**1. Introduction**

20 State of the Art in Biosensors / Book 1

330 State of the Art in Biosensors - General Aspects

Corrected version 2010.

[44] Working Group 1 [2008]. *Evaluation of measurement data - Guide to the expression of uncertainty in measurement*, 1 edn, Joint Committee for Guides in Metrology, Paris.

[45] Yan, H.-H., Xiao, Y.-Y., Xie, S.-X. & Li, H.-J. [2012]. Tunable plasmon resonance of a

[46] Yanik, A. A., Huang, M., Artar, A., Chang, T.-Y. & Altug, H. [2010]. On-chip nanoplasmonic biosensors with actively controlled nanofluidic surface delivery, *in* M. I. Stockman (ed.), *Plasmonics: Metallic Nanostructures and Their Optical Properties VIII*, Vol.

touching gold cylinder arrays, *J. At. Mol. Sci.* 3(3): 252–261.

7757, SPIE, San Diego, California, USA, pp. 775735–1–6.

The research and development of selective analytical systems, not requiring pre-treatment of samples and rapidly providing information in real time, is receiving increasing attention in various areas of human life (Chen *et al*, 2011;Palmisano *et al*, 2000;Maestre *et al*, 2005;Jia *et al*, 2004;Surareungchai *et al*, 1999;Tsai & Doong, 2005;Gülce *et al*, 2002;Akin *et al*, 2011;Mishra *et al*, 2012). For example, as environmental pollution poses a serious threat to the human health, there is an urgent demand to monitor pollutants and initiate appropriate environ‐ mental pollution treatment in the real time course. In the field of food quality control, the product quality and healthiness are the main factors, influencing customer satisfaction. The number of food processing and manufacturing mistakes can be minimized with risk assess‐ ment and continuous checking of the production process, e.g. in dairy farms, it is necessary to control the quality of raw milk on site in order to detect the presence of the residues of different antibiotics and other potentially harmful compounds before loading milk into the dairy production process.

On-site monitoring requires enhanced sensitivity, selectivity, rapidity, and ease of operation of the analytical equipment, which should provide reliable continuous information in realtime and demonstrate sufficient stability of action. Biosensors fulfil all the above-mentioned requirements and have already been applied in clinical diagnostics, food quality control, for‐ ensic chemistry, environmental monitoring and other areas (Castillo *et al*, 2004;Reder-Christ & Bendas, 2011;Kivirand & Rinken, 2011).

According to the IUPAC definition, a biosensor is a self-contained, integrated receptortransducer device, which is capable of providing selective quantitative or semi-quantitative analytical information and which uses a biological recognition element (bio-receptor) and a transducer in direct special contact (Thevenot *et al*, 2001). A biosensor consists of three parts:

© 2013 Kivirand et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Kivirand et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

(1) the sensitive biological element (such as tissues, microorganisms, cell receptors, enzymes, antibodies, nucleic acids, etc.); (2) the transducer or the detector element (physiochemical, optical, piezoelectric, etc.) that transforms the signal, resulting from the interaction between the analyte and the biological element into another signal that can be measured and quanti‐ fied; and (3) associated electronics or signal processors that are primarily responsible for the display of the results in a user-friendly way. All these three parts are associated with an in‐ formation management system. The principle of biosensors is shown in figure 1.

**Figure 1.** The principle of biosensor systems.

Enzyme-based biosensors technology relies upon the natural specificity of a given enzymat‐ ic protein to act selectively on a target analyte or group of analytes. Enzymes are catalysts bearing some excellent properties that may permit to perform the most complex chemical processes under the most benign experimental and environmental conditions. Enzymebased biosensors have emerged as a valuable technique for qualitative and quantitative analysis of a variety of target analytes. Although biosensors based on other biorecognition elements are rapidly progressing, enzyme biosensors are still the ones most frequently used for practical applications and as model systems in scientific studies. There are several ad‐ vantages of enzyme-based biosensors: a known reaction mechanism, a stable bio-renewable source of material and possibilities to modify the catalytic properties or substrate specificity by means of genetic engineering or to use catalytic amplification by the modulation of en‐ zyme activity with respect to the target analyte (Castillo *et al*, 2004;Hu *et al*, 2011).

