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Biosensors & Bioelectronics 16 (2001) 10011007

Analysis of ethanolglucose mixtures by two microbial sensors:application of chemometrics and artificial neural networks for dataprocessing

Alexei V. Lobanov a, Ivan A. Borisov a, Sherald H. Gordon b, Richard V. Greene c,Timothy D. Leathers b,*, Anatoly N. Reshetilov d

a Chair of Biotechnology and En6ironmental Protection, Pushchino State Uni6ersity, Pushchino, Moscow Region 142290, Russiab Biopolymer Research Unit, National Center for Agricultural Utilization Research, USDA, ARS, 1815 North Uni6ersity Street, Peoria,

IL 61604, USAc Office of International Programs, ARS, USDA, 5601 Sunnyside A6enue, Belts6ille, MD 20705, USA

d

G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Pushchino,Moscow Region 142290, Russia

Received 6 July 2000; received in revised form 28 March 2001; accepted 10 April 2001

Abstract

Although biosensors based on whole microbial cells have many advantages in terms of convenience, cost and durability, a major

limitation of these sensors is often their inability to distinguish between different substrates of interest. This paper demonstrates

that it is possible to use sensors entirely based upon whole microbial cells to selectively measure ethanol and glucose in mixtures.

Amperometric sensors were constructed using immobilized cells of either Gluconobacter oxydans or Pichia methanolica. The

bacterial cells of G. oxydans were sensitive to both substrates, while the yeast cells ofP. methanolica oxidized only ethanol. Using

chemometric principles of polynomial approximation, data from both of these sensors were processed to provide accurateestimates of glucose and ethanol over a concentration range of 1.08.0 mM (coefficients of determination, R2=0.99 for ethanol

and 0.98 for glucose). When data were processed using an artificial neural network, glucose and ethanol were accurately estimated

over a range of 1.010.0 mM (R2=0.99 for both substrates). The described methodology extends the sphere of utility for

microbial sensors. Published by Elsevier Science B.V.

Keywords: Amperometric microbial sensor; Artificial neural network; Chemometrics; Ethanol; Glucose; Selectivity

www.elsevier.com/locate/bios

1. Introduction

1.1. Selecti6ity of biosensors

Soon after biosensors appeared in the 1960s, efforts

began to improve their characteristics. One of the major

remaining problems is to provide highly selective analy-

ses (Riedel et al., 1989). As a general rule, one sensor is

used to determine the concentration of one substrate

contained within a sample (the scheme one sensorone

substrate). In this approach, wide substrate specificity,

or low selectivity, appreciably restricts practical appli-

cations (Turner et al., 1987). The problem of lowselectivity is particularly prevalent in sensors based

upon whole cells, organelles, or tissue cuts and signifi-

cantly compromises the advantages of these sensors.

However, low selectivity can also be a problem for

enzyme electrodes and, in some cases, for immunosen-

sors as well (Smolander et al., 1992, 1993; Weller et al.,

1998). Numerous attempts have been made to increase

the specificity of a single sensor. Approaches have

included substrate adaptation, the use of selective mem-

branes, screening for superior organisms and mutant

isolation (Racek, 1995). In some cases, these methods

have led to a satisfactory solution of the problem.

Names are necessary to report factually on available data; how-

ever, the USDA neither guarantees nor warrants the standard of the

product and the use of the name by USDA implies no approval of the

product to the exclusion of others that may also be suitable.

* Corresponding author. Tel.: +1-309-681-6377; fax: +1-309-681-

6689.E-mail address: [email protected] (T.D. Leathers).

0956-5663/01/$ - see front matter Published by Elsevier Science B.V.

