Immobilized laccase-based biosensor for the detection of disubstituted methyl and methoxy phenols – application of Box–Behnken design with response surface methodology for modeling and optimization of performance parameters

Figures & data

Figure 1. Mechanism of oxidation of 2, 6-dimethoxy phenol by laccase.

Figure 2. Biosensor configuration.

Figure 3. Optimum pH for biosensor performance.

Figure 4. Optimum temperature for biosensor performance.

Figure 5. Effect of molarity of buffer on biosensor response.

Figure 6. Effect of glutaraldehyde concentration on biosensor response in cross-linking method.

Table I. Factors and levels in Box–Behnken design.

	Coded-level variables
Variable	−1	0	1
X1: laccase (IU)	1	3	5
X2: BSA (mg)	1	2.5	4
X3: Gluteraldehyde – 5% (μl)	10	15	20

Table II. Coded-level combinations for a three-level, three-variable experimental design.

				Response (V)
Sl no.	X1	X2	X3	Predicted	Observed
1	−1	−1	0	0.6525	0.63
2	−1	1	0	0.2125	0.22
3	1	−1	0	0.8075	0.8
4	1	1	0	0.6875	0.71
5	−1	0	−1	0.935	0.9
6	−1	0	1	0.95	1
7	1	0	−1	1.15	1.1
8	1	0	1	1.365	1.4
9	0	−1	−1	0.8925	0.95
10	0	−1	1	0.8775	0.85
11	0	1	−1	0.4825	0.51
12	0	1	1	0.7275	0.67
13	0	0	0	0.9	0.89
14	0	0	0	0.9	0.9
15	0	0	0	0.9	0.91

Figure 7. Comparison between the observed values and the predicted values of BBD.

Table III. ANOVA table for the RSM model.

Source	Seq. SS	Adj. SS	DF	Adj. MS	F	P
Main	0.3817		3
Laccase (A)	0.1985	0.1985	1	0.1985	58.713	0.001
BSA (B)	0.1568	0.1568	1	0.1568	46.391	0.001
Glutaraldehyde (C)	0.0264	0.0264	1	0.0264	7.8254	0.038
2 – way	0.0525		3
AB	0.0256	0.0256	1	0.0256	7.574	0.040
AC	0.01	0.01	1	0.01	2.9586	0.146
BC	0.0169	0.0169	1	0.0169	5.0	0.076
Quadratic	0.5656		3
AA	0.0042	0.0019	1	0.0019	0.553	0.491
BB	0.4451	0.4082	1	0.4082	120.772	0.000
CC	0.1163	0.1163	1	0.1163	34.417	0.002
Regression	0.9998	0.9998	9	0.1111
Error	0.0169	0.0169	5	0.0034
Error pure	0.0002	0.0002	2	0.0001
Error LOF	0.0167	0.0167	3	0.0056
Total	1.0167		14

P<0.01, highly significant; 0.01 < P<0.05, significant; P>0.05, not significant.

Table IV. Regression statistics.

R^2	0.9834
Adj. R^2	0.9535
Std. Error	0.0581
F	32.866
Sig F	0.0006
FLOF	55.667
Sig FLOF	0.0177

Figure 8. Response surface plots showing the effects of factors (concentrations of laccase (A), BSA (B), and glutaraldehyde (C)) on the response.

Figure 9. Contour plot showing the ranges of concentration of laccase and BSA to obtain various response values taking concentration of glutaraldehyde constant.

Table V. Predicted and experimental value for the responses at the optimum conditions.

Concentration of laccase	5 IU
Concentration of BSA	2.5 mg
Concentration of glutaraldehyde	5%–20 μl
Response:  Predicted – 1.365 V  Experimental – 1.359 V

Figure 10. Calibration graph for 2, 6-dimethoxy phenol with test biosensor using laccase (5 IU) immobilized with BSA (2.5 mg) and glutaraldehyde (5%–20 μl).

Figure 11. Operational stability of laccase-based enzyme electrode for various immobilization methods on nylon membrane.

Figure 12. Calibration graph of laccase-based amperometric biosensor for 2,6-dimethoxy phenol using biosensor employing various immobilization methods on nylon membrane.

Table VI. Calibration data for methyl and methoxy disubstituted phenolic compounds using laccase biosensor employing various immobilization methods on nylon membrane.