For the implementation at industrial scale, the properties of enzymes have to be improved further, as soluble enzymes should be immobilized for their multiple utilizations. The im‐ mobilization of enzymes and the choice of an insoluble carrier are important features in de‐ signing the biorecognition part of enzyme-based biosensors. Various immobilization strategies can be envisioned: adsorption, entrapment, covalent cross-linking or affinity (Sas‐ solas *et al*, 2012;Cao, 2005;Mateo *et al*, 2007;Gibson, 1999). In some cases, enzyme immobili‐ zation protocols are also based on the combination of several immobilization methods: for example, an enzyme can be pre-immobilized on a carrier by adsorption, affinity or covalent bonding before further entrapment into a porous polymer. Biosensors based on immobilized enzymes have good operational and storage stability, high sensitivity and selectivity, good reproducibility and additionally, as enzyme immobilization reduces the time of enzymatic response, these biosensors can be easily used in continuous-flow and flow-through systems (Sassolas *et al*, 2012;Yang *et al*, 2010;Castillo *et al*, 2004;Cao, 2005).

(1) the sensitive biological element (such as tissues, microorganisms, cell receptors, enzymes, antibodies, nucleic acids, etc.); (2) the transducer or the detector element (physiochemical, optical, piezoelectric, etc.) that transforms the signal, resulting from the interaction between the analyte and the biological element into another signal that can be measured and quanti‐ fied; and (3) associated electronics or signal processors that are primarily responsible for the display of the results in a user-friendly way. All these three parts are associated with an in‐

Enzyme-based biosensors technology relies upon the natural specificity of a given enzymat‐ ic protein to act selectively on a target analyte or group of analytes. Enzymes are catalysts bearing some excellent properties that may permit to perform the most complex chemical processes under the most benign experimental and environmental conditions. Enzymebased biosensors have emerged as a valuable technique for qualitative and quantitative analysis of a variety of target analytes. Although biosensors based on other biorecognition elements are rapidly progressing, enzyme biosensors are still the ones most frequently used for practical applications and as model systems in scientific studies. There are several ad‐ vantages of enzyme-based biosensors: a known reaction mechanism, a stable bio-renewable source of material and possibilities to modify the catalytic properties or substrate specificity by means of genetic engineering or to use catalytic amplification by the modulation of en‐

zyme activity with respect to the target analyte (Castillo *et al*, 2004;Hu *et al*, 2011).

For the implementation at industrial scale, the properties of enzymes have to be improved further, as soluble enzymes should be immobilized for their multiple utilizations. The im‐ mobilization of enzymes and the choice of an insoluble carrier are important features in de‐ signing the biorecognition part of enzyme-based biosensors. Various immobilization strategies can be envisioned: adsorption, entrapment, covalent cross-linking or affinity (Sas‐ solas *et al*, 2012;Cao, 2005;Mateo *et al*, 2007;Gibson, 1999). In some cases, enzyme immobili‐ zation protocols are also based on the combination of several immobilization methods: for example, an enzyme can be pre-immobilized on a carrier by adsorption, affinity or covalent bonding before further entrapment into a porous polymer. Biosensors based on immobilized enzymes have good operational and storage stability, high sensitivity and selectivity, good reproducibility and additionally, as enzyme immobilization reduces the time of enzymatic

formation management system. The principle of biosensors is shown in figure 1.

