Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3818
Title: Electrochemical enzymatic biosensing of neotame in sweeteners by experimental and computational methods
Authors: Lephalala, Matshidiso 
Issue Date: 2020
Abstract: 
An enzymatic biosensor comprises of an enzyme, which recognizes and then reacts
with the target analyte producing a chemical signal. In this type of sensor, an electrode
is a key component that is employed as a solid support for the immobilization of
biomolecules and electron movement. This work focuses on two case studies to
assess the signal enhancing strategy that can potentially be used to quantify Neotame
(NTM) in food and non-alcoholic beverages.
The first case study involves a highly sensitive electrochemical enzymatic biosensor
for the detection of NTM in the soft drinks developed, based on multiwalled carbon
nanotubes (MWCNTs) decorated with aloe vera-derived gold nanoparticles (AuNPs)
and carboxylesterase (CaE) enzyme. This electrochemical biosensor showed high
sensitivity with a limit of detection (LOD) and limit of quantification (LOQ) of 27 μg L-1
and 83 μg L-1, respectively. The calibration plot revealed a linear dependence of the
cathodic peak current on the NTM concentration profile with anR2 of 0.9829, indicating
an improved electrocatalytic property of the glassy carbon electrode. The viability of
the proposed strategy was confirmed by assessing the interactions between the
enzyme and the analyte using computational methods. The density functional theory
(DFT) calculations of NTM showed a HOMO–LUMO energy gap of -0.46618 eV,
indicating that NTM can act as a good electron donor. Moreover, adsorption and
enzyme-analyte docking studies were carried out to better understand the redox
mechanism. These outcomes showed that NTM formed hydrogen bonds with LEU
249, GLU251, and other amino acids of the hydrophobic channel of the binding sites,
making it easier for the redox reaction to take place for the detection of NTM. The results confirmed that the aloe vera-derived AuNPs are good platforms for
immobilizing CaE because of their high surface area, encouraging an electron transfer
from NTM to form a substrate-enzyme complex, contributing to improved biosensing
signals.
The second case study deals with an enzymatic biosensor developed, based on
graphene oxide (GO) anchored with honey-derived nickel nanoparticles (NiNPs) and
alcohol oxidase (AOx) enzyme. The biosensor showed high sensitivity with a limit of
quantification (LOQ) of 47 μg L-1 and a limit of detection (LOD) of 15 μg L-1,
respectively. The calibration curve of the cathodic peak current on the analyte
concentration profile showed an improved electrocatalytic property with an R2 of
0.9926. The interactions between the enzyme and analyte were assessed using
computational tools to confirm the viability of the proposed biosensor. A HOMO–
LUMO energy gap of -0.46618 eV was confirmed using density functional theory (DFT)
calculations, this suggested that NTM has great potential to act as an electron donor.
Analyte-enzyme and adsorption docking studies were carried out for a better
comprehension of the redox reaction mechanisms. These outcomes indicated that
NTM forms hydrogen bonds with TRP 47, ARG 56, VAL 328, PRO 55, and other amino
acids, thus assisting the redox reaction for the determination of NTM. The results
confirmed that the honey-derived NiNPs have a high surface area, which acted as a
good platform to immobilize AOx so that the electrons can be transferred from NTM to
form a substrate-enzyme composite to give out an improved biosensing signal.
Moreover, the magnified catalytic activity of these two biosensors for the determination
of NTM in soft drinks showed great potential in the beverage industry.
Description: 
Submitted in fulfilment of the requirements of the degree of Master of Applied Science in Chemistry in the Faculty of Applied Sciences at the Durban University of Technology, 2020.
URI: https://hdl.handle.net/10321/3818
DOI: https://doi.org/10.51415/10321/3818
Appears in Collections:Theses and dissertations (Applied Sciences)

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