PII: S 0 9 5 6 - 5 6 6 3 ( 0 1 ) 0 0 2 4 6 - 9


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A.V. Lobano6 et al. /Biosensors & Bioelectronics 16 () 100110071002

However, a new approach pertaining to the problem

of low selectivity has recently emerged, in which a set

of low-selectivity sensors are used in concert to

provide a highly selective analysis (the scheme N sen-

sors M substances). A prominent example of this

strategy is artificial tongue/nose systems (Vlasov et

al., 1997; Ziegler et al., 1998; Bessant and Saini,

1999). Such analyzers consist of low-selectivity biolog-

ical and/or chemical detectors and a signal processingsystem. In this scheme, it is possible to estimate the

ratio of mixture components and/or their actual con-

centrations by using principles of chemometrics or ar-

tificial neural networks. In practice, multidimensional

calibration dependencies are first obtained for mix-

tures of the analytes in different ratios; then the ra-

tios of components, or the concentrations of selected

components, are estimated from a complex of signals

using chemometric principles. These theoretical princi-

ples were developed for low-selectivity chemical sen-

sors (Schierbaum et al., 1990; Weimar et al., 1990,

1991; Slama et al., 1996).

1.2. Biosensors and the concept of artificial neural

networks

Artificial neural networks represent a comparatively

new trend in the study of artificial intelligence. They

are, in effect, mathematical models of biological neu-

ral systems. Computation strategies based on neural

models facilitate the solution of complex problems

that require a great deal of time and calculation when

solved by traditional methods.In this regard, biosensor response functions are of-

ten characterized by significant deviations from linear-

ity, requiring complex mathematical descriptions.

Accordingly, coupling biosensors with artificial neural

networks is growing in importance as a tool for mul-

ticomponent analyses (Hanaki et al., 1996; Ping and

Jun, 1996; Vlasov et al., 1997; Ziegler et al., 1998).

While an artificial neural network provides a non-lin-

ear approach that needs no a priori knowledge of

functional dependencies, it does require training.

Training or learning is based upon cumulative experi-

mental data.Neural networks consist of simple processing ele-

ments or neurons linked with each other in a partic-

ular configuration (Fig. 1a). Each neuron is a

non-linear transducer of input signals. Input signals

(Xi) are given weight coefficients (Wi), summed and

transferred to a non-linear function of activation

(transfer function, F) that forms an output signal (Y).

Training of the network then consists of the ad-

justment of the weight coefficients of input neuron

signals. Values of the vector of input signals (I) and

the vector of desired output signals (O) are presented

to the network. Weight coefficients are chosen in such

a way that the vector of output signals (O%) maxi-

mally corresponds to the vector O.

The action of the neural network is determined not

only by neuron properties and weights of connections

between them, but also by net topology, i.e. the rela-

tive positions of neurons. The development of a par-

ticular training algorithm, called the delta rule of

error back propagation (Werbos, 1974; McClelland

and Rumelhart, 1988), has made multilayer feed for-ward networks the most popular type (Fig. 1b).

1.3. Aim of the study

We previously showed that a low-selectivity whole-

cell biosensor could be used in conjunction with an

enzyme electrode to effectively estimate ethanol con-

tent in ethanol glucose mixtures (Reshetilov et al.,

1998). The parameters of these sensors were charac-

terized and optimal measuring conditions were re-

ported (Reshetilov et al., 1998). The current work

was designed to further develop this approach. Spe-cifically, the potential for differential analysis of etha-

nol glucose mixtures using two whole-cell microbial

sensors was investigated. Sensors based upon bacterial

cells of Gluconobacter oxydans, known to be highly

sensitive to both ethanol and glucose, were con-

structed. Similarly, sensors based on yeast cells of

Pichia methanolica, known to be highly sensitive to

ethanol and not to glucose, were also constructed.

Chemometric principles and an artificial neural net-

work were then used to process signals from these

biosensors. The reliability and accuracy of themethodology are described in this paper.

Fig. 1. Conceptual design of an artificial neural network. (a) Structure

of a single neuron. (b) Structure of a three-layer feed forwardnetwork.


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A.V. Lobano6 et al. /Biosensors & Bioelectronics 16 () 10011007 1003

2. Materials and methods

2.1. Microorganism strains and their culti6ation

G. oxydans strain B-1280 and P. methanolica strain

Y-2621 were obtained from the All-Russian Collection

of Microorganisms, G.K. Skryabin Institute of Bio-

chemistry and Physiology of Microorganisms, Russian

Academy of Sciences.G. oxydans was cultured on a medium containing 100

g/l sorbitol and 10 g/l yeast extract (Difco, Detroit, MI)

at pH 6.0. Growth was monitored by optical density.

