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Optimization of bioprotein production from palm kernel cake by Aspergillus terreus UNIMAP AA-1.


The rising costs of imported ingredients such as fish meal, soybean meal, corn flour and wheat flour greatly cut into the profit margins of local fish farmers to such an extent that many local aquaculture enterprises are no longer profitable [1]. This is especially true for the culture of lower-value fish species such as catfish, tilapia and carps. There is currently great interest within the animal feed industry to reduce costs by using locally available feed ingredients such as agricultural waste. Furthermore, the increasing need for fish feeds with higher and balanced nutritional composition has created the need for alternative protein sources.

As a promising approach to solve problem of protein shortage, palm kernel cake have been chosen as potential substrate for bioprotein production for fish feed. The use of agro waste byproduct such as palm kernel cake (PKC) likely the ideal solution to reduce the production cost. PKC produced in Malaysia is cheaper because it is exported to other country with lower price as cattle feed. PKC also established as a good feed ingredients. However, several factors can limit the function of PKC in fish diets. The factors are including relatively low protein content, possible amino acid deficiencies and presence of anti--nutritional factors [1]. Fungal solid state fermentation has been chosen in order to enhance nutritional value in PKC such as increasing protein content, as well as lowering cellulose and hemicelluloses in PKC. Common fungus used in the solid state fermentation is Aspergillus terreus [2]. In the previous, Jahwarhar et al [3] used Aspergillus terreus in a solid state fermentation in order to enhance the maximum mannase production since the fungi have the ability to secrete more protein on the carbon sources. This study aims to optimize the operating conditions for maximum yield of bioprotein from PKC, hence highlighting its potential as an alternative protein source to enhance nutrional content of fish feed.


Collection of Substrate:

An agro waste, palm kernel cake (PKC) was procured from Fleet Palm Oil Sdn. Bhd., Penang.

Substrate Preparation:

The pretreatment of PKC was carried out by adding 1% sodium hydroxide at a ratio of 1 L to 100 grams of PKC [4].

Microorganism Strain:

Aspergillus terreus UniMAP AA-1 was obtained from School of Bioprocess Engineering culture collection, University Malaysia Perlis [6].

Inoculums Preparation:

Spores were harvested from a week old A. terreus UniMAP AA-1. The spore count was done using haemacytometer and the concentration was adjusted to 107spores per mL before it was kept in the cold room for further used.

Nutrient/Mineral Preparation:

The amount of nutrients added to the substrate depends on the moisture content of fermentation which contain 0.2% K[H.sub.2]P[O.sub.4], 0.5% N[H.sub.4]N[O.sub.3], 0.1% NaCl, 0.1% MgS[O.sub.4].7[H.sub.2]O, 0.1% FeS[O.sub.4].7[H.sub.2]O, 0.1% CuS[O.sub.4].5[H.sub.2]O, 0.1% ZnS[O.sub.4].7[H.sub.2]O [5]. These nutrients were dissolved in distilled water and stirred to produce homogenous mixture. The nutrients solution was sterilized at 121 [degrees]C before it was added to the media.

Solid State Fermentation:

Nutrient media solution was added into 250 mL conical flask containing sterilized palm kernel cake. Amount of palm kernel cake, nutrient media solution and inoculums suspension depend on moisture content of the solid state fermentation. Aseptically, the flask was inoculated with inoculum suspension before covered with sterilized cotton wool and incubated for 6 days at different temperature.

Protein Analysis:

Protein analysis was performed using the Lowry Method with BSA as standard.

Optimization of operating condition using Central Composite Design (CCD):

Parameters for optimization of bioprotein production were modeled using Response Surface Methodology (RSM) based on CCD. The three studied parameters were temperature (25-35[degrees]C), inoculums size (5-15% v/w) and moisture content (40-60%) [3].


Design of experimental and statistical analysis:

Central Composite Design (CCD) was adopted to estimate the optimum operating condition for maximum bioprotein production by solid state fermentation. The three studied variables were coded as: moisture content (A), inoculums size (B) and temperature (C). Twenty experimental runs were conducted based on Design Expert Software 7.1.5. The experimental design and results of experiment are presented in Table 1 where it shows the predicted and actual protein concentration for standards carried out from the design. From the results, it can be observed that the highest bioprotein production of 2.17 mg/ml was achieved at 50% moisture content, 10% (v/w) inoculums size and temperature of 30[degrees]C. The lowest bioprotein production occurred at the condition of 50% moisture content, 18.41% (v/w) inoculums size and temperature of 30 [degrees]C which is about 0.52 mg/ml bioprotein concentration.

Results showed that the regression model necessary for the maximum bioprotein production follows the quadratic form with independent variables of A, B and C. The predicted values of protein concentration were calculated based on the Equation 1 that was generated by the Design Expert[R] software.

