**3. Results**

**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 –

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‐

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‐

2

t

t

= - +- - - ê ú -- -ç ÷ - ë û è ø <sup>å</sup> (3)

(0) during the bio-recognition process in a biosensor

é ù æ ö

2

t

(*t*) / *cO*<sup>2</sup>

( ) ( ) <sup>2</sup>

1

=

*O n s s c t <sup>t</sup> A Bt A A Bt n*

exp (1 ) 2 ( 1) exp exp (0) /

¥

outflows; 5 – temperature sensor; 6 – inflow.

338 State of the Art in Biosensors - General Aspects

malized oxygen concentration *cO*<sup>2</sup>

is expressed as a 3-parameter function of time *t*:

*O n s*

*c n B*

**2.3. Data processing**

2

( )

put signal was recorded with the interval of 1 sec.

#### **3.1. Output of the biosensor system**

The oxygen optrodes acting as oxygen transducers were employed to measure the rate of oxygen consumption in the enzymatic oxidation reactions. At fixed oxygen concentration, the response of the reference oxygen sensor is virtually constant with increasing glucose concentration, while the response of the glucose sensor decreases due to consumption of oxygen during glucose oxidation. The difference between the reference and the glucose sen‐ sor responses corresponds to the glucose concentration. Some examples of the signal curves of glucose and reference optrodes are shown in Fig. 4 (A).

**Figure 4. (A):** Oxygen concentration responses obtained with the glucose oxidase optrode for different concentra‐ tions of glucose solution: — glucose biosensor response (black line); — reference optrode response (grey line) (at 370C at flow rate 1.1 cm/sec in a 0.1 M phosphate buffer of pH 6.50). Arrows indicate the substrate adding time. **(B):** Nor‐ malized sensor outputs at different glucose concentrations (at 370C at flow rate 1.1 cm/sec in 0.1 M phosphate buffer of pH 6.50).

For the detection of the starting moment of the bio-recognition reaction, the dependence of the time gap between the injection of the probe into the tubing and the probe front reaching the optrodes on the speed of the flow was determined. For the particular measuring cell, it was linearly dependent on the speed of the flow as expected:

$$t = 103.30 - (36.05 \pm 0.95)\nu \tag{5}$$

where *t* is the lag period and *v* is the speed of the flow. Based on the value of this lag period for every measured flow speed, the biosensor data collected during this lag period was ex‐ tracted from the databank, used for the calculation of the signal parameters at different glu‐ cose concentrations. 0.6 0.8 0.2 mM 0.3 mM 0.5 mM 0.7 mM

The response signal of the reference oxygen sensor was stable and its fluctuations did not exceed 1% of the working range of the sensor at any measured glucose concentration and flow speed. Still, to eliminate all potential experimental noise, the difference between the signals of the reference and the glucose sensor response was used to determine the normalized output of the system. An example of the normalized biosensor output curves at *v* = 1.1 cm/sec at different glucose concentrations is shown on Fig. 4 (B). These curves were used for the

The response signal of the reference oxygen sensor was stable and its fluctuations did not exceed 1% of the working range of the sensor at any measured glucose concentration and flow speed. Still, to eliminate all potential experimental noise, the difference between the signals of the reference and the glucose sensor response was used to determine the normal‐ ized output of the system. An example of the normalized biosensor output curves at *v* = 1.1 cm/sec at different glucose concentrations are shown on Fig. 4 (B). These curves were used for the determination of the calibration parameters of the biosensor. 0 40 80 120 160 200 240 0.0 0.2 0.4 0.9 mM 1.0 mM 1.5 mM **(B) Time (sec) Normalized sensor output, cO2(t)/cO2(0)**

#### **3.2. System regeneration**

concentration analyzed, as expected (Fig. 5).

determination of the calibration parameters of the biosensor.

