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Development of electrochemical immunosensor-based poly(3,4-ethylenedioxythiophene) composites for clenbuterol detection


A. Talib, Nurul Ain (2018) Development of electrochemical immunosensor-based poly(3,4-ethylenedioxythiophene) composites for clenbuterol detection. Doctoral thesis, Universiti Putra Malaysia.


Illegal usage of banned antibiotic such as clenbuterol (CLB) in food products is a big concern since this will directly affect the consumer health. World Health Organization (WHO) is forbidding any usage of CLB in the livestock animals due to health effects such as muscular tremor, increase rate of heart throb, glaucoma, fever and respiratory problems to human that influenced by CLB residue in food products. Currently, the methods used for CLB detection is expensive, time-consuming and involving complicated analysis. In this study, immunosensor modified with poly(3,4-ethylenedioxythiophene)/multi-walled carbon nanotube (PEDOT/MWCNT), poly(3,4-ethylenedioxythiophene)/graphene oxide (PEDOT/GO) and anti-clenbuterol antibody (Ab) were developed on screen-printed carbon electrode (SPCE) for detection of CLB. Sensor platforms from modification of electrode with PEDOT/MWCNT and PEDOT/GO composites were prepared by electropolymerization using chronoamperometry (CA) technique. The operating conditions (concentration of MWCNT, concentration of GO, electropolymerization potential and deposition time) were optimized by using response surface methodology (RSM) combined with central composite design (CCD) and Box-Behnken design (BBD) for PEDOT/MWCNT and PEDOT/GO composites, respectively to obtain the optimum peak current. The statistical analysis showed that the concentration of MWCNT, concentration of GO, electropolymerization potential and deposition time have significantly affected the peak current response. The coefficient of determination (R2) for model equations of PEDOT/MWCNT and PEDOT/GO composites resulting value of 0.9973 and 0.9965, respectively. The optimized condition predicted by the software was compared with the experiments and resulting in less than 2% error, indicating that this model was reliable and able to predict the peak current response accurately. The cyclic voltammetry (CV) measurements indicated that PEDOT/MWCNT and PEDOT/GO modified electrodes had successfully enhanced the peak currents compared to PEDOT, MWCNT and GO. Incorporation of MWCNT and GO into PEDOT were proven by field emission scanning electron microscopy (FESEM) images, Fourier transform infrared (FTIR) and Raman spectra. The optimized PEDOT/MWCNT and PEDOT/GO composites were further modified with Ab on SPCE to develop CLB immunosensors. Detection of CLB was performed through direct competitive format, whereby the CLB in sample solutions were competing with CLB conjugated with horseradish peroxide (CLB-HRP) to bind with Ab. The change in current value was analyzed through CA for quantification of CLB amount in the sample. The immunoassay conditions for these immunosensors were optimized by using RSM/CCD, whereby the incubation temperature, Ag incubation time and %blocking were determined as significant parameters. The resulting immunosensors exhibited excellent reproducibility with low standard deviation (SD) value. These immunosensors also very selective towards CLB in comparison with other antibiotics from same family group (β-agonist) and another group of antibiotics. Based on storage stability study, these immunosensors can retain its performance up to 95% after a month storage at 4 °C. Thus, highly reproducible, sensitive and stable immunosensors for detection of CLB in the real samples were developed and satisfactorily meet the requirement for actual application.

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Additional Metadata

Item Type: Thesis (Doctoral)
Subject: Biosensors
Subject: Food safety
Subject: Product safety
Call Number: ITMA 2018 19
Chairman Supervisor: Associate Professor Yusran Bin Sulaiman, PhD
Divisions: Institute of Advanced Technology
Depositing User: Mas Norain Hashim
Date Deposited: 06 Feb 2020 06:51
Last Modified: 06 Feb 2020 06:51
URI: http://psasir.upm.edu.my/id/eprint/76916
Statistic Details: View Download Statistic

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