Detection of Copper (II) in Aqueous Solution by Immobilized Urease ...

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in aqueous solution. Process variables were optimized by Central Composite De- sign using MINITAB. ®. 15 software. Results predicted by the design were ...

Biotechnology and Bioprocess Engineering 2009, 14: 474-481 DOI/10.1007/s12257-008-0292-0

Detection of Copper (II) in Aqueous Solution by Immobilized Urease Obtained from Agro-waste: Optimization of Process Variables Mahe Talat* Department of Biochemistry, Faculty of Science, Banaras Hindu University, Varanasi 221005, India ^Äëíê~Åí= Urease was used for estimation of Cu2+ in aqueous solution. Process variables were optimized by Central Composite De® sign using MINITAB 15 software. Results predicted by the design were close to experimental value indicating suitability of the model. 3D surface plot and isoresponse contour plot were helpful in predicting results by performing limited set of experiments. Urease was extracted from discarded seeds of pumpkin to apparent homogeneity by heat fractionation and gel 2+ filtration. Homogeneous enzyme preparation was further immobilized in 3.5% alginate. Effect of Cu on the activity of soluble and immobilized enzyme was investigated. Enzyme inhibition was biphasic, irreversible, and non-competitive (Ki = 1.06 µM). © KSBB hÉóïçêÇëW=rêÉ~ëÉI=^äÖáå~íÉI=fããçÄáäáò~íáçåI=oÉëéçåëÉ=pìêÑ~ÅÉ=jÉíÜçÇçäçÖó

INTRODUCTION Due to the wide existence of copper in environment it is toxic to plants and animals both at high concentration. For instance, research has indicated that copper could induce the damage of nitrogen metabolism by decreasing the activity of nitrate reductase and the total content of chlorophyll at high concentration. Copper is commonly found as Cu2+ in natural waters and the free Cu2+ is potentially very toxic to aquatic life, both acutely and chronically. It also made large influence on the growth of plants marked by the shortened root length, fewer leaves, and decline in plants biomass [1]. In addition, the exposure at sublethal copper concentration would also influence the pathway of metabolic glyoxalases of Scapharca inaequivalvis [2]. Superfluous absorption of copper could affect the liver of goats [3]. Recently numerous studies have focused on the usage of organic substance such as aquatic plant and humic substance to remove heavy metals from waste water [4-6]. Nowadays, analytical methods are well established for environmental monitoring. However, a paradoxical situation G`çêêÉëéçåÇáåÖ=~ìíÜçê= Tel: +91-0542-2307323 Fax: +91-0542-2368174 e-mail: [email protected]=

has emerged because most of the analytical methodologies employed to investigate environ mental problems by generating chemical wastes, are resulting in an environmental impact [7]. Nowadays, in the development of a new analytical procedure, the amount and toxicity of wastes are as important as any other analytical feature [8]. Several analytical techniques are actually available to analyze Cu2+ concentration in environmental water with different matrices, such as atomic absorption spectrometry [9-11], inductively coupled plasma-atomic emission spectroscopy [12,13] stripping voltammetry on a mercury drop [14-17], differential pulse anodic stripping voltammetry [18], X-ray fluorescence [19], and atomic fluorescence spectrometry [20]. Besides the well-known advantages of these instrumental techniques (precision, accuracy, sensitivity, selectivity, etc.), all of them present a series of disadvantages, such as high investment cost, complexity, and difficulty in situ application. Numbers of enzymes have been used for the heavy metal detection [21]. Immobilized enzymes offer wide application because they are ecofriendly and can be reused [22]. Urease for the detection of heavy metal toxicity appears to be simple and sensitive tool for the quantitation, which is a method based on the strong inhibitory effect of these metal ions on enzyme activity [23,24]. Urease based biosensors is