1.0

**Figure 4. (A):** Oxygen concentration responses obtained with the glucose oxidase optrode for different concentra‐ tions of glucose solution: — glucose biosensor response (black line); — reference optrode response (grey line) (at 370C at flow rate 1.1 cm/sec in a 0.1 M phosphate buffer of pH 6.50). Arrows indicate the substrate adding time. **(B):** Nor‐ malized sensor outputs at different glucose concentrations (at 370C at flow rate 1.1 cm/sec in 0.1 M phosphate buffer

For the detection of the starting moment of the bio-recognition reaction, the dependence of the time gap between the injection of the probe into the tubing and the probe front reaching the optrodes on the speed of the flow was determined. For the particular measuring cell, it

n

(5)

*t* = -± 103.30 (36.05 0.95)

was linearly dependent on the speed of the flow as expected:

of pH 6.50).

340 State of the Art in Biosensors - General Aspects

To use the bio-sensing system for real-time analysis, it is necessary to regenerate the system as quickly as possible. Regeneration involves passing a background flow of fluid without re‐ active components through the flowing system. The speed of cleaning of the flow system de‐ pends on the flow rate and slightly on the substrate concentration analyzed, as expected (Fig. 5). *phosphate buffer of pH 6.50).*  **3.2 System regeneration**  To use the bio-sensing system for real-time analysis, it is necessary to regenerate the system as quickly as

possible. Regeneration involves passing a background flow of fluid without reactive components through the flowing system. The speed of cleaning of the flow system depends on the flow rate and slightly on the substrate

*Figure 4 (B): Normalized sensor outputs at different glucose concentrations (at 370C at flow rate 1.1 cm/sec in 0.1 M* 

*a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at least 3 parallel measurements.* **Figure 5.** The speed of the cleaning of the biosensing system at different flow rates. Measurements were performed at 370C in a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at least 3 parallel measure‐ ments.

*Figure 5. The speed of the cleaning of the biosensing system at different flow rates. Measurements were performed at 370C in* 

The flow rate was varied between 0.3 to 5.1 cm/sec. At lower flow rates (0.3 to 1.3 cm/sec) the system regeneration time increased by increasing of the flow rate. With further increase of the flow rate, the regeneration time became independent on the flow rate, which could be explained with the limits substrate diffusion in the GOD-containing threads. From Fig. 5, it can be seen that the regeneration time was also dependent on the substrate concentration. At lower flow rates (0.3 to 1.1 cm/sec) the dependence was clearly seen – increasing the sub‐ strate concentration the regeneration time also increased. At higher flow rates the regenera‐ tion time did not depend on the substrate concentration any more. From these results it could be concluded that the minimum required flow rate for system regeneration was at least 1.1 cm/sec. At this flow rate, the time for cleaning the system was 4.5 to 5 min. Studies to minimize the regeneration time required, are ongoing.

#### **3.3. Calibration of the biosensor at different flow rates**

The flow rate in the system affected the values of the reaction parameters and thus the sam‐ ple throughput, biosensor sensitivity and detection limit. The choice of optimal flow rate is the presumption of obtaining accurate and reliable results in flow-through biosensor setups. At low flow rates, the apparent speed of the enzyme - catalyzed reaction, registered with a biosensor, is smaller than at high flow speeds, but the steady state signal can be cal‐ culated more accurately. For practical biosensor applications, it is important that the time re‐ quired for the acquirement of the results and for biosensor regeneration is as short as possible.

The flow rate in the system was varied between 0 (stopping the flow for the measurements) and 5.1 cm/sec; at flow rates over 5.1 cm/sec the waste of reagents became unreasonable. In case the flow rates were below 0.3 cm/sec, the experimental noise was very big due to the air bubbles, gathering on the surfaces of the sensors and walls of the flow channels and the val‐ ue of the signal to noise ratio was below 3.

#### *3.3.1. Sensitivity of the biosensor system based on different calibration parameters*

As described earlier, two different calibration parameters were used: the maximum signal change parameter *A* and the apparent maximal speed parameter *vapp*, determined as descri‐ bed in chapter 2.3. The biosensor calibration curves were made by plotting these parameters versus glucose concentration, as presented in Fig. 6 (A and B).