Cells from early stationary phase cultures (16 18 h)

were harvested by centrifugation (3000g, 15 min),

washed twice with a sterile physiological solution and

immediately immobilized in receptors.

P. methanolica cells were initially grown in a medium

containing 10 g/l glucose, 1.0% (v/v) methanol, 5.0 g/l

yeast extract (Difco, Detroit, MI), 1.0 g/l KH2PO4, 3.5

g/l (NH4)2SO4, 0.5 g/l MgSO47H2O, 0.1 g/l CaCl2, 40

mg/l adenine, 40 mg/l arginine and 20 mg/l methionine.When cultures reached 0.5 1.0 mg/ml (wet wt.), cells

were harvested by centrifugation, washed and then

placed in fresh medium containing 1.0% ethanol in

place of glucose. After an additional 8 10 h of incuba-

tion at 30 C, cells were harvested and immediately

immobilized in receptors.

2.2. Cell immobilization

Receptor elements for both types of biosensors were

formed by immobilizing cells by adsorption onto chro-matographic paper (Whatman GF/A, UK). A 5 ml

portion of a cell suspension (80 mg/ml dry wt.) were

applied to the paper surface with a micropipet and

dried for 20 min. The receptor element was then placed

onto the measuring surface of a Clark-type amperomet-

ric electrode and was fixed utilizing nylon netting.

2.3. Measurements

An industrially manufactured amperometric trans-

ducer (Ingold 531-04, Ingold Mettler-Toledo, Wilming-

ton, MA) was coupled to the microbial biosensor.Sensor signals were then amplified in conjunction with

a built-in filter for noise suppression (U7-1, ZIP, Rus-

sia) and analog signals were directly converted to digi-

tal form by an ADD device (ADDA-12, Flytech

Technology, Taipei, Taiwan). Digital signals were pro-

cessed on a personal computer using the program Sen-

sor for Windows developed by the authors. The

program recorded sensor responses, performed signal

preprocessing (smoothing, removal of signal peak out-

bursts and zero drift), calculated signal parameters

(amplitude and rate of change), built calibration depen-

dencies and calculated substrate concentrations. The

rate of change of electrode current was used in further

calculations as sensor response. Simultaneously, analog

signals were registered on a two-coordinate recorder

(H-307/1, ZIP, Russia). All measurements were made at

20 C in a continuously stirred open cuvette with a

working volume of 5.0 ml. The working solution was 20

mM potassium-phosphate buffer, pH 7.0. Assays were

initiated by the introduction of the sample (50 ml) into

the cuvette. The measuring time for a single sample wasno more than 2 min. After each measurement, the

electrode was washed with buffer for 10 15 min until

oxygen concentrations were restored to initial levels.

2.4. Chemometric approach

2.4.1. Calibration surfaces

An unambiguous description of a sample consisting

of m components requires either direct knowledge of

the concentrations of all m components, or an indirect

expression of these values, such as the actual concentra-

tion value of one substrate and the ratio of concentra-tions of all substrates. Objects characterized by several

values can be mathematically described as vectors.

Thus, each sample will correspond to a vector with

coordinates equal to the concentration of components

comprising the sample. The vector is graphically de-

picted as a line segment in m-dimensional space con-

necting the origin of coordinates with a point whose

coordinates are equal to the vector components. For a

mixture of two components, the locus of all possible

concentrations forms a plane (i.e. a two-dimensional

space), called the concentration plane. A point on thisplane represents each particular sample. The calibration

dependence is a surface in three-dimensional space,

with the concentration values of constituent compo-

nents as abscissa and ordinate and the sensor response

value as applicate.

Since the overall sensor response to a sample repre-

sents the sum total of individual responses to each

constituent substrate, there may be equivalent overall

responses to samples containing substrates in different

ratios. A curved line that connects all points corre-

sponding to different samples inducing an identical

sensor response value is called an isocline. An isocline isthus the projection to the plane of substrate concentra-

tions of the curved line formed by a sensor response

plane intersecting the calibration surface. Individual

component concentrations can be determined using the

isoclines derived from two different sensors. The point

at which these isoclines intersect on the concentration

plane describes the concentration values for each

component.

