., Wang, Y.C., Dong, X.X., Hu, G.P., Liu, L.M., 2017. Engineering rTCA pathway and C4-dicarboxylate transporter for L-malic acid production. Appl. Microbiol. Biotechnol. 101, 4041–4052. Claret, C., Bories, A., Soucaille, P., 1993. Inhibitory effect of dihydroxyacetone on Gluconobacter oxydans: kinetic aspects and expression by mathematical equations. J. Ind. Microbiol. 11, 105–112. Dai, Z., Zhou, H., Zhang, S., Gu, H., Yang, Q., Zhang, W., Dong, W., Ma, J., Fang, Y., Jiang, M., Xin, F., 2018. Current advance in biological production of malic acid using wild type and metabolic engineered strains. Bioresour. Technol. 258, 345–353. Dikshit, P.K., Moholkar, V.S., 2016. Kinetic analysis of dihydroxyacetone production from crude glycerol by immobilized Gluconobacter oxydans MTCC 904. Bioresour. Technol. 216, 948–957.
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