The glucose assay had a linear range up to 1.2 mM; at higher glucose concentrations the de‐ pendence became nonlinear. In case the measurements were carried out with the stopped flow, the biosensor showed linearity up to 0.8 mM. The linear part of these calibration curves and the values of slopes characterize the sensitivity of the biosensing system. Due to the different nature of the used calibration parameters, the dependences of the value of their slopes on flow rate are different (Fig. 7).

The maximum signal change parameter did not substantially depend on the flow rate in the range of the studied glucose concentrations (0.2 to 1.5 mM). Actually, this reaction parame‐ ter is also indifferent towards the determination of time, at which the analyte front reaches

The flow rate was varied between 0.3 to 5.1 cm/sec. At lower flow rates (0.3 to 1.3 cm/sec) the system regeneration time increased by increasing of the flow rate. With further increase of the flow rate, the regeneration time became independent on the flow rate, which could be explained with the limits substrate diffusion in the GOD-containing threads. From Fig. 5, it can be seen that the regeneration time was also dependent on the substrate concentration. At lower flow rates (0.3 to 1.1 cm/sec) the dependence was clearly seen – increasing the sub‐ strate concentration the regeneration time also increased. At higher flow rates the regenera‐ tion time did not depend on the substrate concentration any more. From these results it could be concluded that the minimum required flow rate for system regeneration was at least 1.1 cm/sec. At this flow rate, the time for cleaning the system was 4.5 to 5 min. Studies

The flow rate in the system affected the values of the reaction parameters and thus the sam‐ ple throughput, biosensor sensitivity and detection limit. The choice of optimal flow rate is the presumption of obtaining accurate and reliable results in flow-through biosensor setups. At low flow rates, the apparent speed of the enzyme - catalyzed reaction, registered with a biosensor, is smaller than at high flow speeds, but the steady state signal can be cal‐ culated more accurately. For practical biosensor applications, it is important that the time re‐ quired for the acquirement of the results and for biosensor regeneration is as short as

The flow rate in the system was varied between 0 (stopping the flow for the measurements) and 5.1 cm/sec; at flow rates over 5.1 cm/sec the waste of reagents became unreasonable. In case the flow rates were below 0.3 cm/sec, the experimental noise was very big due to the air bubbles, gathering on the surfaces of the sensors and walls of the flow channels and the val‐

As described earlier, two different calibration parameters were used: the maximum signal change parameter *A* and the apparent maximal speed parameter *vapp*, determined as descri‐ bed in chapter 2.3. The biosensor calibration curves were made by plotting these parameters

The glucose assay had a linear range up to 1.2 mM; at higher glucose concentrations the de‐ pendence became nonlinear. In case the measurements were carried out with the stopped flow, the biosensor showed linearity up to 0.8 mM. The linear part of these calibration curves and the values of slopes characterize the sensitivity of the biosensing system. Due to the different nature of the used calibration parameters, the dependences of the value of their

The maximum signal change parameter did not substantially depend on the flow rate in the range of the studied glucose concentrations (0.2 to 1.5 mM). Actually, this reaction parame‐ ter is also indifferent towards the determination of time, at which the analyte front reaches

*3.3.1. Sensitivity of the biosensor system based on different calibration parameters*

versus glucose concentration, as presented in Fig. 6 (A and B).

to minimize the regeneration time required, are ongoing.

342 State of the Art in Biosensors - General Aspects

**3.3. Calibration of the biosensor at different flow rates**

ue of the signal to noise ratio was below 3.

slopes on flow rate are different (Fig. 7).

possible.

**Figure 6. (A).** Glucose calibration curves based on maximum signal change parameter *A* at different flow rates. Meas‐ urements were performed at 370C in a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at least 3 parallel measurements. **(B).** Glucose calibration curves based on the apparent maximal speed parameter *vapp* at different flow rates. Measurements were performed at 370C in a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at least 3 parallel measurements.

their slopes on flow rate are different (Fig. 7).

