F.d.A. Corrêa et al. / Journal of Molecular Catalysis B: Enzymatic 81 (2012) 7–11
9
Table 1
Table 3
Real and coded (+ superior level, 0 intermediate, − inferior level) values for the
Effect of parameters estimates of CCD 23 for enzyme studied.
variables evaluated in the epoxidation reactions.
Variables
Effect
p-Value
Factors
−1
0
+1
Mean
Temperature
Amount of enzyme
Peroxide concentration
Temperature × Amount of enzyme
Temperature × Peroxide concentration
Amount of enzyme × Peroxide concentration
58.00
12.75
−0.75
37.25
6.25
6.25
−7.25
<0.0001*
0.0004*
0.4552
Temperature (◦C)
25
10
0.1
40
15
55
20
0.2
Amount of enzyme (%)a
Peroxide concentration (%)
0.15
0.0004*
0.0166*
0.0166*
0.0124*
a
By weight of oleic acid.
*
Statistically significant at 95% of confidence level.
thiosulfate titration of the sample spent; m is the mass, in g, of the
sample aliquot.
It can be checked in Table 2 that the best results are among
entries 5 and 8, turning evident the positive effect of the hydro-
gen peroxide concentration variable in the epoxide conversion.
This positive effect occurs within the analyzed range 0.1–0.2%. It is
important to highlight that previous tests performed in our labora-
the activity of the immobilized B. cepacia lipase. Several studies
have already reported the occurrence of this increased effect on
epoxide conversion with increasing concentration of hydrogen per-
oxide in the reaction medium [19,20]. H2O2 has been reported to
the surface of the protein seem to be most susceptible. The oxi-
side-chain to side-chain transfer reactions were already described,
transferring the initial site of oxidation to readily oxidized amino
acids [21].
Table 3 shows the estimative of the effects for the experimen-
tal design 23 and p values. Variables with p < 0.05 were significant
(10–20%), once it has presented a p > 0.05 (0.4552). Added to that,
the lowest lipase concentration (10%) did not harm the reaction rate
once the best result could be obtained at this percentage (entry 6,
Table 2). Focusing on the industrial applicability, this result is con-
The variable temperature promoted positive effect (12.75,
Table 3). By increasing the temperature the product formation rate
is also enhanced within the range studied. It is corroborated by
entries 6 and 8 (Table 2) which achieved the highest conversions
(88 and 83, respectively). However, the continuous increase in tem-
perature, there may be a gradual inactivation of the enzyme to total
inactivation, caused by protein denaturation by heat [22].
The experimental data have been adjusted to the proposed
model and its adequacy was performed by the analysis of variance
and parameter R2. Eq. (1) represents the mathematical model of
epoxystearic acid conversion depending on the variables.
3. Results and discussion
The reaction time is not generally considered as a variable in
ence on the system and on other variables, mistaking eventual
responses. However, we recognize that it is very significant for final
conversion rates. Based on that, we followed the literature data to
select the reaction time and fixed that on 3 h [15,16].
The central composite design (CCD) consisting of three vari-
ables and varying in two levels was used to identify the important
immobilized PSCI-Amano lipase from B. cepacia. Temperature,
amount of enzyme, and peroxide concentration were considered
critical variables (independent) and therefore assessed in planning
[17,18]. The CCD 23 was applied with triplicate central points for
calculating the experimental error. The three variables and their
real and coded levels for the enzyme evaluated are shown in Table 1.
3.1. Central composite design
The experimental designs and data analysis were carried out
using the software Statistica 6.0 (Statsoft Inc., USA), according to
the significance level established for obtaining the mathematical
ficients and associated probabilities, p(t), were determined by the
Student’s t-test; the model equation significance was determined
by the Fisher’s F-test.
Table 2 shows the 11 treatments considering the three variables
and the percentage yield conversion for each experiment. The first
eight treatments were used to determine the mathematical model
and refer to statistical design. Treatments 9–11 represent the trip-
licates of the central points for obtaining the experimental error.
Table 2
Experimental design and oleic acid epoxidation conversion rates in different tem-
perature, enzyme load and hydrogen peroxide load.
Y = 58.00 + 18.62H2O2 + 6.37T − 3.62E × H2O2 + 3.12T
Entriesa
Reaction
Enzyme
load (%)b
H2O2
(%)c
Conversion
(%)d
× E + 3.12T × H2O2
temperature (◦C)
the coded values of temperature, amount of enzyme and perox-
ide concentration, respectively. Statistical testing of the model was
performed by the Fisher’s statistical test for ANOVA (Table 4).
Table 4 of analysis of variance (ANOVA) shows the model valid-
ity by F test and the residue shows the magnitude of experimental
error. The F calculated (92.41) was higher than the F tabulated
(F5,5 = 5.05), showing the experimental model validity. The good-
ness of a model can be checked by the determination coefficient
(R2). The determination coefficient (R2 = 0.98) implies that the 98%
sample variation for epoxystearic acid production is attributed to
the independent variables and can be accurately explained by the
model.
1
2
3
4
5
6
7
8
9
−1
−1
−1
37
34
34
50
72
88
61
83
59
59
61
+1
−1
+1
−1
+1
−1
+1
0
−1
+1
+1
−1
−1
+1
+1
0
−1
−1
−1
+1
+1
+1
+1
0
10
11
0
0
0
0
0
0
a
Numbers were run in random order.
b
c
Enzyme load (%, relative to the weight of oleic acid).
Hydrogen peroxide load (%, relative to the mol).
Analyzed by GC–MS.
d