**Figure 1.** The principle of biosensor systems.

332 State of the Art in Biosensors - General Aspects

The other moiety of a biosensor is the signal transduction system, which can be based on the measurement of electrochemical, magnetic, piezoelectric, thermometric or optical signals (Mehrvar & Abdi, 2004;Mello & Kubota, 2002;Sarma *et al*, 2009;Castillo *et al*, 2004). Among the above-mentioned systems, fibre-optic sensors are gradually achieving popularity. In comparison with electrochemical transducers, they do not consume any analytes and are in‐ sensible to electrical or magnetic interference (in fact, the oxygen detection capability has been demonstrated on single luminescent molecules) (D.R.Walt, 2006;Leung *et al*, 2007). Flu‐ orescence measurements can be used whenever a fluorescent analyte is detected. Naturally fluorescent compounds are not common in biosensor development and the technique is usu‐ ally applied in combination with artificially labelled compounds. Fluorescence is applied, for instance in biosensors with oxidase-type enzymes, which catalyze the consumption of oxygen, resulting the decrease in the luminescent signal of a fluorescent dye attached to the surface of an optical fibre. Besides a direct detection of the analyte of interest, the optical bio‐ sensor format may also involve indirect detection through optically labelled probes. Optical transducers may detect changes of absorbance, luminescence, polarization or refractive in‐ dex and can be adapted for the assembly of different enzyme-substrate systems. For exam‐ ple, using a fibre-optic sensor assay which senses pH changes, Viveros et al. have demonstrated a rapid detection of organophosphates (insecticides and potent neurotoxins) (Viveros *et al*, 2006) and Bidmanova et al. developed an enzyme-based fibre-optic biosensor by co-immobilization of purified enzyme and a fluorescent pH indicator (Bidmanova *et al*, 2010). Polster et al. have immobilized enzymes onto an array of optical fibers for use in the simultaneous detection of penicillin and ampicillin. These biosensors employ an interfero‐ metric technique based on following the shifts in the reflectance spectrum, caused by the pH changes of the solution during the penicillinase - catalyzed hydrolysis of the analytes, peni‐ cillin and ampicillin (Polster et al, 1995).

The incorporation of an optical fibre into a biochemical sensor results in several advan‐ tages: (1) numerous optically based methods are available for chemical analysis, as al‐ most every chemical analyte can be determined by measuring its spectroscopic properties; (2) fibres can be used to transmit light over long distances; (3) fibres have a multiplex capability (because they can guide light of different wavelengths at the same time and in different directions, multiple- or single-analyte monitoring in single locations can be performed with a single central unit); (4) fibres can be used in harsh environ‐ ments and are immune to electric or magnetic interferences, and so can be safer than electrochemical biosensors; (5) fibres can be easily miniaturized at low cost (6) fibres can be made biocompatible and thus used for in-vivo measurements; (7) a light guide can carry more information than electric wire; and (8) the temperature-dependence of the fi‐ bre is lower than that of common electrodes (Marazuela & Moreno-Bondi, 2002).

Some drawbacks of optical-fibre sensors can limit their applicability: (1) interference of am‐ bient light, although this can be avoided by use of suitable light isolation or modulated light sources; (2) background absorbance or fluorescence of the fibre itself; (3) long response times if mass transfer to the reagent phase is needed; and (4) limited availability of optimized commercial accessories for use with optical fibres (Marazuela & Moreno-Bondi, 2002).

Fibre-optic sensors are a perspective replacement of wide-spread Clark-type amperometric sensors for the detection of oxygen, although the application of oxygen measuring is hindered due to their sensitivity to oxygen fluctuations in natural samples (Li & Walt, 1995). To over‐ come the problem of the variability of oxygen concentrations, a two-sensor (or dual-sensor) ap‐ proach with a reference sensor included in the system can be used. Pasic et al. have used a microdialysis-based glucose-sensing system based on a fibre-optic hybrid sensor (Pasic *et al*, 2006; Pasic *et al*, 2007). They used a reference oxygen optrode to detect and compensate re‐ sponse changes caused by side events, like bacterial growth, temperature fluctuations or fail‐ ure of the peristaltic pump. The constructed sensor was evaluated in vitro using a 3-day continuous testing. As a result, they found that all glucose readings were clinically accurate and acceptable. With the purpose of analyzing complex biological samples without the need for any sample pre-treatment, Chen et al. developed a thermal based flow injection biosensor system, where a reference column was used to detect the non-specific thermal response (Chen *et al*, 2011). This sensor system was used to detect urea and lactate in non-standard milk prod‐ ucts (such as lactose free milk). They found that when using that kind of biosensing systems, it was not necessary to remove the interfering compounds during milk analysis. The sensitivity and accuracy of the analysis were in the ranges required by the dairy industry.