*least 3 parallel measurements.* 

**Apparent maximal speed parameter**

 *app* **\*103 (sec-1)**

10 1.7 cm/sec

1.1 cm/sec 1.3 cm/sec 0.8 cm/sec

2.6 cm/sec

0.5 cm/sec 0.3 cm/sec

3.9 cm/sec 5.1 cm/sec

0.0 0.2 0.4 0.6 0.8 1.0 1.2

**Glucose concentration (mM)**

*Figure 6 (B). Glucose calibration curves based on the apparent maximal speed parameter vapp at different flow rates. Measurements were performed at 370C in a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at* 

The glucose assay had a linear range up to 1.2 mM; at higher glucose concentrations the dependence became nonlinear. In case the measurements were carried out with the stopped flow, the biosensor showed linearity up to

**(B)**

*Figure 7. Dependence of the slopes of the calibration curves on different flow rates. The slopes were calculated from the calibration curves. On the left side (●) is the biosensor system response parameter A and on the right side (○) the apparent maximal speed parameter νapp.*  **Figure 7.** Dependence of the slopes of the calibration curves on different flow rates. The slopes were calculated from the calibration curves. On the left side (●) is the biosensor system response parameter *A* and on the right side (○) the apparent maximal speed parameter ν*app*.

the biosensor, as it is defined as a biosensor maximum signal change in steady-state condi‐ tions (*t* → ∞). In case this parameter for the glucose oxidation reaction was measured in the standing medium (the flow was stopped), the values of this parameter at different glucose concentrations were significantly higher (Fig. 6A) and the slope of the calibration curve was about 1.7 times higher than it should be, if the same signal rising mechanism had been con‐ sidered. Actually this increase has only a qualitative value, as the hydraulic stroke of halting the flow influences the parameter values. Actually, in the standing medium the diffusion layer of oxygen and glucose at the surface of the sensors is much thicker and the impact of the reaction kinetics of the measured signal is much bigger than in the flowing mediums. Due to the accumulation of air bubbles in the flow system at small flow rates, it was not pos‐ sible to carry out experimental measurements at flow rates under 0.8 cm/sec and it is not clear, at which flow rates the signal rising mechanism changes.

The slope of biosensor calibration curve constructed with the apparent maximum speed pa‐ rameter *vapp*, increases along with the increase of the flow rate until 1.1 cm/sec; at higher flow rates it reaches its maximum value and glucose calibration curves are similar. Thus, apply‐ ing this parameter, the sensitivity of the biosensor can be modified according to the aim of analysis. As already pointed out, it was not possible to conduct measurements at flow rates under 0.8 cm/sec.

The flow rate also influences the biosensor response time. In standing solutions, 8 minutes were the minimal time of acquiring results with acceptable precision. So the flow rate of 1.3 cm/sec was chosen for the studies of the system repeatability, as it offers acceptable response time and sufficient sensitivity.

The repeatability of the experimental measurements was studied at glucose concentration of 0.5 mM (15 experiments per day and four days in a row). The repeatability of the measure‐ ments was very good considering that the standard deviation of the vertical distances of the points from the line *Sy.x.* was 0.0051 and the coefficient of determination *R2* was 98% (Fig.8). The results indicated the biosensor to exhibit a fairly analytical feature of repeatability. The flow rate also influences the biosensor response time. In standing solutions, 8 minutes were the minimal time of acquiring results with acceptable precision. So the flow rate of 1.3 cm/sec was chosen for the studies of the system repeatability, as it offers acceptable response time and sufficient sensitivity. The repeatability of the experimental measurements was studied at glucose concentration of 0.5 mM (15 experiments per day and four days in a row). The repeatability of the measurements was very good considering

that the standard deviation of the vertical distances of the points from the line *Sy.x.* was 0.0051 and the coefficient of determination *R2* was 98% (Fig.8). The results indicated the biosensor to exhibit a fairly analytical feature of

The slope of biosensor calibration curve constructed with the apparent maximum speed parameter *vapp*, increases along with the increase of the flow rate until 1.1 cm/sec; at higher flow rates it reaches its maximum value and

