Optimization of the multianalyte determination with biased biosensor response

Baronas, Romas; Kulys, Juozas; Žilinskas, Antanas; Lančinskas, Algirdas; Baronas, Darius

doi:10.1016/j.chemolab.2013.05.003

Cited by 11 publications

(25 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar problems for simpler models have been already considered in [18,22]. In this work, we consider the quantitative determination of two substrates acting as competitive inhibitors of each other at the external diffusion limitation.…”

Section: Optimization Problemmentioning

confidence: 98%

“…Assuming that the temperature, T , changes linearly with time ( T = T 0 + at ) and R (0) equals unity, the trend factor, R ( t ), is expressed as follows:

R (t) = exp (\frac{E_{a}}{\overset{R}{true}} \times \frac{a t}{T_{0} (T_{0} + a t)})

where E a is the activation energy, R̄ = 1.98 (cal/(K · mol)) is the gas constant, T is absolute temperature, T 0 = 298 K, and a (K/s) is the coefficient of proportionality [22,35]. The zero value of a corresponds to a signal effected with no trend, I T ( t ) = I ( t ) at a = 0 and t > 0.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The measurements, I N ( t ), of the noisy signal can be modeled by adding a white Gaussian noise to the noise free biosensor response, I ( t ),

I_{N} (t) = I (t) + ξ

where ξ ∼ (0, σI ( t )) stands for the random number, generated following the Gaussian distribution with zero mean and standard deviation equal to σI ( t ) [22]. Then, the trended noisy output signal I TN ( t ) can be modeled as follows:

I_{T N} (t) = I (t) R (t) + ξ

The noise can be signal-dependent or signal-independent [20].…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The multiplicative signal-dependent noise is used in the modeling, since the measurement errors in such applications are usually proportional to the measured signal value [20,36]. The influence of the additive noise can be also investigated; e.g., the influence of both kinds of noise was compared in [22] for a similar model, but without an external diffusion layer.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The approach to apply a surrogate objective function has been proposed in [22], where the surrogate objective function, z̃ ( t i , c ), is defined as bi-linear interpolant of the function, z ( t i , c ), assuming that the theoretical biosensor responses, z ( t i , c ), are already simulated using a discrete set, C′ , of concentrations c = ( c 1 , c 2 ) for all i = 1, …, n .…”

Section: Optimization Problemmentioning

confidence: 99%

See 4 more Smart Citations

Effect of Diffusion Limitations on Multianalyte Determination from Biased Biosensor Response

Baronas

Kulys

Lančinskas

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

The optimization-based quantitative determination of multianalyte concentrations from biased biosensor responses is investigated under internal and external diffusion-limited conditions. A computational model of a biocatalytic amperometric biosensor utilizing a mono-enzyme-catalyzed (nonspecific) competitive conversion of two substrates was used to generate pseudo-experimental responses to mixtures of compounds. The influence of possible perturbations of the biosensor signal, due to a white noise- and temperature-induced trend, on the precision of the concentration determination has been investigated for different configurations of the biosensor operation. The optimization method was found to be suitable and accurate enough for the quantitative determination of the concentrations of the compounds from a given biosensor transient response. The computational experiments showed a complex dependence of the precision of the concentration estimation on the relative thickness of the outer diffusion layer, as well as on whether the biosensor operates under diffusion- or kinetics-limited conditions. When the biosensor response is affected by the induced exponential trend, the duration of the biosensor action can be optimized for increasing the accuracy of the quantitative analysis.

show abstract

Section: Optimization Problemmentioning

confidence: 98%

“…Assuming that the temperature, T , changes linearly with time ( T = T 0 + at ) and R (0) equals unity, the trend factor, R ( t ), is expressed as follows:

R (t) = exp (\frac{E_{a}}{\overset{R}{true}} \times \frac{a t}{T_{0} (T_{0} + a t)})

Section: Mathematical Modelmentioning

confidence: 99%

“…The measurements, I N ( t ), of the noisy signal can be modeled by adding a white Gaussian noise to the noise free biosensor response, I ( t ),

I_{N} (t) = I (t) + ξ

I_{T N} (t) = I (t) R (t) + ξ

The noise can be signal-dependent or signal-independent [20].…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Optimization Problemmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Diffusion Limitations on Multianalyte Determination from Biased Biosensor Response

Baronas

Kulys

Lančinskas

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

A novel fast dynamic optimization approach for complex multivariable chemical process systems

Liu

et al. 2016

Can J Chem Eng

View full text Add to dashboard Cite

A novel fast dynamic optimization approach is proposed for complex multivariable chemical process systems, where the control variables are parameterized with different non‐uniform time grids and are treated as optimal decision variables to obtain the optimal switching structures for tackling the shortcoming caused by the conventional uniform parameterization method. Meanwhile, an adaptive fast calculation approach is proposed to calculate the differential equations so as to decrease the optimization time. The gradient formulae of decision variables are therefore further derived so that the conventional gradient‐based optimization algorithm can be utilized easily. Two well‐known complex multivariable systems in engineering are tested as illustrations and are compared with other literature reports in detail, where the uniform discretization control vector parameterization (ud‐CVP) method is also developed as the comparative base. Numerical results show that the proposed method can achieve better optimization results with fewer parameters and lower computation costs.

show abstract