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of great importance due to its high sensitivity, stability, low price, and short response time. Different methods and matrices have been employed for the urease immobilization for example, gelatin [25], Agarose gel [26], chitosan beads [27], DEAE cellulose paper strips [28], etc. Techniques used for immobilization, entrapment in natural biopolymers are favored for various reasons. Alginate is the most frequently employed material for the elaboration of the polymer matrix and outer biocompatible membrane because of its mild gelling, biocompatibility, and biodegradability properties [29]. Because of this property alginate has therefore, been widely used to immobilize a variety of cell types and enzymes [30]. Since immobilization work has mostly been attempted on expensive sources of urease like Jackbean, pigeon-pea, etc [23,31]. Therefore, urease from an un-utilized and cheaper source for wide applications is always required. Using simple steps, urease was purified to apparent homogeneity from an agricultural waste, i.e., the dehusked seeds of pumpkin and immobilized into alginate beads .The effect of Cu2+ ion concentration and time of interaction which are affecting the activity of immobilized urease were evaluated by using two-level-two-factor full factorial Central Composite Design (CCD) model [32-34]. The predicted results by this model were then compared with the experimental results.

MATERIALS AND METHODS `ÜÉãáÅ~äë

Pumpkin seeds were purchased from the local market and dehusked just before soaking. Tris was obtained from Boehringer Mannheim Gmbh, Germany. Bovine serum albumin was obtained from Sigma Chemical Co., USA. Sephadex G200 was from Pharmacia Fine Chemicals, Uppsala, Sweden. Urea (enzyme grade), Metal salt, Nessler’s and FolinCiocalteau reagents were from Qualigens Fine Chemicals, Mumbai. All other reagents were analytical grade chemicals either from BDH or E. Merck, India. fëçä~íáçå=çÑ=rêÉ~ëÉ

Urease was isolated and purified in 50 mM Tris-acetate buffer (pH 7.5) from dehusked seeds of pumpkin to electrophoretic homogeneity as described earlier [35]. mìêáÑáÅ~íáçå=çÑ=rêÉ~ëÉ=

The following steps were taken to purify the crude enzyme preparation. eÉ~í=íêÉ~íãÉåí=~åÇ=dÉä=cáäíê~íáçå

Crude enzyme preparation was heated at 48 ± 0.1°C in a water bath for 10 min and was immediately chilled in crushed ice. This was centrifuged at 21,000 × g for 15 min at 0~4°C. The supernatant was collected. Heat fractionated

enzyme was gel filtered through Sephadex G-200 column. Various 2.0 mL fractions containing urease activity were pooled and concentrated against solid sucrose. The enzyme preparation (sp act 353 ± 12 U/mg protein), which showed a single enzyme and protein band on native 7.5% PAGE (at pH 8.3), was employed for the study [35]. `~äÅáìã=^äÖáå~íÉ=_É~Çë=mêÉé~ê~íáçå

A 3.5% solution of sodium alginate was prepared in 25 mM Tris Acetate buffer (pH 7.5) and was stored at 4ºC. Suitably diluted enzyme solution (0.7 mg Protein/mL) was mixed in a chilled alginate solution and dropped in a continuously stirring calcium chloride solution (400 mM). The beads formed were thoroughly washed with the buffer and stored at 4°C [35]. rêÉ~ëÉ=^Åíáîáíó=^ëë~ó=

Enzyme activity was assayed in 50 mM Tris-acetate buffer (pH 8.0). An aliquot (0.8 mL) of buffer and 1.0 mL of 250 mM urea in the same buffer were brought to 30°C. The reaction was started by adding 0.2 mL of suitably diluted enzyme. After 10 min, 1.0 mL of 10% trichloroacetic acid was added to stop the reaction. The total reaction mixture was transferred to a measuring flask (50 mL) and the volume was made to 50 mL with distilled water after adding 1.0 mL of Nessler’s reagent. The amount of ammonia liberated was measured at 405 nm in a Spectronic 21UVD spectrophotometer. A unit of enzyme activity was defined as the amount of enzyme required to liberate 1 μmol of ammonia in 1 min under the test conditions defined above (30°C, 50 mM Tris-acetate buffer, pH 8.0, 250 mM urea). Protein was estimated by the method of Lowry et al. [36] with Folin-Ciocalteau reagent calibrated with crystalline bovine serum albumin. aÉíÉêãáå~íáçå=çÑ=íÜÉ=háåÉíáÅ=`çåëí~åíë

The activity as a function of substrate concentration was measured for inhibited (metal ion) and uninhibited enzymes; the Km (an approximate measurement of the affinity of the substrate for the enzyme) and Vmax (the maximal velocity when enzyme is saturated with the substrate) were determined by using Lineweaver-Burk plot. bÑÑÉÅí=çÑ=`ì +=fçåë=çå=íÜÉ=^Åíáîáíó O

