Abstract
The rapid and accurate detection of food pathogens plays
a critical role in the early prevention of foodborne
epidemics. Current bacteria identification practices,
including colony counting, polymerase chain reaction
(PCR) and immunological methods, are time consuming and
labour intensive; they are not ideal for achieving the
required immediate diagnosis. Different SERS substrates
have been studied for the detection of foodborne
microbes. The majority of the approaches are either based
on costly patterning techniques on silicon or glass
wafers or on methods which have not been tested in large
scale fabrication. We demonstrate the feasibility of
analyte specific sensing using mass-produced,
polymer-based low-cost SERS substrate in analysing the
chosen model microbe with biological recognition. The use
of this novel roll-to-roll fabricated SERS substrate was
combined with optimised gold nanoparticles to increase
the detection sensitivity. Distinctive SERS spectral
bands were recorded for Listeria innocua ATCC 33090 using
an in-house build (785 nm) near infra red (NIR) Raman
system. Results were compared to both those found in the
literature and the results obtained from a commercial
time-gated Raman system with a 532 nm wavelength laser
excitation. The effect of the SERS enhancer metal and the
excitation wavelength on the detected spectra was found
to be negligible. The hypothesis that disagreements
within the literature regarding bacterial spectra results
from conditions present during the detection process has
not been supported. The sensitivity of our SERS detection
was improved through optimization of the concentration of
the sample inside the hydrophobic polydimethylsiloxane
(PDMS) wells. Immunomagnetic separation (IMS) beads were
used to assist the accumulation of bacteria into the path
of the beam of the excitation laser. With this
combination we have detected Listeria with gold enhanced
SERS in a label free manner from such low sample
concentrations as 104 CFU ml-1.
Original language | English |
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Pages (from-to) | 62981-62989 |
Journal | RSC Advances |
Volume | 6 |
Issue number | 67 |
DOIs | |
Publication status | Published - 2016 |
MoE publication type | A1 Journal article-refereed |