Remodelling of the calibration curve for the potentiometric sensing of lamotrigine based on molecularly imprinted polymers using the four-parameter logistic model

Authors

  • Mohd Yunus Shukor Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia.

DOI:

https://doi.org/10.54987/jemat.v5i1.413

Keywords:

four-parameter logistic, Molecularly Imprinted Polymers, sigmoidal, calibration curve, lamotrigine

Abstract

A standard or a calibration curve allows the determination of an analyte concentration from an experiment. The calibration curve is constructed using a known concentration of analyte. The unidentified concentration of an analyte will then be determined utilizing this standard curve. Similar to immunoassay, ligand-binding interaction in Molecularly Imprinted Polymers (MIPs) also mimics immunoassay in that the calibration curve is sigmoidal in many cases. In a previous study an ion selective electrode based on molecularly imprinted polymers calibration curve for the detection of lamotrigine (LTG) showed a sigmoidal calibration curve but was not modelled according to a sigmoidal dose response model. Using the four-parameter logistic (4PL) equation, the calibration curve was remodelled according to this equation resulting in the calculated values of 19.87, -87.43, -4.614 and 0.6354 for the parameters a and d (maximum and minimum responses), Log EC50 (value that produces a 50% signal response) and Hillslope (slope-like parameter or Hill coefficient). The use of the 4PL model gave a correlation coefficient value of 0.9985 indicating the model excellently fit the experimental data

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Published

31.07.2017

How to Cite

Shukor, M. Y. (2017). Remodelling of the calibration curve for the potentiometric sensing of lamotrigine based on molecularly imprinted polymers using the four-parameter logistic model. Journal of Environmental Microbiology and Toxicology, 5(1), 1–3. https://doi.org/10.54987/jemat.v5i1.413

Issue

Vol. 5 No. 1 (2017)

Section

Articles

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