One of the key problems of real-time measurements is the calibration of the measuring system and the management of data acquisition to obtain the results as quickly as possi‐ ble. It is common in biosensor studies that the only information used, is the steady-state output (or the presumed 95% of it, the value of *T95*) (Baker & Gough, 1996). Unfortunate‐ ly, the determination of the value of *T95* is often imprecise because of the difficulties in estimating the attainment of steady state – thus there is an urgent need for exact model‐ ling of processes taking place in different set-ups of experimental measurements (Baker & Gough, 1996;Li & Walt, 1995; Lammertyn *et al*, 2006;Baronas *et al*, 2011). Measure‐ ments in continuous-flow systems require additional consideration of the flowing effects, both laminar and turbulent, in the biosensor output signal. The nature of the flow pro‐ file of the plug of solution introduced into the flowing stream of carrier solution is nor‐ mally affected mainly by laminar flow. The resistance between solution and the wall of the tubing causes the solution to travel slower near the wall of the tubing. However, some reports demonstrate that laminar flow in the small tubing does not have much ef‐ fect on distortion of the zone to the point that affects the accuracy of kinetic studies (Ko‐ nermann, 1999;Zhou *et al*, 2003;Hartwell & Grudpan, 2012).

The utilization of biosensors in continuous-flow manifolds allows samples to be manipu‐ lated or modified as required for the execution of various operations such as separation, automatic dilution or pre-concentration, or some kind chemical or biochemical reaction prior to the final detection step (Hansen, 1996). It is also necessary to distinguish clearly between continuous-flow biosensing (referred to as "sensor system") systems and flowthrough biosensors. The primary difference between these two systems lies in whether or not the detection of the analyte of interest is performed simultaneously with other ana‐ lytical steps (chemical reaction, separation, or both) in the continuous system. Thus in a continuous-flow sensor system, the biochemical reaction takes place before the sample has reached the flow cell for detection, while the flow-through biosensors involve the de‐ velopment of either the overall process or only the last step of the biochemical reaction in the flow cell (Hansen, 1996).

if mass transfer to the reagent phase is needed; and (4) limited availability of optimized commercial accessories for use with optical fibres (Marazuela & Moreno-Bondi, 2002).

334 State of the Art in Biosensors - General Aspects

Fibre-optic sensors are a perspective replacement of wide-spread Clark-type amperometric sensors for the detection of oxygen, although the application of oxygen measuring is hindered due to their sensitivity to oxygen fluctuations in natural samples (Li & Walt, 1995). To over‐ come the problem of the variability of oxygen concentrations, a two-sensor (or dual-sensor) ap‐ proach with a reference sensor included in the system can be used. Pasic et al. have used a microdialysis-based glucose-sensing system based on a fibre-optic hybrid sensor (Pasic *et al*, 2006; Pasic *et al*, 2007). They used a reference oxygen optrode to detect and compensate re‐ sponse changes caused by side events, like bacterial growth, temperature fluctuations or fail‐ ure of the peristaltic pump. The constructed sensor was evaluated in vitro using a 3-day continuous testing. As a result, they found that all glucose readings were clinically accurate and acceptable. With the purpose of analyzing complex biological samples without the need for any sample pre-treatment, Chen et al. developed a thermal based flow injection biosensor system, where a reference column was used to detect the non-specific thermal response (Chen *et al*, 2011). This sensor system was used to detect urea and lactate in non-standard milk prod‐ ucts (such as lactose free milk). They found that when using that kind of biosensing systems, it was not necessary to remove the interfering compounds during milk analysis. The sensitivity

and accuracy of the analysis were in the ranges required by the dairy industry.

nermann, 1999;Zhou *et al*, 2003;Hartwell & Grudpan, 2012).

