Towards Electrochemical Sensor Based on Molecularly Imprinted Polypyrrole for the Detection of Bacteria—Listeria monocytogenes
Abstract
Detecting bacteria—Listeria monocytogenes—is an essential healthcare and food industry issue. The objective of the current study was to apply platinum (Pt) and screen-printed carbon (SPCE) electrodes modified by molecularly imprinted polymer (MIP) in the design of an electrochemical sensor for the detection of Listeria monocytogenes. A sequence of potential pulses was used to perform the electrochemical deposition of the non-imprinted polypyrrole (NIP-Ppy) layer and Listeria monocytogenes-imprinted polypyrrole (MIP-Ppy) layer over SPCE and Pt electrodes. The bacteria were removed by incubating Ppy-modified electrodes in different extraction solutions (sulphuric acid, acetic acid, L-lysine, and trypsin) to determine the most efficient solution for extraction and to obtain a more sensitive and repeatable design of the sensor. The performance of MIP-Ppy- and NIP-Ppy-modified electrodes was evaluated by pulsed amperometric detection (PAD). According to the results of this research, it can be assumed that the most effective MIP-Ppy/SPCE sensor can be designed by removing bacteria with the proteolytic enzyme trypsin. The LOD and LOQ of the MIP-Ppy/SPCE were 70 CFU/mL and 210 CFU/mL, respectively, with a linear range from 300 to 6700 CFU/mL.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.