Method of immobilization	LOD (×10^−5 M)	Response time (min.)	Linearity range (×10^−5 M)	R^2	Slope
Co-cross-linking
2,6-dimethoxyphenol	0.3	2	0.5–8	0.997	0.258
2,4-dimethoxyphenol	0.9	2	1–15	0.987	0.178
2,3-dimethoxyphenol	0.9	2	2–15	0.994	0.0190
3,4-dimethoxyphenol	2	5	4–60	0.997	0.039
2,3-dimethylphenol	2	3	4–40	0.993	0.068
2,6-dimethylphenol	0.8	2	1–10	0.990	0.219
2,4-dimethylphenol	1	4	2–30	0.991	0.070
3,4-dimethylphenol	2	5	6–60	0.994	0.037
Entrapment
2,6-dimethoxyphenol	1	4	2–40	0.999	0.051
2,4-dimethoxyphenol	2	5	6–50	0.989	0.016
2,3-dimethoxyphenol	2	4	5–60	0.979	0.018
3,4-dimethoxyphenol	8	8	10–100	0.994	0.005
2,3-dimethylphenol	5	6	15–90	0.986	0.01
2,6-dimethylphenol	3	4	5–50	0.978	0.020
2,4-dimethylphenol	4	6	8–90	0.990	0.011
3,4-dimethylphenol	10	8	15–120	0.980	0.005
Cross-linking
2,6-dimethoxyphenol	3	3	10–100	0.985	0.015
2,4-dimethoxyphenol	5	3	30–140	0.991	0.009
2,3-dimethoxyphenol	4	3	25–150	0.982	0.011
3,4-dimethoxyphenol	15	6	40–240	0.986	0.001
2,3-dimethylphenol	10	4	35–190	0.998	0.004
2,6-dimethylphenol	5	3	15–120	0.985	0.015
2,4-dimethylphenol	8	5	30–180	0.987	0.005
3,4-dimethylphenol	15	6	50–250	0.979	0.002

Figure 13. Calibration graph of laccase-based amperometric biosensor for different methyl and methoxy substituted phenols using co-cross-linking with BSA and glutaraldehyde.

Table VII. Statistical analysis of biosensor response to varying concentration of catechol for the various type of immobilization.

Type of immobilization	Concentration of catechol (μM)	Standard deviation (SD)	Coefficient of variation (CV)
Co-cross-linking	80	0.03	0.01463
	100	0.043	0.01535
Entrapment	80	0.019	0.038
	100	0.0245	0.04083
Cross-linking	80	0.0038	0.03167
	100	0.0045	0.02368

Table VIII. A comparison of properties of various enzyme based phenol biosensors.

Enzyme used	Support for immobilization	Method of immobilization	Substrate	Detection limit (M)	Linearity (M)	Stability (Months)	Ref.
Laccase	Nylon membrane	Co-cross-linking	2,6-dimethoxy  phenol	0.09 × 10^−5	0.5 × 10^−5 to 8 × 10^−5	5	Present
Laccase	Poly ether sulphone  membrane	Physical adsorption	Catechol	1 × 10^−5	1 × 10^−5 to 8 × 10^−5	0.5	Gomes and Rebelo  (2003)
Laccase	Epoxy resin	Entrapment	Guaiacol	3 × 10^−7	5 × 10^−7 to 5 × 10^−5	10	Chawla et al. (2011)
Laccase	Carbon fibers	Cross-linking	Catechol	NR	1 × 10^−6 to 90 × 10^−6	NR	Freire et al. (2001)
Tyrosinase	Egg shell membrane	Cross-linking	Dopamine  hydrochloride	2.5 × 10^−5	5 × 10^−5 to 25 × 10^−5	6	Tembe et al. (2008)
Tyrosinase	Cellophane membrane	Co-cross-linking	Catechin	NR	1 × 10^−2 to 8 × 10^−2	1	Abhijith et al. (2007)
PPO from  banana	PVA membrane	Cross-linking	L-Dopa	50 × 10^−8	50 × 10^−8 to 20 × 10^−6	6	Narang et al. (2013)

Table IX. Simulated effluent sample analysis using test biosensor.

			Phenolic compound content (experimental) μg/ml
Sample	Different phenol content	Phenolic compound content (theoretical) μg/ml	Biosensor	HPLC
Simulated effluent 1	2,6-dimethoxy phenol –  0.462 μg 2,6-dimethyl phenol – 0.366 μg	0.788	0.78 ± 0.035 (1.02)	0.80 (1.52)
Simulated effluent 2	2,4-dimethyl phenol –  0.610 μg 2,3-dimethyl phenol – 0.732 μg	1.342	1.30 ± 0.046 (3.129)	1.36 (1.341)
Simulated effluent 3	3,4-dimethoxy phenol –  0.77 μg 3,4-dimethyl phenol – 0.488 μg	1.258	1.22 ± 0.026 (3.02)	1.26 (0.15)

Percentage error of instrumental methods in comparison with theoretical values is represented in parenthesis. Results are expressed are ± SD, n=5.

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