One of the key problems of real-time measurements is the calibration of the measuring system and the management of data acquisition to obtain the results as quickly as possi‐ ble. It is common in biosensor studies that the only information used, is the steady-state output (or the presumed 95% of it, the value of *T95*) (Baker & Gough, 1996). Unfortunate‐ ly, the determination of the value of *T95* is often imprecise because of the difficulties in estimating the attainment of steady state – thus there is an urgent need for exact model‐ ling of processes taking place in different set-ups of experimental measurements (Baker & Gough, 1996;Li & Walt, 1995; Lammertyn *et al*, 2006;Baronas *et al*, 2011). Measure‐ ments in continuous-flow systems require additional consideration of the flowing effects, both laminar and turbulent, in the biosensor output signal. The nature of the flow pro‐ file of the plug of solution introduced into the flowing stream of carrier solution is nor‐ mally affected mainly by laminar flow. The resistance between solution and the wall of the tubing causes the solution to travel slower near the wall of the tubing. However, some reports demonstrate that laminar flow in the small tubing does not have much ef‐ fect on distortion of the zone to the point that affects the accuracy of kinetic studies (Ko‐

The utilization of biosensors in continuous-flow manifolds allows samples to be manipu‐ lated or modified as required for the execution of various operations such as separation, automatic dilution or pre-concentration, or some kind chemical or biochemical reaction prior to the final detection step (Hansen, 1996). It is also necessary to distinguish clearly between continuous-flow biosensing (referred to as "sensor system") systems and flowthrough biosensors. The primary difference between these two systems lies in whether or not the detection of the analyte of interest is performed simultaneously with other ana‐ The properties of a continuous-flow biosensor must meet the requirement of being able to follow the maximal anticipated concentration fluctuations within a specific acceptable error (Baker & Gough, 1996). In the flow systems the biosensor contacts the substrate for a short time only. When the analyte disappears, a buffer solution swills the enzyme surface, reduc‐ ing the substrate concentration at this surface to zero. Because of (analyte) remaining in the enzyme membrane substrate, the mass diffusion as well as the reaction still continues for some time even after the disconnection of the biosensor and substrate (Baronas *et al*, 2002). Compared to a batch system, the flow system present the advantages of the reduction in analysis time allowing a high sample throughput and the possibility to work with small vol‐ umes of the substrate (Baronas *et al*, 2002;Baronas *et al*, 2011). The flow arrangement also presents a wide response range and high sensitivity. While modelling a biosensor in a flow‐ ing system, it is of crucial importance to take into consideration the external diffusion limita‐ tions, because of the mass transport outside the enzyme region (Baronas *et al*, 2011;Ivanauskas & Baronas, 2008;Baronas *et al*, 2002).

Recently, the interest of constructing the fibre-optic flow biosensing systems is growing and these are mostly made for glucose monitoring (Pasic *et al*, 2007; Pasic *et al*, 2006;Zhu *et al*, 2002;Akin *et al*, 2011). Usually glucose oxidase is selected as a model enzyme in biosensing systems because of its low cost, stability and high solubility in different medium. Glucose oxidase is an enzyme which catalyzes the oxidation of β-D-glucose by molecular oxygen to δ-gluconolactone, which subsequently hydrolyzes to gluconic acid and hydrogen peroxide (Bankar *et al*, 2009). The enzyme is also of considerable commercial importance, as it is used in the removal either of glucose or oxygen from food products and in the production of glu‐ conic acid. The most important application of glucose oxidase is as a molecular diagnostic tool. The enzyme is used in biosensors for the quantitative determination of D-glucose in various samples of natural origin, such as body fluids, foodstuff, beverages, and fermenta‐ tion products (Bankar *et al*, 2009).

The aim of the present research was to study the modelling of a biosensor response and to propose optional biosensor calibration parameters in a flowing medium. We used a glucose optrode, which was a dual-sensor system, enabling to eliminate the fluctuations in the initial dissolved oxygen concentration, temperature and fluidic flow. The system consisted of two oxygen optrodes, one covered with glucose oxidase-containing nylon thread and the other with a similar, but "blank" thread, which were placed into isolated parallel flow channels. Glucose biosensor was selected for these studies as the bioactive compound of this biosen‐ sor; enzyme glucose oxidase is a well-characterized robust enzyme of high stability and se‐ lectivity towards glucose. The effect of the speed of the flow on different calibration parameters, obtained from the transient phase of the biosensor signal, was studied.