Bioprotein = (-10.64041) + (0.13158 x A) + (0.082846 x B) + (0.58531xC) + (1.62500E-003XAxB) - (1.62500E-003x A x C) + (5.05000-003xBxC) - (1.10671E-003x[A.sup.2] ) (0.017155x[B.sup.2])-(8.66947E-003x[C.sup.2]) (1)

where A, B and C represent moisture content, inoculums size and temperature respectively.

There was a good agreement between the experimental values and predicted values suggested by the CCD of response surface methodology through the model equation, with only a slight difference in term of bioprotein concentration. According to the results analysis of variance (ANOVA) generated after statistical analysis of the data (Table 2), it was shown that the model was highly significant with a probability value (p-value) of 0.0001 and F-value of 14.29 under same condition. The value probability >F less than 0.05 indicated the model term were significant [3]. Probability, p-value was used as a tool to check significant of each coefficient which in turn may indicate the pattern of the interaction between the variables [7]. In this case, there is only a 0.001% chance that a "Model F-Value" this large could occur due to noise. The p-value shows that the model is significant with all the three parameters which are moisture content, inoculums size and temperature are significant. Table 3 shown the p-value of the parameters is 0.0395, 0.0185 and 0.0056 for moisture content, inoculums size and temperature, respectively. The smaller the p-value, the more significant is the corresponding coefficient. The analyzed regression model suggested that the linear coefficient moisture content (A), inoculums size (B), temperature (C) and quadratic terms coefficient A2, B2, C2 were significant while the other terms coefficient included cross product coefficient (AB, AC, BC) were not significant model term because the p-value is greater than 0.00500 but still can be accepted because A2, B2 and C2 is significant model terms. Since there are significant squared terms in each model, all lower model including the interaction and main effects, significant should be retained in this model.

The significance of quadratic term of moisture content (A2), inoculums size (B2), temperature (C2) indicated that this factor can act as limiting factor and even small changes in value can be the cause of the big influence on the bioprotein concentration. The value of pure error shown in Table 2 was very low which is 0.038 indicating good reproducibility of the experimental data. The "Lack of Fit F-value" of 0.73 implies the Lack of Fit is not significant relative to the pure error. There is a 62.82% chance that a "Lack of Fit F-Value" this large could occur due to noise. The goodness of fit of the model can be checked by Lack of fit-test, Coefficient of determination ([R.sup.2]), Coefficient of Variance (CV), Prediction Residual Error Sum of Squares (PRESS) and adequate precession [7]. The good model exhibit low standard deviation, high value of [R.sup.2] value and a low PRESS. The closer [R.sup.2] value to 1.0, the stronger the model to make a prediction to the response [8].

This experimental result was in agreement with those reported by Archana and Satyanarayana [9] that moisture content is one of the key factors which could affect the metabolite production in solid state fermentation. Increasing the moisture content can leads to a decrease of the porosity of substrate thus limiting the oxygen transfer into the substrate all of which, in turn, results in decrease fungi growth and product formation. Meanwhile, a lower moisture content can reduced the solubility of nutrient of the substrate, lower degree of swelling and higher water tension [10]. The experimental results were linked to the previous study which reported that at 50% of moisture content, the maximum bioprotein production was achieved in optimization experiment by Aspergillus niger using PKC as a sole carbon sources [11]. Furthermore, the experimental result also was in agreement with Jahwarhar et al [3] that the optimal moisture content to enhance maximum mannase production by Aspergillus terreus was determined to fall in the range of 40-60%.

Temperature also has been stated as one of the most important environmental factors influencing the growth of fungi. According to Pietikainen et al [12], the optimum growth temperature for fungi is between 25-30 [degrees]C. This observation was in agreement with those reported by Singh et al [13] who showed that the highest protein produced by A. niger was in the temperature range of 25-30 [degrees]C. The experimental result also was in agreement with Jahwarhar et al [3] which found that the optimal temperature to enhance maximum mannase production by Aspergillus terreus falls in range of 25-35 [degrees]C. Lower inoculum size will retard the proliferation of biomass. Thus, the degradation of the substrates by the microbes is slower and affects the metabolite production [14]. Kumar et al [15] also reported that higher inoculum size than the optimum may produce too much biomass and may deplete the nutrient that necessary for microbial metabolite production. According to Jahwarhar et al [3], the optimal inoculums size to enhance the maximum mannase production by Aspergillus terreus to fall in range of 5-15% v/w. The experimental result was in agreement with the statement.

Validation for Optimum Process Condition:

Based on the results generated from interactions of the studied variables, one numerical solution was suggested by the software within the experimental range of the optimized parameters. The proposed experimental condition was at 32.46 [degrees]C, 42.38% of moisture content and 9.2% (w/v) of inoculums size. Bioprotein concentration of 1.986 mg/ml was successfully attained, slightly lower than the predicted value. A small error value demonstrated the validation of the RSM model, indicating that the model was adequate for the bioprotein production. Percent error of 2.1% fell within 5% indicated that the process optimization by CCD were capable and reliable to optimize the production of the bioprotein.


