Monitoring of irritant gas using a whole-cell-based sensor system

Bohrn, Ulrich; Stütz, Evamaria; Fuchs, Karen; Fleischer, Maximilian; Schöning, Michael J.; Wagner, Patrick

doi:10.1016/j.snb.2012.05.088

Cited by 14 publications

(7 citation statements)

References 58 publications

(65 reference statements)

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“…Cell based sensor technologies attract much interest in biomechanical engineering, especially in application fields concerned with environmental quality monitoring, see Bohrn et al (2012) or Kubisch et al (2012) for some examples and references. In this study we use cell based chips, which means that the chips' surfaces are covered with a monolayer of a living cell population.…”

Section: Application To Real World Data -Cell Based Sensor Chipsmentioning

confidence: 99%

Classification of Functional Data with k-Nearest-Neighbor Ensembles by Fitting Constrained Multinomial Logit Models

Fuchs¹,

Pößnecker²,

Tutz³

2016

Preprint

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During the last decades, many methods for the analysis of functional data including classification methods have been developed. Nonetheless, there are issues that have not been adressed satisfactorily by currently available methods, as, for example, feature selection combined with variable selection when using multiple functional covariates. In this paper, a functional ensemble is combined with a penalized and constrained multinomial logit model. It is shown that this synthesis yields a powerful classification tool for functional data (possibly mixed with non-functional predictors), which also provides automatic variable selection. The choice of an appropriate, sparsity-inducing penalty allows to estimate most model coefficients to exactly zero, and permits class-specific coefficients in multiclass problems, such that feature selection is obtained. An additional constraint within the multinomial logit model ensures that the model coefficients can be considered as weights. Thus, the estimation results become interpretable with respect to the discriminative importance of the selected features, which is rated by a feature importance measure. In two application examples, data of a cell chip used for water quality monitoring experiments and phoneme data used for speech recognition, the interpretability as well as the selection results are examined. The classification performance is compared to various other classification approaches which are in common use.

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Section: Application To Real World Data -Cell Based Sensor Chipsmentioning

confidence: 99%

Classification of Functional Data with k-Nearest-Neighbor Ensembles by Fitting Constrained Multinomial Logit Models

Fuchs¹,

Pößnecker²,

Tutz³

2016

Preprint

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show abstract

“…Cell based sensor technologies are promising tools concerning environmental quality monitoring (see e.g. [51,52]). …”

Section: Application To Real World Data-cell Based Sensor Chipsmentioning

confidence: 99%

Nearest neighbor ensembles for functional data with interpretable feature selection

Fuchs

Gertheiss

Tutz

2015

Chemometrics and Intelligent Laboratory Systems

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“…Biosensor platforms profit from the high degree of specificity that a natural receptor has for its target to detect the analyte of interest. This natural receptor layer can consist of nucleic acids (DNA or RNA), − enzymes, − cells, − or antibodies. , …”

Section: Introductionmentioning

confidence: 99%

The Heat-Transfer Method: A Versatile Low-Cost, Label-Free, Fast, and User-Friendly Readout Platform for Biosensor Applications

Grinsven

Eersels²,

Peeters³

et al. 2014

ACS Appl. Mater. Interfaces

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In recent years, biosensors have become increasingly important in various scientific domains including medicine, biology, and pharmacology, resulting in an increased demand for fast and effective readout techniques. In this Spotlight on Applications, we report on the recently developed heat-transfer method (HTM) and illustrate the use of the technique by zooming in on four established bio(mimetic) sensor applications: (i) mutation analysis in DNA sequences, (ii) cancer cell identification through surface-imprinted polymers, (iii) detection of neurotransmitters with molecularly imprinted polymers, and (iv) phase-transition analysis in lipid vesicle layers. The methodology is based on changes in heat-transfer resistance at a functionalized solid-liquid interface. To this extent, the device applies a temperature gradient over this interface and monitors the temperature underneath and above the functionalized chip in time. The heat-transfer resistance can be obtained by dividing this temperature gradient by the power needed to achieve a programmed temperature. The low-cost, fast, label-free and user-friendly nature of the technology in combination with a high degree of specificity, selectivity, and sensitivity makes HTM a promising sensor technology.

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Monitoring of irritant gas using a whole-cell-based sensor system

Cited by 14 publications

References 58 publications

Classification of Functional Data with k-Nearest-Neighbor Ensembles by Fitting Constrained Multinomial Logit Models

Classification of Functional Data with k-Nearest-Neighbor Ensembles by Fitting Constrained Multinomial Logit Models

Nearest neighbor ensembles for functional data with interpretable feature selection

The Heat-Transfer Method: A Versatile Low-Cost, Label-Free, Fast, and User-Friendly Readout Platform for Biosensor Applications

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