2.4.2. Building of calibration surfaces

Amperometric sensors based upon whole cells of G.

oxydans were previously described as being sensitive to


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Fig. 2. Calibration surface for the sensor based on Gluconobacter

oxydans cells, showing the normalized response (nA/s) to mixtures of

glucose and ethanol over the range of 0.010.0 mM. Interpolation

was performed using a polynomial of the second degree.

3. To simplify the mathematical processing, calibration

surfaces were approximated by piecewise or other

dependencies (Table 1).

4. The responses of both sensors to the unknown

sample were measured.

5. The possible ratios of substrate concentrations in

the sample were determined by the calibration de-

pendence and sensor response value (described in

more detail in Reshetilov et al., 1998).6. The final estimated substrate values were deter-

mined as those congruent for both sensors.

Sensors suffered from slight losses in response values

over time. This was overcome by normalization to a

control sample (10 mM glucose and 10 mM ethanol).

2.5. Artificial neural networks

There are several variations on the back propagation

algorithm. In batch back propagation, the weights are

changed after presentation of all patterns of the train-

ing set. However, in the case of large training samples,

the network may be adjusted more rapidly by incorpo-

rating a weight change upon presentation of every

forward and backward pass of the network. Other

frequently used training methods include Quickprop

(Fahlman, 1988) and the method of resilient back

propagation (Rprop) (Riedmiller and Braun, 1993).

Rprop has a number of advantages over other training

methods. Rprop institutes a change in weight at every

change of the sign of a partial derivative of the error

function of the corresponding weight of connection

(Wij ) between neurons i and j. The value of the partial

both glucose and ethanol, resulting from highly active

aldose- and alcohol dehydrogenases in the cytoplasmic

membrane (Kitagawa et al., 1987; Smolander et al.,

1993). Sensors based upon whole cells of P. methanolica

are sensitive to ethanol but not to glucose (Morozova et

al., 1996). Although it would have been sufficient to

calibrate the P. methanolica sensor using only ethanolstandards, for convenience, a single set of glucose eth-

anol mixtures was used to calibrate both the P.

methanolica and G. oxydans sensors.

The sensors were calibrated over a range of substrate

concentrations from 0.0 to 10.0 mM, for both glucose

and ethanol. Preliminary studies showed that saturating

substrate levels did not impede the subsequent analysis

of data. The calibration surface for the response of the

G. oxydans sensor to glucose and ethanol is shown in

Fig. 2. The response of the P. methanolica sensor to

ethanol is shown in Fig. 3. As noted, the latter sensor

exhibits no response to glucose.

2.4.3. Chemometric determination of mixture

component concentrations

Chemometric principles were used to estimate the

concentrations of substrates in samples by the following

scheme:

1. Both sensors were calibrated as described in Section

2.4.2.

2. A calibration surface reflecting the dependence of

the obtained response value on the concentrations

of substrates in a sample was built for each sensor

(Figs. 2 and 3).

Fig. 3. Calibration surface for the sensor based on Pichia methanolica

cells, showing the normalized response (nA/s) to ethanol over therange of 0.010.0 mM, in the presence or absence of glucose.


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Table 1

Approximation function coefficients

Parameter AG. oxydansgl AG. oxydans

et BG. oxydansgl, et KG. oxydans

gl KG. oxydanset RG. oxydans

0 KP. methanolicaet RP. methanolic

a 0

0.008 0.001 0.082 0.128 0.096 0.025Value 0.0160.003

derivative itself is not taken into account, avoiding the

problem of blurred adaptivity. Compared with thegradient descent algorithm, weight coefficients are

changed evenly throughout the network, independent

of the distance to the output layer. Another important

advantage of this method is the algorithm stability in

relation to the choice of training parameters. The

choice of training rate and momentum parameter is

often of critical importance when using standard back

propagation. In practice, Rprop generally provides bet-

ter solutions for most problems, with fewer training

cycles.

