Simultaneous Mixture Analysis Using a Dynamic Microbial Sensor Combined with Chemometrics

Slama, Michael; Zaborosch, Christiane; Wienke, D.; Spener, Friedrich

doi:10.1021/ac9604144

Cited by 25 publications

(17 citation statements)

References 16 publications

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“…The application of chemometrics for processing of the array signals makes it possible to carry out the selective quantitative assay of components in a complex sample. Thus, the differences in the maximal rate of signal changes in response to various substrates allowed to analyze components of mixtures of organic acids with the measurement error less than 10% (Slama et al, 1996;Plegge et al, 2000). The possibility of selective analysis of a substrate mixture by a system including various combinations of sensors was studied in a number of works (Reshetilov et al, 1998;Lobanov et al, 2001).…”

Section: Substrate Specificity Of Microbial Sensors and Its Improvementmentioning

confidence: 99%

The Microbial Cell Based Biosensors

Решетилов¹,

Iliasov²,

Reshetilova³

2010

Intelligent and Biosensors

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Section: Substrate Specificity Of Microbial Sensors and Its Improvementmentioning

confidence: 99%

The Microbial Cell Based Biosensors

Решетилов¹,

Iliasov²,

Reshetilova³

2010

Intelligent and Biosensors

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“…For qualitative data analysis, principal components analysis (PCA), back-propagation feed-forward neural networks (BP-ANN), and learning vector quantization (LVQ) have been used [9,12,13,14]. For the often more demanding quantitative data analysis widespread partial least squares (PLS) has been used [4,16]. Application of neural networks to time-resolved data is often hindered by too high a number of variables for too few calibration samples; if so, data compression by a combination of PCA and BP-ANN can help [5].…”

Section: Frank Dieterlementioning

confidence: 99%

Time-resolved sensor arrays

Dieterle¹

2004

Analytical and Bioanalytical Chemistry

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tection and determination of a variety of substances has gained increasing popularity not only in analytical chemistry but also in our daily life. Most commercially available sensor systems, for example exhaust-gas sensors for automobiles or CO-sensors, are based on single-sensor devices. Such sensors are supposed to be as selective as possible for the analyte of interest, rendering data analysis rather simple.Despite this, the problem of interfering cross-reactive analytes and the lack of highly specific sensors for many analytes have resulted in the development of so-called sensor arrays in which several different cross-reactive sensors are combined. Multivariate analysis of the signal patterns from the sensors enables simultaneous determination of several analytes. In the field of gas sensing this array approach is well known, the devices being known as "electronic noses", whereas the approach is rather new in the field of biosensors, in which the cross-reactivity of antibodies is exploited for multi-analyte determination and for detection of whole classes of substance [1, 2]. Comparing sensor approaches with classical analytical techniques, sensor arrays are equivalent to multi-wavelength spectrometers whereas single-sensor approaches can be compared with single-wavelength photometry (Table 1). An advantage of the sensor-array approach is the possibility of adapting the set-up to many different combinations of analytes by re-calibrating the device without the need to change the hardware [3]. A severe limitation of almost all the devices available for quantification of analytes in mixtures is, however, the requirement that the number of sensors exceeds the number of analytes in the samples, because usually only a single property per sensor response, for example the height, area, or slope of the sensor signal, is used for data analysis. This renders the array approach hardware-and cost-intensive for many applications.A very recent trend in sensor research is, therefore, exploitation of differences between the kinetics of interaction of the sensor with different analytes. These differences are visible as sensor responses of different shapes during exposure to different analytes. When such sensors are combined with data evaluation which recognizes and exploits the different shapes of the signals, the number of analytes quantified per sensor is limited only by the similarity of the interaction kinetics of analytes and interfering substances. This approach, which combines the sensory principle with the chromatographic principle of separating analytes in space or time, thus opens the door to multi-analyte determinations based on single-sensor setups. Consequently, this approach is comparable with simple chromatographic set-ups (Table 1). All publications using this time-resolved sensor approach can be defined by three fundamental characteristics:1. interaction principle, 2. feature extraction, and 3. data analysis.The choice of the interaction principle mainly determines the feasible sensor materials and set-ups. Se...

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“…Biosensors are cost-effective to prepare 6 and easy to handle, and they can be miniaturized for in situ and online determination of various analytes. Above all, they give us the bioavailable content of heavy-metal ions 7 instead of the total metal ion 8 concentration given by traditional methods. Some of the heavy metals have long been recognized [9][10][11][12] as playing an important role in the human bodyfor example, as micronutrients and cofactors of various enzymes 13 but metals such as cadmium and mercury have no physiological role in the human body.…”

Section: Introductionmentioning

confidence: 99%