A stock solution of Cu2+ was made in 50 mM Tris-acetate buffer (pH 8.0) and diluted with the same buffer as required. The activity of suitably diluted enzyme was determined in the presence of varying concentrations of Cu2+ added in the standard assay mixture individually. For the direct effect of Cu2+, enzyme alone was incubated with the desired concentration of inhibitor for 10 min at 30°C and the treated enzyme was assayed for the activity. The results reported are the mean of 5~8 replicate experiments carried out with a fresh batch of purified enzyme.

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The optimization of enzymatic detection of Cu2+ in aqueous solution was studied with a standard response surface methodology (RSM) which is a collection of mathematical and statistical techniques that are useful for modeling and analysis of problems in which a response of interest is influenced by several variables. Factorial design is the most useful scheme to estimate the RSM. A variety of factorial designs are available to accomplish this task. The most appropriate among them is Central Composite Design (CCD). This method is suitable for fitting a quadratic surface and it helps to optimize the effective parameters with a minimum number of experiments, and also to analyze the interaction between the parameters. It was reported that if the factorial is a full factorial then, α = [2k]1/4

(1)

Since in the present investigation two factors i.e., Cu2+ concentration and interaction time which are affecting the result. Thus, k = 2. So, α = 1.414. Furthermore, the total number of experimental point (N) in a CCD can be calculated by the following equation, N = 2k + 2k + x0

(2)

Where, N is the number of experimental runs, k is the number of variables, and x0 is the number of center points. Thus, for this design the total number of experimental runs will be 14 because k is 2 and x0 is 6. Data from the central composite design for the optimization of enzymatic detection of Cu2+ in aqueous solution was subjected to a second-order multiple regression analysis to explain the behavior of the system using the least squares regression methodology to obtain the parameter estimators of the mathematical model. Y = β0 + ΣβiXi + ΣβiiXi2 + Σ ΣβijXiXj + є

(3)

Where, Y is response, β0 is the constant, βi the slope or linear effect of the input factor Xi, βij the linear interaction effect between the input factor Xi and Xj, and є is the residual term. MINITAB® Release 15, developed by Minitab Inc. (USA), a statistical software package, was used for regression analysis of the data obtained and to estimate the coefficient of regression equation. ANOVA (analysis of variance), which is statistical testing of the model in the form of linear terms, squared terms, and the interaction, was also utilized to test the significance of each term in the equation and the goodness of fit of the regression model obtained. This response surface model was also used to predict the result by Isoresponse contour plots and three dimensional surface plots, in order to study the individual and cumulative effects of the variables and the mutual interactions between the variables on the dependent variable. Optimization curves were plotted in order to confirm the experimental results and to achieve

cáÖK=NK Inactivation of pumpkin urease by Cu2+. Suitably diluted enzyme (14.8~16.0 U/mL, 7~9 µg protein/mL) was incubated with varying concentrations (0.03~10-6 mM) of Cu2+ for 10 min at 30°C standard assay mixture and then assayed for the activity.

the required removal of Cu2+ by choosing the predicted conditions.

RESULTS AND DISCUSSION bÑÑÉÅí=çÑ=`ì +=fçåë=çå=pçäìÄäÉ=~åÇ=fããçÄáäáòÉÇ= rêÉ~ëÉ= O

The effect of Cu2+on the activity of pumpkin urease was studied in the concentration range that had measurable inhibition. The desired concentration (0.03~10-6 mM Cu2+) was added into the standard assay mixture and assayed the activity after incubating the enzyme solution for 10 min. The results revealed a concentration-dependent inhibition of the activity. At lower concentration of metal ion, the inhibition was less and it increased with increasing level of metal ion. About 50% inhibitions were observed at 10-2 mM, which increased to about 63% at 0.03 mM of Cu2+ (Fig. 1). Further increase in the concentration reported almost complete loss in the enzyme activity. For the calculation of inhibition constant Ki, urea concentration in the assay mixture was varied from 2~125 mM in the presence of inhibitor. The results, when expressed by a Lineweaver-Burk double reciprocal plot of substrate concentration versus velocity (absorbance at 405 nm), revealed a non-competitive inhibition. The Ki was found to be 1.06 μM. Time-dependent interaction of pumpkin (soluble) urease was studied by incubating the enzyme with 0.001 mM Cu2+ (Fig. 2). Followed by incubation of enzyme with Cu2+, aliquots were withdrawn at specific time intervals and assayed for activity. The results revealed a biphasic, time-dependent inhibition in the activity. There was an initial rapid loss of activity, approximately 37% in initial 10 min followed by a slow and sustained inhibition. Time-dependent hydrolysis of urea by alginate immobilized pumpkin urease was studied by incubating the enzyme