#### **2. Materials and experimental procedures**

Glucose oxidase (GOD, EC 1.1.3.4. from *Aspergillus niger*, 17 300 U/g protein) was obtained from Sigma. All other reagents used in the study were of analytical grade. Glucose stock sol‐ utions were prepared in phosphate buffer (PB) (pH 6.50, *I* = 0.1 M) and allowed to mutaro‐ tate overnight at 370 C; glucose working solutions were prepared immediately before use.

#### **2.1. Enzyme immobilization**

Glucose oxidase was immobilized onto nylon-6,6 threads, used as a carrier, according to previously published protocol with some minor modifications. Nylon is a perfect carrier for enzyme immobilization, because it is inert, hydrophilic and mechanically strong. However, its inertness prevents enzyme binding without a specific treatment (Isgrove *et al*, 2001;Se‐ gura-Ceniceros *et al*, 2006;Sassolas *et al*, 2012;Nan *et al*, 2009). Hence, activation of nylon is essential in immobilizing of an enzyme. One of the possibilities to activate nylon surface is by *O*-alkylation with dimethyl sulfate (DMS). Thus, pieces of nylon thread with a length of 100 cm were immersed into 98% (w/w) dimethyl sulfate at 500 C for 10 min; washed thor‐ oughly at first with ice-cold methanol and after that with 0.1 M PB (pH 6.50). Thereafter, the threads were immersed into 12.5% glutaraldehyde solution (in 0.1 M PB, pH 6.50) for 1 h at room temperature. Glutaraldehyde (GA) was used as a linker between the activated carrier and enzyme (Betancor *et al*, 2006;Pahujani *et al*, 2008). The threads were washed with 0.1 M PB (pH 6.50) and incubated overnight at 40 C in GOD solution (100 U/ml) (Scheme 1) (Kivir‐ and & Rinken, 2009). Finally, the threads were thoroughly washed and stored in a 0.1 M PB (pH 6.50) at 40 C until further use.

$$\mathbf{a} = \underbrace{\mathbf{g}}\_{\mathbf{f}} - \underbrace{\mathbf{g}}\_{\mathbf{m}\mathbf{m}\mathbf{m}} \underbrace{\mathbf{e}}\_{\mathbf{m}\mathbf{e}\mathbf{e}} - \underbrace{\mathbf{g}}\_{\mathbf{e}\mathbf{g}} \underbrace{\mathbf{e}}\_{\mathbf{m}\mathbf{e}} \underbrace{\mathbf{e}}\_{\mathbf{e}\mathbf{e}\mathbf{e}} - \underbrace{\mathbf{e}}\_{\mathbf{m}\mathbf{e}} \underbrace{\mathbf{e}}\_{\mathbf{e}\mathbf{e}} - \underbrace{\mathbf{e}}\_{\mathbf{g}} \underbrace{\mathbf{e}}\_{\mathbf{e}} \underbrace{\mathbf{e}}\_{\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}} - \mathbf{e}\_{\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}} \mathbf{e}\_{\mathbf{e}} - \underbrace{\mathbf{e}\_{\mathbf{e}}}\_{\mathbf{e}} \underbrace{\mathbf{e}}\_{\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}\mathbf{e}}$$

The GOD-containing threads kept at least 80% of their initial enzymatic activity for 35 days at 370 C. The thread's activity was controlled before each series of measurements and the sen‐ sor output corrected according to the actual activity of the enzyme.

#### **2.2. Biosensor system**

Glucose oxidase catalyzes the oxidation of β-D-glucose by dissolved oxygen causing a de‐ crease of dissolved oxygen concentration in the reaction medium:

$$\text{\(\beta-D-glucose\text{+}O}\_2\text{\(\gamma\)}\text{\(\gamma\)}\text{\(\gamma\)}\text{\(\gamma\)}\text{\(\gamma\)}\text{\(\gamma\)}\text{\(\gamma\)}$$

The applied oxygen optrode was constructed in the Institute of Physics at the University of Tartu. This sensor was based on measuring the oxygen-induced phosphorescence quench‐ ing of Pd-tetraphenylporphyrin molecules, encapsulated into thermally aged polymethyl methacrylate (PMMA) film, covering the cylindrical surface of a 30 mm long PMMA optical fibre with the diameter of 1 mm (Õige *et al*, 2005;Jaaniso *et al*, 2005). The dissolved oxygen concentration was calculated automatically with the help of Stern-Volmer relationship, us‐ ing original software Oxysens 2.0.