The Stuttgart Neural Network Simulator ver. 4.1(SNNS Group, IPVR, University of Stuttgart) was used

to create an artificial neural network. Calculations were

performed on a personal computer based on a Pen-

tium 233MHz processor. Different learning rules

(Rprop, Quickprop, Backprop) and transfer functions

of activation (sigmoid, binary) were tested. A neural

network with one internal layer was used.

3. Results and discussion

3.1. Approximation of calibration surfaces anddetermination of mixture component concentrations

Calibration surfaces were approximated with polyno-

mials of different degrees in order to simplify the

determination of mixture component concentrations.

The simplest subprograms for finding the roots of

polynomials were used for building isoclines, in place of

complex mathematical programs.

The calibration dependence for the sensor based

upon the strain P. methanolica (Fig. 3) was adequately

described by the following equation:

RP. methanolica=KP. methanolicaet [et]+RP. methanolica

0 ,

where RP. methanolica is the sensor response value,

KP. methanolicaet is the sensor sensitivity to ethanol, [et] is

the ethanol concentration in the analyzed sample, and

RP. methanolica0 is the sensor response value in the absence

of ethanol. Effects of the second order (dependence of

the sensor response value on the square of the ethanol

concentration) can be ignored in describing the calibra-

tion surface.

The calibration surface for the sensor based upon the

strain G. oxydans (Fig. 2) was described by the follow-

ing polynomial of the second degree:

RG. oxydans=AG. oxydansgl [gl]2+AG. oxydans

et [et]2

+BG. oxydansgl, et [gl][et]+KG. oxydans

gl [gl]

+KG. oxydanset [et]+RG. oxydans

0

where RG. oxydans is sensor response value; KG. oxydansgl and

KG. oxydanset are sensor sensitivities to glucose and etha-

nol, respectively; [gl] and [et] are glucose and ethanol

concentrations in the analyzed sample; AG. oxydansgl and

AG. oxydanset are coefficients reflecting the second order

non-linearity of the sensor response dependence on

substrate concentration; BG. oxydansgl,et is a parameter show-

ing the degree of interaction of glucose and ethanoleffects on the sensor response value; RG. oxydans0 is the

sensor response value in the absence of both substrates.

The solution of the first degree equation is substituted

into the second degree equation, which is then solved

by finding the root of the second degree polynomial.

It should be noted that the polynomial of the second

degree restricts the useful range of this analysis. For

example, due to a nonzero value of the parameter

RG. oxydans0 , the approximation gives large errors with

low concentrations (B0.5 mM) of either substrate.

Furthermore, the useful range must be restricted to

substrate concentrations that do not result in two possi-ble values of component concentrations. In this case,

this restriction means that glucose concentrations must

be B8 mM. The values of parameters obtained are

given in Table 1.

3.2. Determination of mixture component

concentrations using artificial neural networks

A neural network with one internal layer and sig-

moid transfer functions of activation was used in this

study. Analytical signals of the two biosensors,RG. oxydans and RP. methanolica, arrived at the input layer.

The third input signal T, characterizing the time from

the start of measurement, was used to increase the

accuracy of determination. Network outputs were val-

ues of glucose and ethanol concentrations normalized

by the formula Cnorm=C/Cmax. The best results were

achieved with Rprop training (Riedmiller and Braun,

1993). Fig. 4 shows examples of dependencies of the

sum of square errors (SSE) at output neurons on the

number of training cycles at different distributions of

the weights of connections between neurons. Expect-

edly, since the weights were initialized at random, the


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training proceeded in different ways. The training pro-

cess was often stopped after reaching one of the local

minima of the error function (Fig. 4). To solve the

problem of local minima in some modifications of the

back propagation algorithm, a dynamic change of the

training rate was used. Local minima could also be

avoided (after the values of weight coefficients are

stabilized) by the addition of a random constituent to

start the gradient descent from a new point. Fig. 4shows that 8000 cycles provided sufficient training. No

significant decrease of the error was observed upon

further training.

Fig. 5 shows a plot of SSE dependence at output

neurons of the network on the number of neurons in

the internal layer. Due to the above described peculiar-

ities of weights initialization, the errors in each point

were measured three times to choose the least value.