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cáÖK=OK Inactivation of pumpkin urease by Cu2+. Suitably diluted enzyme (14.8~16.0 U/mL, 7~9 µg protein/mL) was incubated with 0.001 mM of Cu2+ for 10 min at 30°C in 50 mM Trisacetate buffer (pH 8.0) and aliquots were withdrawn at specified intervals and were assayed for the activity.

designed for analysis of urea and Cu2+ based on the immobilized urease also reported the increase of the enzyme stability on immobilization against the metal ion inactivation. This could be attributed to the decrease of the thiol group’s reaction ability after immobilization [38]. Also, this less inhibition at a higher concentration of inhibitor requiring longer time duration might be due to the protection provided to the enzyme by alginate entrapment. Probably because of this, the penetration of the inhibitor to the essential -SH groups of the enzyme active site in the immobilized state is also impeded [37]. Prakash and Upadhaya reported that the alginate immobilized watermelon urease showed improved stability in organic media as compared to the soluble form of the enzyme which is a unique and interesting phenomenon [39]. It gives a scope for studying the use of enzyme in non-aqueous solvents. Like the previously published reports, here also interaction of the metal ion with the enzyme protein was so strong that the inhibition could not be reversed by dialysis (50 mM Trisacetate buffer, pH 8.0, 4~6°C, and 24 hours). Similar irreversible interaction has also been shown for jack bean urease and Hg2+ ion inhibition [31,35]. The enzyme requiring the presence of free-SH groups are generally inhibited by heavy metallic ions, because it forms the insoluble sulfides [40]. It has been established that all the -SH groups of the enzyme do not react simultaneously. The pigeon pea urease when titrated with 5,5′-dithiobis-(2nitrobenzoate) reported the presence of two types of free sulfhydryl groups in the enzyme protein; one group being easily available while the other was titrable only after protein denaturation [41]. These findings of half-and-half distribution of sites in the enzyme were further supported by the biphasic kinetics of thermal inactivation of watermelon urease [42]. oÉëéçåëÉ=pìêÑ~ÅÉ=c~Åíçêá~ä=aÉëáÖå=Ñçê=íÜÉ= = léíáãáò~íáçå=çÑ=íÜÉ=mêçÅÉëë

cáÖK=PK Time-dependent hydrolysis of urea by alginate immobilized pumpkin urease. Suitably diluted enzyme (12.8~14.7 U/mL, 8.0~8.5 µg protein/mL was immobilized and one bead 3 µg protein/bead) was incubated in the presence of 0.005 mM of Cu2+ added in the standard assay mixture. The samples withdrawn at different time intervals were assayed for the ammonia formed using Nessler’s reagent.

in absence of Cu2+ in the standard assay system. The urea hydrolysis in the absence of Cu2+ progressed with time. But, immobilized urease was found to be fairly active at higher concentration of inhibitor i.e., 0.005 mM of Cu2+. The immobilized enzyme was inhibited approximately 42% in 15 min at 0.005 mM Cu2+ which showed improved stability towards the inhibitor (Fig. 3). Such enhanced stability has also been reported for immobilized Citrullus vulgaris urease on cyanuric chloride-DEAEcellulose ether towards inhibitory effect of heavy metal ions [37]. A flow-injection system

