In Malaysia, fish processing industry, wet market and fish loading are looked
upon as producers of worthless garbage by discarding a huge number of wastes
which are parts of fish body. The Fish Waste (FW) is discarded without attempt
for recovery. Without proper utilization, these wastes may cause environmental
problems and for now, most of the fish waste is dumped as garbage or directly
used as feedstuff. Fish waste consists of fish head that constitute approximately
20% of the fresh water fish biomass and are a rich source of protein and polyunsaturated
lipids (Bhaskar et al, 2008). This solid waste
has approximately the same protein content as fish flesh (Batista,
1999). Although, some amount of fish waste is being utilized today as feedstuff,
a huge amount is still being discarded. The reason for introdufcing fish waste
as feedstuff is due to good source of protein content in the fish waste (Arnesen
and Gildberg, 2007; Bhaskar et al., 2007;
Gildberg, 2001). However, using FW directly as feedstuff
might be harmful for animal due to microbiological factor contributed by the
composition of undesirable substances in the fish waste (Garcia
et al., 2005).
In this study, FW was being extracted in order to get protein by using enzymatic
method. Enzymes used to produce fish protein hydrolysate have at least one common
characteristic; they should be food grade and if they are of microbial origin,
the producing organism has to be non-pathogenic. The choice of substrate, protease
employed and the degree to which the protein gets hydrolysed generally affects
the physicochemical properties of the resulting hydrolysates (Mullally
et al., 1995). For this study, two enzymes (alcalase and protamex) were
combined for the investigation. The choice of the two enzymes was made based
on the fact that these enzymes have already been widely used in the food industry
for various applications and are proved to be safe (Beg
et al., 2003). Both enzymes are belongs to protease enzyme which
produced a significantly higher extraction yield. Alcalase and protamex are
alkaline bacterial protease produced from Bacillus, have been proven
to be among the best enzymes used in the preparation of fish protein hydrolysate
by many researchers (Hoyle and Merrit, 1994).
Above all, several factors in the extraction process, such as extraction time,
temperature, rotation speed and level of enzyme to substrate, may affect final
properties of the extracted protein from FW. When more than a few factors affect
the desired responses in a certain process designs, Response Surface Methodology
(RSM) becomes an effectiveness tool for optimizing the process. Experimental
design technique is a very useful tool for this purpose as it provides statistical
models which help in understanding the interactions among the parameters that
have been optimized (Hameed et al., 2009). The
advantages of using RSM have been reported to include reduction in the number
of experimental trials needed to evaluate multiple parameters and the ability
of the statistical tool to identify interactions. In addition to analyzing the
effects of the independent variables, the experimental methodology also generates
a mathematical model that describes the overall process (Batista,
With respect to the background described above, the specific objective of the study was to optimize the parameters (extraction time, temperature, rotation speed and level of enzyme to sample) in the extraction of protein from FW using combination of two enzymes which were alcalase and protamex.
MATERIALS AND METHODS
Fish waste: Fish waste was supplied by Protigam Food Industries Sdn. Bhd., which contained fish head. Fish waste was then minced using a standard electrical blender, Panasonic and treated with petroleum ether for the purpose of fat removal. De-fatted FW were placed in container for further analysis.
Proximate composition of protein: Proximate composition of protein was carried out by Kjehdahl method using Kjeltec protein analyser.
Protein extraction process: Enzyme solution was prepared with a ratio
of 1:1 between alcalase and protamex. Amount of enzyme solution added to the
sample and the condition of extraction were based on different combinations
programmed by RSM. From RSM, 29 individual points were employed in the extraction
process. The independent variables and their levels were selected based on previous
studies. Extraction was done using a mixer (Wise Stire, Model HS30D). Following
the extraction process, FW solution was centrifuged (Sigma, Model 3K18) at 13000
rpm and 4°C. The supernatant containing soluble protein was collected.
Protein determination in supernatant: Soluble protein in supernatant was determined using Bradford method.