**2. Materials and experimental procedures**

100 cm were immersed into 98% (w/w) dimethyl sulfate at 500

sor output corrected according to the actual activity of the enzyme.

crease of dissolved oxygen concentration in the reaction medium:

β−*D* −glucose + O2 →

PB (pH 6.50) and incubated overnight at 40

C until further use.

tate overnight at 370

(pH 6.50) at 40

at 370

**2.2. Biosensor system**

**2.1. Enzyme immobilization**

336 State of the Art in Biosensors - General Aspects

Glucose oxidase (GOD, EC 1.1.3.4. from *Aspergillus niger*, 17 300 U/g protein) was obtained from Sigma. All other reagents used in the study were of analytical grade. Glucose stock sol‐ utions were prepared in phosphate buffer (PB) (pH 6.50, *I* = 0.1 M) and allowed to mutaro‐

Glucose oxidase was immobilized onto nylon-6,6 threads, used as a carrier, according to previously published protocol with some minor modifications. Nylon is a perfect carrier for enzyme immobilization, because it is inert, hydrophilic and mechanically strong. However, its inertness prevents enzyme binding without a specific treatment (Isgrove *et al*, 2001;Se‐ gura-Ceniceros *et al*, 2006;Sassolas *et al*, 2012;Nan *et al*, 2009). Hence, activation of nylon is essential in immobilizing of an enzyme. One of the possibilities to activate nylon surface is by *O*-alkylation with dimethyl sulfate (DMS). Thus, pieces of nylon thread with a length of

oughly at first with ice-cold methanol and after that with 0.1 M PB (pH 6.50). Thereafter, the threads were immersed into 12.5% glutaraldehyde solution (in 0.1 M PB, pH 6.50) for 1 h at room temperature. Glutaraldehyde (GA) was used as a linker between the activated carrier and enzyme (Betancor *et al*, 2006;Pahujani *et al*, 2008). The threads were washed with 0.1 M

and & Rinken, 2009). Finally, the threads were thoroughly washed and stored in a 0.1 M PB

The GOD-containing threads kept at least 80% of their initial enzymatic activity for 35 days

Glucose oxidase catalyzes the oxidation of β-D-glucose by dissolved oxygen causing a de‐

The applied oxygen optrode was constructed in the Institute of Physics at the University of Tartu. This sensor was based on measuring the oxygen-induced phosphorescence quench‐ ing of Pd-tetraphenylporphyrin molecules, encapsulated into thermally aged polymethyl

*GOD*

C. The thread's activity was controlled before each series of measurements and the sen‐

C; glucose working solutions were prepared immediately before use.

C for 10 min; washed thor‐

(1)

C in GOD solution (100 U/ml) (Scheme 1) (Kivir‐

<sup>D</sup>−gluconic acid + H2*O*<sup>2</sup> (2)

For the preparation of a glucose optrode, 18 cm of GOD-containing thread was cut and coiled around the oxygen-sensitive surface of the oxygen optrode (Fig.2) (Kivirand *et al*, 2011). If the activity of the thread dropped below 80% of its initial activity, the GOD-contain‐ ing thread was replaced. The reference optrode was covered with 18 cm of "blank" thread.

The glucose and the reference optrodes were placed into identical and parallel isolated channels of the measuring cell. A schematic cross-section of the cell system used in the stud‐ ies is presented in Fig.3. The flow channels were 50 mm long and with the diameter of 3 mm. All measurements were carried out at flow speed varying from 0 to 5.1 cm/sec at 37±0.020 C. A peristaltic pump was used to deliver samples through the system and to wash the system; the temperature was stabilized by a specially constructed oven. To minimize temperature fluctuations, the flow tubing was going through the oven for 10 times before entering the measuring cell, making the temperature very stable, although slightly increas‐ ing the analysis time. The stabilization of the temperature was really important, as the ana‐ lytical performance of the biosensor was anticipated to be greatly dependent on temperature (Pasic *et al*, 2007;Peedel & Rinken, 2012). At temperature 370 C no enzyme denaturation could be detected and this temperature was used to gain the highest biosensor sensitivity. All measurements at different flow rates and different substrate concentrations were carried out in 0.1 M PB at pH 6.50 (the oxygen saturation concentration at 370 C is 6.7 mg/ml).