The parameter which were affecting the detection of Cu2+ by immobilized urease viz., concentration of Cu2+ and interaction time, and each parameter was having one lower value and one higher value. Thus for the two-level-two-factors, full factorial Central Composite Design (CCD), 14 experimental values were required as per the requirement of the design [43]. Experiments were performed where the concentration of Cu2+ was changed from 10-5~0.1 mM, the interaction time from 2~30 minutes, and a residual activity obtained varied from 23~99%. Table 1 shows significant change in enzyme activity, implying that both these variables were affecting the enzyme activity. fåíÉêéêÉí~íáçå=çÑ=íÜÉ=oÉÖêÉëëáçå=^å~äóëáë

The results predicted by full factorial central composite design for two variables, using Minitab software along with experimental results are given in Table 1. It has been observed that the predicted results were very much close to

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q~ÄäÉ=NK=Predicted % residual activity of urease after processing experimental data by MINITAB® software 2

Std order

Run order

Pt type

Blocks

Concentration of Cu + (mM)

Time (min)

1 5 4 2 6 7 3 10 11 14 13 8 9 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 0 1 1 0 0 1 -1 -1 0 0 -1 -1 0

1 1 1 1 1 1 1 2 2 2 2 2 2 2

0.10000 0.01000 0.00100 0.00010 0.00001 0.00100 0.00100 0.00100 0.00100 0.00100 0.00100 0.00100 0.00100 0.00100

10 10 10 10 10 2 4 6 8 10 15 20 25 30

Experimental % residual activity 23.0 50.5 69.7 73.0 77.0 99.0 92.0 82.7 73.0 63.0 54.0 53.0 52.0 52.0

Predicted % residual activity 23.0075 49.6747 69.3112 71.4446 71.6596 99.1566 90.4313 82.5486 75.5085 69.3112 57.5046 50.9647 49.6916 53.6852

q~ÄäÉ=OK Estimated regression coefficients for % residual activity of urease versus Cu2+ concentration (mM), time (min) in coded unit Term Constant Concentration (mM) Time (min) Concentration (mM) × Concentration (mM) Time (min) × Time (min)

Coef

SE Coef

T

P

-13.81 -24.33 -22.74 -47.60 -20.65

10.392 1.837 1.693 10.716 2.980

-1.329 -13.243 -13.427 -14.442 -16.927

0.217 0.000 0.000 0.002 0.000

S = 3.41561, PRESS = 23710248, R-Sq = 97.93%, R-Sq (pred) = 0.00%, R-Sq (adj) = 97.01%.

q~ÄäÉ=PK= Analysis of variance for % residual activity versus Cu2+ concentration (mM), time (min) in coded units Source

DF

Seq SS

Adj SS

Adj MS

F

P

Regression Linear Square Residual error Lack-of-fit Pure error Total

4 2 2 9 8 1 13

4967.55 4080.78 886.77 105.00 82.55 22.45 5072.54

4967.55 3820.80 886.77 105.00 82.55 22.45

1241.89 1910.40 443.38 11.67 10.32 22.45

106.45 163.75 38.01

0.000 0.000 0.000

0.46

0.822

experimental results indicating the applicability of the proposed design. Furthermore, the response surface regression analysis has given the coefficients for all the terms in the model and each term estimated independently Table 2. The T value, which is used to determine the significance of the regression coefficients of the parameters and the P-value, is defined as the smallest level of significance leading to rejection of null hypothesis. The effect of these two factors i.e., Cu2+ concentration and interaction time was found to be highly significant (P is zero) on the activity of the urease. The coefficient of quadratic term of concentration was also found to be highly significant. The R-Sq value was 97.93% indicating that the correlation is best suited for predicting the performance of the system and that only 2.07% of the total variation cannot be explained by the model.

In addition to this, analysis of variance (ANOVA) of the data is given in Table 3. The ANOVA demonstrates that the regression model was highly significant, as it is evident from the calculated Fisher’s ‘F’ value (106.45) and a very low probability value (P = 0.00). Because it has been reported that higher value of F indicates that most of the variation in the response can be explained by the regression equation and the associated P value is used to estimate whether F is large enough to indicate statistical significance [44]. If value of F is high and the value of P is less than 0.05, that means the model is statistically significant. It was observed from the table that the coefficients for the linear (P = 0.00) and square (P = 0.00) effects were highly significant and thus confirm the applicability of the predicted model. The ANOVA in Table 3 also shows a term for residual error, which measures