The maximum signal change parameter did not substantially depend on the flow rate in the range of the studied glucose concentrations (0.2 to 1.5 mM). Actually, this reaction parameter is also indifferent towards the determination of time, at which the analyte front reaches the biosensor, as it is defined as a biosensor maximum signal change in steady-state conditions (*t* → ∞). In case this parameter for the glucose oxidation reaction was measured in the standing medium (the flow was stopped), the values of this parameter at different glucose concentrations were significantly higher (Fig. 6A) and the slope of the calibration curve was about 1.7 times higher than it should be, if the same signal rising mechanism had been considered. Actually this increase has only a qualitative value, as the hydraulic stroke of halting the flow influences the parameter values. Actually, in the standing medium the diffusion layer of oxygen and glucose at the surface of the sensors is much thicker and the impact of the reaction kinetics of the measured signal is much bigger than in the flowing mediums. Due to the accumulation of air bubbles in the flow system at small flow rates, it was not possible to carry out experimental measurements at flow rates under 0.8 cm/sec and it is not clear, at which flow rates the signal rising mechanism

**3.4 Operational stability of the biosensor Figure 8.** Repeatability of the measurements with the glucose biosensor. Measurements were carried out at 370C in 0.5 mM glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec.

biosensors. Besides possible leaching of the bio-selective material, the biosensors are ascribed to the inactivation

*Figure 8. Repeatability of the measurements with the glucose biosensor. Measurements were carried out at 370C in 0.5 mM* 

#### The loss of sensitivity under operational conditions is one of the most serious limits of the practical utility of **3.4. Operational stability of the biosensor**

*glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec.* 

changes.

repeatability.

flow rates under 0.8 cm/sec.

the biosensor, as it is defined as a biosensor maximum signal change in steady-state condi‐ tions (*t* → ∞). In case this parameter for the glucose oxidation reaction was measured in the standing medium (the flow was stopped), the values of this parameter at different glucose concentrations were significantly higher (Fig. 6A) and the slope of the calibration curve was about 1.7 times higher than it should be, if the same signal rising mechanism had been con‐ sidered. Actually this increase has only a qualitative value, as the hydraulic stroke of halting the flow influences the parameter values. Actually, in the standing medium the diffusion layer of oxygen and glucose at the surface of the sensors is much thicker and the impact of the reaction kinetics of the measured signal is much bigger than in the flowing mediums. Due to the accumulation of air bubbles in the flow system at small flow rates, it was not pos‐ sible to carry out experimental measurements at flow rates under 0.8 cm/sec and it is not

0 1 2 3

**Flow rate (cm/sec)**

*Figure 7. Dependence of the slopes of the calibration curves on different flow rates. The slopes were calculated from the calibration curves. On the left side (●) is the biosensor system response parameter A and on the right side (○) the apparent* 

**Figure 7.** Dependence of the slopes of the calibration curves on different flow rates. The slopes were calculated from the calibration curves. On the left side (●) is the biosensor system response parameter *A* and on the right side (○) the

0.0 0.2 0.4 0.6 0.8 1.0 1.2

**Glucose concentration (mM)**

*Figure 6 (B). Glucose calibration curves based on the apparent maximal speed parameter vapp at different flow rates. Measurements were performed at 370C in a 0.1 M phosphate buffer (pH 6.50). The values of all points are the results of at* 

The glucose assay had a linear range up to 1.2 mM; at higher glucose concentrations the dependence became nonlinear. In case the measurements were carried out with the stopped flow, the biosensor showed linearity up to 0.8 mM. The linear part of these calibration curves and the values of slopes characterize the sensitivity of the biosensing system. Due to the different nature of the used calibration parameters, the dependences of the value of

**(B)**

0

2

4

6

**Slope of calibration curve;**

**(1/mM cm)**

*app* **\*103**

8

10

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

*least 3 parallel measurements.* 

their slopes on flow rate are different (Fig. 7).

344 State of the Art in Biosensors - General Aspects

*A*

**Slope of calibration curve; parameter**

*maximal speed parameter νapp.* 

apparent maximal speed parameter ν*app*.