The dependence analysis showed that 12 neurons in the

internal layer were sufficient for optimal training. Fur-

ther increases in the number of neurons did not result

in a significant decrease of SSE and required moretraining time. Under the optimal conditions established

above, the minimal SSE was 0.039.

3.3. Error of determination of concentrations

The accuracy of determining concentrations using the

Fig. 5. Dependence of the sum of squared errors (SSE) on the number

of neurons (n) in the internal layer.

chemometric approach and artificial neural networks

was estimated by the coefficient of determination (R 2)

described by the formula:

where

Yobs= %n

i=1

wiYi, %n

i=1

wi

is the weighed mean value of substrate concentration

(Yobsi), wi is weight coefficient for each measurement,Ycal are concentration values obtained from data pro-

cessing and n is number of measurements. The coeffi-

cient of determination describes the degree of data

deviation from standard values. Thus, when R 2=1 the

model provides a precise determination of the substrate

concentration.

3.4. Comparison of the efficiency of mixture analysis

using chemometric methods and artificial neural

networks

The analyses of experimental data processed by

chemometric and artificial neural network methods are

summarized in Table 2. These results testify to the

potential of using a system consisting of two microbial

R 2= %ni=1

wi(YobsiYobs)

2 %n

i=1

wi(YobsiYcal

i)2, %n

i=1

wi(YobsiYobs)

2,

Fig. 4. Examples of dependence of the logarithm of the sum of

squared errors (log(SSE)) on the number of training cycles (N).

Curves ad correspond to different initial distributions of weights onthe number of connections between neurons.

Table 2

Coefficient of determination

Analyte Value of R 2 for artificialValue of R 2 for

polynomial approximation neural networks

Glucose 0.9950.976

0.9920.993Ethanol


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sensors to analyze a two-component mixture. Data

processing was successful using either traditional chemo-

metric methods or an artificial neural network. Indeed,

differences in the accuracy of determinations were not

found to be significant. Since polynomial approximations

restricted the range of substrate concentrations that

could be analyzed, however, processing of data using the

neural network was preferable. The choice of a method

for experimental data processing should consider suchfactors as the availability of sufficient data for neural

network training and the difficulty of designing a

portable device using the polynomial approximation

dependence, as well.

4. Conclusions

Results demonstrated that a system consisting of only

two microbial sensors can be used in the quantitative

analysis of ethanol glucose mixtures. An artificial neural

network was shown to be highly efficient for the analysisof data from this system. The accuracy of the artificial

neural network was compared with that of a traditional

chemometric method. The coefficients of determination

(R 2) were 0.99 for ethanol and 0.98 for glucose in data

processed by polynomial approximation and 0.99 for

each substrate in data processed by the artificial neural

network.

Acknowledgements

This work was conducted under Specific Cooperative

Agreement 58-3620-8-F005 between the Institute of Bio-

chemistry and Physiology of Microorganisms,

Pushchino, Moscow Region, Russia and the Agricultural

Research Service of the US Department of Agriculture.

The authors thank N.O. Morozova for providing data

on measurements with the sensor on the basis of P.

methanolica.

References

Bessant, C., Saini, S., 1999. Simultaneous determination of ethanol,

fructose, and glucose at an unmodified platinum electrode using

artificial neural networks. Anal. Chem. 71, 28062813.

Fahlman, S.E., 1988. Faster-learning variations on back-propagation:

an empirical study. In: Sejnowski, T.J., Hinton, G.E., Touretzky,

D.S. (Eds.), Connectionist Models Summer School. Morgan

Kaufmann, San Mateo, CA.

Hanaki, S., Nakamoto, T., Moriizumi, T., 1996. Artificial odor-

recognition system using neural network for estimating sensory

quantities of blended fragrance. Sens. Actuat. 57, 6571.

Kitagawa, Y., Ameyama, M., Nakashima, K., Tamiya, E., Karube,

I., 1987. Amperometric alcohol sensor based on immobilised

bacteria cell membrane. Analyst 112, 17471749.

McClelland, J.L., Rumelhart, D.E., 1988. Explorations in Parallel

Distributed Processing, A Handbook of Models, Programs, and

Exercises. MIT Press, Cambridge.