**Figure 3.** Schematic cross–section of the measuring cell 1 – glucose and oxygen optrodes, covered with nylon thread; 2 – cylindrical messing oven for the stabilization of temperature (± 0.020C); 3 – measuring cell with flow channels; 4 – outflows; 5 – temperature sensor; 6 – inflow.

In case the biosensor signal parameters were studied in a standing liquid, the glucose assays were injected at the speed of 1.1 cm/sec. After each measurement the system was washed with 0.1 M PB (pH 6.50) until the sensor signals reached their initial values. The sensor out‐ put signal was recorded with the interval of 1 sec.

#### **2.3. Data processing**

The change of oxygen concentration was found as the difference between the signals of glu‐ cose and reference optrodes and normalized to bring the data from different sensors onto a common scale. From the reaction transient phase data, we calculated the total signal change parameter (at *t* →∞) using the earlier – proposed biosensor dynamic model, taking into ac‐ count the ping-pong mechanism of enzyme kinetics, diffusion phenomena and the inertia of the signal transduction system (Rinken & Tenno, 2001). According to this model, the nor‐ malized oxygen concentration *cO*<sup>2</sup> (*t*) / *cO*<sup>2</sup> (0) during the bio-recognition process in a biosensor is expressed as a 3-parameter function of time *t*:

$$\frac{c\_{O\_2}(t)}{c\_{O\_2}(0)} = A \exp\{-Bt\} + (1 - A) - 2A \sum\_{n=1}^{\sigma} (-1)^n \frac{\tau\_s}{n^2 / B - \tau\_s} \left[ \exp\{-Bt\} - \exp\left(-n^2 \frac{t}{\tau\_s}\right) \right] \tag{3}$$

where *cO*<sup>2</sup> (*t*) is the biosensor output current at time moment *t*; *cO*<sup>2</sup> (0) is the output current at the start of the reaction; *t* is time.

The parameter *A* is a complex coefficient, corresponding to the total possible biosensor sig‐ nal change at the steady–state and parameter *B* is the initial maximal slope of process curve; both parameters *A* and *B* depend hyperbolically on substrate concentration; *τ<sup>s</sup>* is the time constant of the transducer's response (Rinken & Tenno, 2001). Parameters *A*, *B* and *τs* are all independent on each other. According to the applied model, the total signal change parame‐ ter *A* is expressed as

$$A = \frac{k\_{cat}^{\*} \left[ \begin{matrix} E \end{matrix} \right]\_{total} c\_s^{bulk}}{k\_{diff}^{O\_2} K\_{O\_2} K\_s + (k\_{cat}^{\*} \left[ \begin{matrix} E \end{matrix} \right]\_{total} + k\_{diff}^{O\_2} K\_{O\_2}) c\_{bulk}} \tag{4}$$

where *kcat* \* is the apparent catalytic constant of the reaction; *E total* is the overall concentra‐ tion of the immobilized enzyme; *kdiff <sup>O</sup>*<sup>2</sup> is the apparent diffusion constant of the oxygen; *KO*<sup>2</sup> is the dissociation constant of the enzyme-oxygen complex; *Ks* the dissociation constant for the enzyme-substrate complex; and *cbulk* is the substrate concentration in solution.

Additionally, from the transient phase data, collected between 20 to 60 seconds from the start of the reaction, the apparent maximal speed parameter of the reaction was calculated and used for the biosensor calibration. The starting moment of the reaction was determined experimentally for the particular measuring system: due to the length of the tubing the probe reached the optrodes after a time interval dependent on the flow speed. The values of all points on biosensor calibration curves are the results of at least 3 parallel measurements.