Experimental design: Response Surface Methodology (RSM) was used in
this study to determine the optimum conditions for the extraction of protein
from FW samples. The experimental design and statistical analysis were performed
using design expert software. The experiments were based on a box-behnken design
with a quadratic model in order to study the combined effects of four independent
variables (temperature, level of enzyme: sample, speed rotation and extraction
||Independent variables and their coded levels used in RSM studies
for optimizing extraction of protein from FW
These four independent variables were represented by X1, X2, X3 and X4, respectively.
Each independent variable had 3 levels which were -1, 0 and +1, as shown in
Table 1. The dependent variable was known as response function.
RESULTS AND DISCUSSION
Proximate composition of the raw material and other intermediates in the study.
As can be seen in Table 2, the raw FW had a protein content of 16.3 g with a high amount of fat content. After have been de-fatted, the protein and fat content of FW decreased slightly to 15.5 and 8.3 g, respectively.
Fitting the models: The relationship between four variables (level of
enzyme:sample, speed rotation, extraction time and temperature) and the important
process response (extracted protein) for protein extraction process was analyzed
using Response Surface Methodology (RSM). Significance model terms are desired
to obtain a good fit in a particular model (Ghafari et
al., 2009). The study utilized RSM to develop a prediction model for
optimizing the extraction of protein from FW. The experimental conditions and
the corresponding response values from the experimental design are presented
in Table 2. The independent and dependent values were analyzed
to obtain a regression equation that could predict the response within the given
range. The regression equation for protein extraction is as follows:
Effects of independent variables on responses: The response surface
graph for extracted protein from FW as a function of speed rotation and level
of enzyme to sample is shown in Fig. 1. The graph indicates
that the amount of extracted protein increased up to 76 mg mL-1 before
reducing considerably with the decrease in level of enzyme to sample.
Response surface graph for amount of extracted protein as
a function of speed rotation and level of enzyme:sample during protein extraction
from FW (extraction time and temperature at the center of their levels)
||Surface graph for amount of extracted protein as a function
of extraction time and temperature during protein extraction from FW (rotation
speed and level of enzyme:sample at the center of their levels)
||Protein composition of FW (g/100 g)
The extracted protein was more pronounced at the high level of enzyme. In Fig.
2, amount of extracted protein showed a decreasing pattern with increase
in temperature and at the same time increase in extraction time. It also showed
an increasing pattern of extracted protein yield with the increase in extraction
time at the lower temperature.
Figure 3 and 4 represent the pattern of
changes in protein yield as affected by extraction time. In Fig.
3, the plots are approximately symmetrical in shape with circular contour.
Rotation speed did influence the extraction, where the protein yield increased
with increasing in the speed of rotation. Figure 4 concludes
that the higher amount of extracted protein occurred at the longest extraction
time and at the highest level of enzyme to sample. For each graph discussed
above, all other independent variables not depicted in graph were positioned
at the center of their levels. From the graphs, it was clearly shown that level
of enzyme to sample and extraction time values played a major role in protein
extraction compared to other independent variables.
Optimum conditions for the extraction of FW and model verification:
The influence of temperature, speed rotation, extraction time and level of enzyme
to sample, on the extracted protein using combination of alcalase and protamex
enzymes was determined using Box-behnken design as mentioned in the previous
Response surface graph for extracted protein as a function
of speed rotation and extraction time during protein extraction from FW
(level of enzyme:sample and temperature at the center of their levels)
Response surface graph for amount of extracted protein as
a function of level of enzyme:sample and extraction time during protein
extraction from FW (rotation speed and temperature at the center of their
From the model, under the optimum conditions, a maximum yield of 80.75 mg
mL-1 protein was extracted at level of enzyme:sample of 1.50, rotation
speed 171.24, extraction time 1429 min and temperature of 35°C.
The conditions of protein extraction (level of enzyme to sample, speed rotation, extraction time and temperature) were optimized using RSM to improve protein extraction from FW using combination of alcalase and protamex enzymes. From the RSM results, the optimum conditions of level of enzyme: sample (1.50), speed rotation (171.24), extraction time (1429 min) and temperature (35°C) were obtained with the highest predicted protein value of 80.75 mg mL-1. For further study, these results can be compared with extraction of fish waste by using single enzyme or combination of enzyme in different stage of extraction.