Morozova, N.O., Iliasov, P.V., Ashin, V.V., Aleksandrova, I.F.,

Reshetilov, A.N., 1996. Comparative characteristics of Glu-

conobacter oxydans B-1280 and Pichia methanolica MN4 cells at

biosensoric ethanol detection. In: Proceedings of Eurosensors X,

Leuven, Belgium, vol. 2, pp. 421424.

Ping, W., Jun, X., 1996. A novel recognition method for electronicnose using artificial neural network and fuzzy recognition. Sens.

Actuat. 37, 169174.

Racek, J., 1995. Cell-Based Biosensors. Technomic, Lancaster.

Reshetilov, A.N., Lobanov, A.V., Morozova, N.O., Gordon, S.H.,

Greene, R.V., Leathers, T.D., 1998. Detection of ethanol in a

two-component glucose/ethanol mixture using a nonselective mi-

crobial sensor and a glucose enzyme electrode. Biosens. Bioelec-

tron. 13, 787793.

Riedel, K., Renneberg, R., Wollenberg, U., Kaiser, G., Scheller, F.,

1989. Microbial sensors: fundamentals and application for process

control. J. Chem. Technol. Biotechnol. 44, 85106.

Riedmiller, M., Braun, H., 1993. A direct adaptive method for faster

backpropagation learning: The RPROP algorithm. In: Proceed-

ings of the IEEE International Conference on Neural Networks,

pp. 586591.

Schierbaum, K.D., Weimar, U., Goepel, W., 1990. Multicomponent

gas analysis: an analytical chemistry approach applied to modified

SnO2 sensors. Sens. Actuat. 2, 7178.

Slama, M., Zaborosch, C., Wienke, D., Spener, F., 1996. Simulta-

neous mixture analysis using a dynamic microbial sensor com-

bined with chemometrics. Anal. Chem. 68, 38453850.

Smolander, M., Livio, H.-L., Rasanen, L., 1992. Mediated ampero-

metric determination of xylose and glucose with an immobilized

aldose dehydrogenase electrode. Biosens. Bioelectron. 7, 637643.

Smolander, M., Cooper, J., Schuhmann, W., Hammerle, M.,

Schmidt, H.-L., 1993. Determination of xylose and glucose in a

flow-injection system with PQQ-dependent aldose dehydrogenase.Anal. Chim. Acta 280, 119127.

Turner, A.P.F., Karube, I., Wilson, G.S. (Eds.), 1987. Biosensors:

Fundamentals and Application. University Press, Oxford.

Vlasov, Y.G., Legin, A.V., Rudnitskaya, A.M., DAmico, A.D.,

Natale, K., 1997. Chemical analysis of multicomponent water

solutions using nonselective sensors and artificial neural networks.

Zh. Analit. Khim. 52, 11991205.

Weimar, U., Schierbaum, K.D., Goepel, W., Kowalkowski, R., 1990.

Pattern recognition methods for gas mixture analysis: application

to sensor arrays based upon SnO2. Sens. Actuat. 1, 93 96.

Weimar, U., Vaihinger, S., Schierbaum, K.D., Goepel, W., 1991.

Multicomponent analysis in chemical sensing. In: Yamazoe, N.

(Ed.), Chemical Sensor Technology, vol. 3. Kodansha Ltd,

Tokyo, pp. 5188.Weller, M.G., Schutz, A.J., Winklmair, M., Niessner, R., 1998.

Highly parallel affinity sensor for the determination of water

contaminants. In: Abstracts: The Fifth World Congress on

Biosensors, p. 145.

Werbos P., 1974. Beyond Regression: New Tools for Prediction and

Analysis in the Behavioral Sciences. Ph.D. Thesis, Cambridge,

MA.

Ziegler, C., Goepel, W., Haemmerle, H., Hatt, H., Jung, G., Lax-

huber, L., Schmidt, H.-L., Schuetz, S., Voegtle, F., Zell, A., 1998.

Bioelectronic noses: a status report. Part II. Biosens. Bioelectron.

13, 539571.

bio sensors and bio electronics 16, 2001

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