Based on the experimental results, the solid state fermentation has successfully increased the protein content of the fermented PKC significantly. This finding highlighted the potential of the palm kernel cake (PKC) as the substrate in bioprotein production in the future. Fermentation of agricultural waste products offers the generation of microbial protein or bioprotein that has high nutritional value, with no competition with food for human consumption. Thus, it is one of the most promising approaches for increasing the availability of proteins for fish feed.


Article history:

Received 25 September 2014

Received in revised form 26 October 2014

Accepted 25 November 2014

Available online 31 December 2014


The authors are thankful to Universiti Malaysia Perlis (UniMAP) for providing good facilities in the campus and for funding this project under Short Term Grant UniMAP STG 9001-00307.


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(1) Nur Solehah Baharuddin, (1) Siti Jamilah Hanim Mohd Yusof, (2) Khadijah Hanim Abd Rahman and (2) Zarina Zakaria

(1) School of Bioprocess Engineering, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia.

(2) Department of Chemical Engineering Technology, Faculty of Engineering Technology, Engineering Center, Universiti Malaysia Perlis, Pauh Putra, 02600 Arau, Perlis, Malaysia.

Corresponding Author: Nur Solehah Baharuddin, School of Bioprocess Engineering, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia.

Table 1: Experimental design and result of CCD of response
surface methodology for the optimization of bioprotein

Standard   Moisture Content   Inoculums Size
                 (%)             (% v/w)

           Coded    Actual   Coded    Actual
                     (%)               (%)

1          -1.000   40.00    -1.000    5.00
2          1.000    60.00    -1.000    5.00
3          -1.000   40.00    1.000    15.00
4          1.000    60.00    1.000    15.00
5          -1.000   40.00    -1.000    5.00
6          1.000    60.00    -1.000    5.00
7          -1.000   40.00    1.000    15.00
8          1.000    60.00    1.000    15.00
9          -1.682   33.18    0.000    10.00
10         1.682    66.82    0.000    10.00
11         0.000    50.00    -1.682    1.59
12         0.000    50.00    1.682    18.41
13         0.000    50.00    0.000    10.00
14         0.000    50.00    0.000    10.00
15         0.000    50.00    0.000    10.00
16         0.000    50.00    0.000    10.00
17         0.000    50.00    0.000    10.00
18         0.000    50.00    0.000    10.00
19         0.000    50.00    0.000    10.00
20         0.000    50.00    0.000    10.00

Standard      Temperature         Bioprotein
              ([degrees]C)    production (mg/mL)

           Coded    Actual   Actual   Predicted

1          -1.000   25.00     1.44      1.38
2          -1.000   25.00     1.24      1.15
3          -1.000   25.00     0.81      0.70
4          -1.000   25.00     0.67      0.78
5          1.000    35.00     1.83      1.64
6          1.000    35.00     1.04      1.08
7          1.000    35.00     1.44      1.45
8          1.000    35.00     1.24      1.22
9          0.000    30.00     1.64      1.81
10         0.000    30.00     1.49      1.42
11         0.000    30.00     0.81      0.95
12         0.000    30.00     0.52      0.49
13         -1.682   21.59     0.98      1.03
14         1.682    38.41     1.55      1.60
15         0.000    30.00     2.01      1.93
16         0.000    30.00     1.61      1.93
17         0.000    30.00     2.04      1.93
18         0.000    30.00     1.83      1.93
19         0.000    30.00     2.17      1.93
20         0.000    30.00     1.95      1.93

Table 2: Analysis of Variance (ANOVA) to Optimize Protein

Source                        SS     DF    MS     F Value

Model                        4.21    9    0.47     14.29
A-Moisture Content (%)       0.18    1    0.18     5.60
B-Inoculums Size (%v/w)      0.26    1    0.26     7.88
C-Temperature ([degrees]C)   0.40    1    0.40     12.33
AB                           0.053   1    0.053    1.61
AC                           0.053   1    0.053    1.61
BC                           0.13    1    0.13     3.89
A2                           0.18    1    0.18     5.39
B2                           2.65    1    2.65     80.95
C2                           0.68    1    0.68     20.67
Residual                     0.33    10   0.033
Lack of fit                  0.14    5    0.028    0.73
Pure Error                   0.19    5    0.038
Cor Total                    4.54    19

Source                       Probe >F        Note

Model                         0.0001      Significant
A-Moisture Content (%)        0.0395
B-Inoculums Size (%v/w)       0.0185
C-Temperature ([degrees]C)    0.0056
AB                            0.2329
AC                            0.2329
BC                            0.0767
A2                            0.0426
B2                           < 0.0001
C2                            0.0011
Lack of fit                   0.6282    Not Significant
Pure Error
Cor Total

SS: Sum of squares; DF: Degree of freedom; MS: Mean of squares.
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Article Details
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Author:Baharuddin, Nur Solehah; Yusof, Siti Jamilah Hanim Mohd; Rahman, Khadijah Hanim Abd; Zakaria, Zarina
Publication:Advances in Environmental Biology
Article Type:Report
Date:Nov 1, 2014
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