Biotechnol. Bioprocess Eng. QTV=

q~ÄäÉ=QK Estimated regression coefficients for % residual activity of urease versus Cu2+ concentration (mM), time (min) using data in uncoded units Term Constant Concentration (mM) Time (min) Concentration (mM) × Concentration (mM) Time (min) × Time (min)

Coef 111.097 -2391.34 -4.99470 19045.8 0.105335

cáÖK=RK Contour plot of the combined effect of concentration of Cu2+ and interaction time on residual activity of the enzyme. cáÖK=QK 3D surface plot of the combined effect of concentration of Cu2+ and interaction time on residual activity of the enzyme.

the amount of variation in the response data left unexplained by the model. A response surface model is proposed based on the regression coefficients Table 4 (for uncoded value) for the percentage residual activity of the enzyme. Y = 111.097 − 2391.34 × Concentration of Cu2+ − 4.995 × Time + 19045.8 × Concentration of Cu2+ × Concentration of Cu2+ + 0.105 × Time × Time

(4)

Where, Y is the predicted response variable. The predicted values were found to be very closer to the experimental results Table 1. fåíÉêéêÉí~íáçå=çÑ=Pa=pìêÑ~ÅÉ=mäçíë

The surface plot in Fig. 4 is a three dimensional graph where percentage residual activity of enzyme has been predicted by simultaneously varying Cu2+ concentration from 10-5~0.1 mM and time from 2~30 minutes. From the 3D surface plot, it is also clear that percentage residual enzyme activity of 99% could be achieved when concentration of Cu2+ will be 0.001 mM at the interaction of 2 min. The surface plot is not only describing individual effect of variables but also cumulative effect of these two test variable on the response [45]. fëçêÉëéçåëÉ=`çåíçìê=mäçí

The isoresponse contour plot (Fig. 5) is a graphical technique for representing the individual and interactive effect of

the variables on the residual activity of enzyme. Thus contour (constant z- slices) indicates the percentage residual activity, which is obtained on 2-D format (concentration of Cu2+ on x-axis and interaction time on y-axis).

CONCLUSION Present work was intended to employ an inexpensive but an excellent source to yield an economical urease preparation for extensive applications. The urease obtained from agro-waste (seeds of pumpkin) is comparable with the expensive sources of urease. Hence, urease obtained from the discarded seeds of pumpkin revealed its suitability for the detection of Cu2+ in water, industrial effluents, soil, etc. The study conducted and results reported above suggest the wide application of urease. Also, with the help of experimental results a model was designed by Statistical and Graphical technique, Response Surface Methodology (RSM) where two-level-two-factor (22) was used. The predicted value thus obtained using MINITAB software has been found close to the experimental value indicating the suitability of the model. Received December 4, 2008; accepted March 21, 2009

REFERENCES 1. Xiong, Z. T., C. Liu, and B. Geng (2006) Phytotoxic effects of copper on nitrogen metabolism and plant growth in Brassica pekinensis Rupr. Ecotoxicol. Environ. Saf. 64: 273-280. 2. Antognelli, C., F. Baldracchini, and R. Frosini (2006)

QUM=

3.

4. 5. 6. 7. 8. 9. 10. 11.

12.

13.

14. 15.

16. 17.