**(sec/mM cm)**

**Apparent maximal speed parameter**

 *app* **\*103 (sec-1)**

10 1.7 cm/sec

1.1 cm/sec 1.3 cm/sec 0.8 cm/sec

2.6 cm/sec

0.5 cm/sec 0.3 cm/sec

3.9 cm/sec 5.1 cm/sec

The slope of biosensor calibration curve constructed with the apparent maximum speed pa‐ rameter *vapp*, increases along with the increase of the flow rate until 1.1 cm/sec; at higher flow rates it reaches its maximum value and glucose calibration curves are similar. Thus, apply‐ ing this parameter, the sensitivity of the biosensor can be modified according to the aim of analysis. As already pointed out, it was not possible to conduct measurements at flow rates

The flow rate also influences the biosensor response time. In standing solutions, 8 minutes were the minimal time of acquiring results with acceptable precision. So the flow rate of 1.3 cm/sec was chosen for the studies of the system repeatability, as it offers acceptable response

clear, at which flow rates the signal rising mechanism changes.

under 0.8 cm/sec.

time and sufficient sensitivity.

and denaturation of their bioactive compounds. The operational stability of the present biosensor system was assessed by a continuous long-term experiment, in which we repeatedly analysed 0.5 mM glucose solutions. The biosensor system was in everyday exploitation – used for about a 15-measurement-serie per day – after which it was washed with 0.1 M PB (pH 6.50) and left overnight at 370C. The initial activity of the sensor dropped for The loss of sensitivity under operational conditions is one of the most serious limits of the prac‐ tical utility of biosensors. Besides possible leaching of the bio-selective material, the biosensors are ascribed to the inactivation and denaturation of their bioactive compounds. The operation‐ al stability of the present biosensor system was assessed by a continuous long-term experi‐ ment, in which we repeatedly analysed 0.5 mM glucose solutions. The biosensor system was in everyday exploitation – used for about a 15-measurement-serie per day – after which it was washed with 0.1 M PB (pH 6.50) and left overnight at 370 C. The initial activity of the sensor dropped for about 20% during the first 3 days; after that the biosensor response remained con‐ stant for over 35 days operation period with no significant loss of activity (Fig. 9).

*Figure 9. Stability of the biosensor with a GOD-containing nylon thread held at 37oC. Measurements were carried out in 0.5 mM glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec. The values of all points are the results of at least 3 parallel measurements.*  **Figure 9.** Stability of the biosensor with a GOD-containing nylon thread held at 37oC. Measurements were carried out in 0.5 mM glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec. The values of all points are the results of at least 3 parallel measurements.

#### **4. Conclusions 4. Conclusions**

A differential optrode based biosensor system for real-time monitoring of glucose in flow-through set-up has been studied and the selection of different calibration parameters analyzed. The influences of the flow rate and oxygen fluctuations on the sensor response have been studied. It was found that even at quite low flow rates the rising of the biosensor signal was controlled by diffusion and only in standing solutions the kinetics of the biorecognition reaction had a substantial impact on the measurable output. The biosensor steady-state signal, calculated from the transient response was not dependent on the flow rate, if the latter exceeded 0.8 cm/sec. The applied enzyme immobilizing procedure ensured a good operational stability of the system. Thus, an A differential optrode based biosensor system for real-time monitoring of glucose in flowthrough set-up has been studied and the selection of different calibration parameters ana‐ lyzed. The influences of the flow rate and oxygen fluctuations on the sensor response have been studied. It was found that even at quite low flow rates the rising of the biosensor signal was controlled by diffusion and only in standing solutions the kinetics of the bio-recognition reaction had a substantial impact on the measurable output. The biosensor steady-state sig‐ nal, calculated from the transient response was not dependent on the flow rate, if the latter exceeded 0.8 cm/sec.

interference and cross-talk free device for the real-time monitoring of glucose concentration was successfully established. Used sensing system can be generalized for the other biologically important compounds catalyzed by oxidase-class enzymes and for the construction of biosensor arrays for different applications. **List of symbols**  The applied enzyme immobilizing procedure ensured a good operational stability of the system. Thus, an interference and cross-talk free device for the real-time monitoring of glu‐ cose concentration was successfully established. Used sensing system can be generalized for the other biologically important compounds catalyzed by oxidase-class enzymes and for the construction of biosensor arrays for different applications.