Effect of exposure to sublethal copper concentration on glyoxalase system enzymes in specimens of Scapharca inaequivalvis. Biochem. Syst. Ecol. 34: 275-281. Solaiman, S. G., M. A. Maloney, and M. A. Qureshi (2001) Effects of high copper supplements on performance, health, plasma copper, and enzymes in goats. Small Ruminant Res. 41: 127-139. Kapoor, A. and T. Viraraghavan (1997) Heavy metal biosorption sites in Aspergillus niger. Bioresour. Technol. 61: 221-227. Frenkel, A. I., G. V. Korshin, and A. L. Ankudinov (2000) XANES study of Cu2+-binding sites in aquatic humic substances. Environ. Sci. Technol. 34: 2138-2142. Hsu, J. H. and S. L. Lo (2001) Effect of composting on characterization and leaching of copper, manganese, and zinc from swine manure. Environ. Pollut. 114: 119-127. Anastas, P. T. (1999) Green chemistry and the role of analytical methodology development. Crit. Rev. Anal. Chem. 29: 167-175. Rocha, F. R. P , J. A. Nobrega, and O. F. Filho (2001) Flow analysis strategies to greener analytical chemistry. Green Chem. 3: 216-220. Wolf, W. R. and K. K. Stewart (1979) Automated multiple flow injection analysis for flame atomic absorption spectrometry. Anal. Chem. 51: 1201-1205. Fukamachi, K. and N. Ishibashi, (1980) Flow injectionatomic-absorption spectrometry with organic solvents. Anal. Chim. Acta. 119: 383-387. Fang, Z., S. Xu, and S. Zhang (1984) The determination of trace amounts of heavy metals in waters by a flowinjection system including ion-exchange preconcentration and flame atomic absorption spectrometric detection. Anal. Chim. Acta. 164: 41-50. Jacintho, A. O., E. A. G. Zagatto, H. Bergamin, F. F. J. Krug, B. F. Reis, R. E. Bruns, and B. R. Kowalski (1981) Flow injection systems with inductively-coupled argon plasma atomic emission spectrometry Part 1: Fundamental Considerations. Anal. Chim. Acta. 130: 243-255. Zagatto, E. A. G., A. O. Jacintho, F. F. J. Krug, B. F. Reis, R. E. Bruns, and M. C. U. Araujo (1983) Flow injection systems with inductively-coupled argon plasma atomic emission spectrometry Part 2: The generalized standard addition method. Anal. Chim. Acta. 145: 169178. Ruzicka, J and E. H. Hansen (1978) Flow injection analysis Part X: theory, techniques, and trends. Anal. Chim. Acta. 99: 37-76. Hu, A., R. E. Dessy, and A.Graneli (1983) Potentiometric stripping with matrix exchange techniques in flow injection analysis of heavy metals in groundwaters. Anal. Chem. 55: 320-328. Anderson, L., D. Jagner, and M. Josefson (1982) Potentiometric stripping analysis in flow cells. Anal. Chem. 51: 1371-1376. Janata, J. and J. Ruzicka (1982) Combination of flow injection analysis and voltammetry. Anal. Chim. Acta. 139: 105-115.

18. Gillain, G., G. Duyckaerts, and A. Disteche (1979) Direct and simultaneous determinations of Zn, Cd, Pb, Cu, Sb, and Bi dissolved in sea water by differential pulse anodic stripping voltammetry with a hanging mercury drop electrode. Anal. Chim. Acta. 106: 23-27. 19. Lau, O. W and S. Y. Ho (1993) Simultaneous determination of traces of iron, cobalt, nickel, copper, mercury, and lead in water by energy-dispersive x-ray fluorescence spectrometry after preconcentration as their piperazino1,4-bis(dithiocarbamate) complexes. Anal. Chim. Acta. 280: 269-277. 20. Rigin, V. (1993) Simultaneous atomic fluorescence spectrometric determination of traces of iron, cobalt, and nickel after conversion to their carbonyls and gas-phase atomization by microwave-induced plasma. Anal. Chim. Acta. 283: 895-901. 21. Jung, K., G. Bitton, and B. Koopman (1995) Assesment of urease inhibition assays for measuring toxicity of environmental samples. Wat. Res. 29: 1929-1933. 22. Duran, N., M. A. Rosa, A. D. Annibale, and L. Gianfreda (2002) Applications of laccases and tyrosinases (phenoloxidases) immobilized on different supports. Enzyme Microb. Technol. 31: 907-931. 23. Krawczyk, B. (2003) Simple optical fibre biosensor based on immobilized enzyme for monitoring of trace heavy metal ions. Anal. Bioanal. Chem. 376: 1104-1110. 24. Prakash, O. and D. K. Vishwakarma (2001) Inhibition of urease from seeds of water melon by heavy metal ions. J. Plant Biochem. Biotech.10: 147-149. 25. Srivastava, P. K., A. M. Kayastha, and A. Srinivasan (2001) Characterization of gelatin-immobilized pigeonpea urease and preparation of a new urea biosensor. Biotech. Appl. Biochem. 34: 55-62. 26. Prakash, O., S. Puliga, and L. S. B. Upadhaya (2007) Immobilization of watermelon urease in agarose gel for urea estimation. Biotechnol. Bioprocess Eng. 12: 131135. 27. Kayastha, A. M. and P. K Srivastava (2001) Pigeon pea (Cajanus cajan) urease immobilized on glutaraldehyde activated chitosan beads and its analytical applications. Appl. Biochem. Biotechnol. 96: 41-53. 28. Reddy, K. R. C., P. K Srivastava, P. M. Dey, and A. M Kayastha, (2004) Immobilization of pigeon pea (Cajanus cajan) urease on DEAE-cellulose paper strip for urea estimation. Biotech. Appl. Biochem. 39: 323-327 29. Smidsröd, O. and S. B Gudmund (1990) Alginate as immobilization matrix for cells. TIBTEC 8: 71-78. 30. Kierstan, M. and Bucke, C. (2000) The immobilization of microbial cells, sub-cellular organelles, and enzymes in calcium alginate gels. Biotechnol. Bioeng. 67: 726736. 31. Krajewska, B. W., Zaborska and M. J. Chudy (2004) Multistep analysis of Hg2+ ion inhibition of jack bean urease. Inorg. Biochem. 98: 1160-1168. 32. Khuri, A. and J. Cornell (1987) Response surfaces Design and Analysis. Marcel Dekker Inc., NY, USA. 33. Mason, B. H., R. T. Haftka, E. R. Johnsona and, G. L. Farley (1998) Variable complexity design of composite