*A* Complex coefficient, corresponding to the total possible biosensor signal change at the steady-state

#### ( ) <sup>2</sup> *c t <sup>O</sup>* Biosensor output current at time moment *<sup>t</sup>* (0) *<sup>O</sup>*<sup>2</sup> *c* Output current at the start of the reaction **List of symbols**

*s* 

*B* Initial maximal slope of process curve

*app* Apparent maximal speed parameter

Time constant of the transducer´s response

*t* Time *cO*2 (*t*) Biosensor output current at time moment *t*

*<sup>y</sup> <sup>x</sup> S* . Standard deviation of the vertical distances of the points from the line

*cO*2 (0) Output current at the start of the reaction

*t* Time

about 20% during the first 3 days; after that the biosensor response remained constant for over 35 days operation

0 5 10 15 20 25 30 35 40

**Time (days)**

*Figure 9. Stability of the biosensor with a GOD-containing nylon thread held at 37oC. Measurements were carried out in 0.5 mM glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec. The values of all points are the results of* 

**Figure 9.** Stability of the biosensor with a GOD-containing nylon thread held at 37oC. Measurements were carried out in 0.5 mM glucose solutions in 0.1 M phosphate buffer (pH 6.50) at flow rate 1.3 cm/sec. The values of all points are

A differential optrode based biosensor system for real-time monitoring of glucose in flow-through set-up has been studied and the selection of different calibration parameters analyzed. The influences of the flow rate and oxygen fluctuations on the sensor response have been studied. It was found that even at quite low flow rates the rising of the biosensor signal was controlled by diffusion and only in standing solutions the kinetics of the biorecognition reaction had a substantial impact on the measurable output. The biosensor steady-state signal, calculated from the transient response was not dependent on the flow rate, if the latter exceeded 0.8 cm/sec.

A differential optrode based biosensor system for real-time monitoring of glucose in flowthrough set-up has been studied and the selection of different calibration parameters ana‐ lyzed. The influences of the flow rate and oxygen fluctuations on the sensor response have been studied. It was found that even at quite low flow rates the rising of the biosensor signal was controlled by diffusion and only in standing solutions the kinetics of the bio-recognition reaction had a substantial impact on the measurable output. The biosensor steady-state sig‐ nal, calculated from the transient response was not dependent on the flow rate, if the latter

The applied enzyme immobilizing procedure ensured a good operational stability of the system. Thus, an interference and cross-talk free device for the real-time monitoring of glucose concentration was successfully established. Used sensing system can be generalized for the other biologically important compounds catalyzed by

The applied enzyme immobilizing procedure ensured a good operational stability of the system. Thus, an interference and cross-talk free device for the real-time monitoring of glu‐ cose concentration was successfully established. Used sensing system can be generalized for the other biologically important compounds catalyzed by oxidase-class enzymes and for the

*A* Complex coefficient, corresponding to the total possible biosensor signal change at the steady-state

oxidase-class enzymes and for the construction of biosensor arrays for different applications.

period with no significant loss of activity (Fig. 9).

346 State of the Art in Biosensors - General Aspects

60

the results of at least 3 parallel measurements.

*at least 3 parallel measurements.* 

**4. Conclusions** 

**4. Conclusions**

exceeded 0.8 cm/sec.

**List of symbols**

**List of symbols** 

*t* Time

*cO*2

*s* 

( ) <sup>2</sup> *c t <sup>O</sup>* Biosensor output current at time moment *<sup>t</sup>* (0) *<sup>O</sup>*<sup>2</sup> *c* Output current at the start of the reaction

*B* Initial maximal slope of process curve

*app* Apparent maximal speed parameter

Time constant of the transducer´s response

*<sup>y</sup> <sup>x</sup> S* . Standard deviation of the vertical distances of the points from the line

construction of biosensor arrays for different applications.

(*t*) Biosensor output current at time moment *t*

70

80

**Relative activity (%)**

90

100

*A* Complex coefficient, corresponding to the total possible biosensor signal change at the steady-state