Biotechnol. Bioprocess Eng. QUN=

34.

35.

36. 37.

38.

39.

fuselage frames by response surface techniques. Thin Walled Structures 32: 235-261. Heck, J. X., S. H. Flôresa, P. F. Hertza, and M. A. Z. Ayub (2006) Statistical optimization of thermo-tolerant xylanase activity from amazon isolated bacillus circulans on solid-state cultivation. Biores. Tech. 97: 19021906. Prakash, O., M. Talat, S. H. Hasan, and R. K. Pandey (2008) Enzymatic detection of mercuric ions in groundwater from vegetable wastes by immobilizing PUMPKIN (Cucumis melo) Urease in calcium alginate beads. Biores. Tech. 99: 4524-4528. Lowry, O. H., N. J. Rosebrough, A. L Farr, and R. J. Randall (1951) Protein measurement with folin-phenol reagent. J. Biol. Chem.193: 265-275. Fahmy, A. S., V. C Bagos, and T. M. Mohammed (1998) Immobilization of Citrullus vulgaris urease on cyanuric chloride DEAE-Cellulose ether: preparation and properties. Biores. Tech. 64: 121-129. Godjevargova. T. I., K. I. Gabrovska, and N. V. Ivanova (2005). Copper (II) determination by immobilized urease inhibition in a spectrometric flow-injection system. Biotechnol. Biotechnol. Eq. 19: 202-210. Prakash, O. and L. S. B. Upadhyay (2006) Immobilization imparts stability to watermelon urease to work in

40. 41.

42. 43.

44.

45.

water miscible organic media. Biotechnol. Bioprocess Eng.11: 140-145. Krajewska, B. (1991) Urease immobilized on chitosan membrane. Inactivation by heavy metal ions. J. Chem. Technol. Biotechnol. 52: 157-162. Srivastava, P. K. and A. M. Kayastha (2000). Significance of sulfhydryl groups in the activity of urease from pigeon pea (Cajanus cajan L.) seeds. Plant Sci. 159: 149-158. Prakash, O. and G. Bhushan (1998) Molecular asymmetry in urease of watermelon (Citrullus vulgaris). J. Plant Biochem. Biotechnol. 7: 53-55. Prakash, O., M. Talat, S. H. Hasan, and R. K Pandey (2008). Factorial design for the optimization of enzymatic detection of cadmium in aqueous solution using immobilized urease from vegetable waste. Biores. Technol. 99: 7565-7572. Azargohar, R. and A. K. Dalai (2005) Production of activated carbon from Luscar Char: Experimental and modeling studies. Micropor. Mesopor. Mater. 85: 219225. Tan, I. A. W., A. L. Ahmad, and B. H. Hameed (2008) Optimization of preparation conditions for activated carbon from coconut husk using response surface methodology. Chem. Eng. J. 137: 462-470.

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