posted on 2016-04-11, 08:38authored byKathy Ridgway
Determination of low levels of chemicals unintentionally present in foods (trace
contaminants) often requires extensive wet chemistry extraction and clean-up
regimes prior to instrumental analysis and this is usually the bottleneck in
analysis. In order to determine if levels pose a human health risk, rapid
reliable methods are required that can unequivocally identify and quantify
contaminants at trace levels. In particular, current methods for targeted food
taint analysis have long extraction times and rapid screening methods such as
direct headspace, do not provide the sensitivity required.
In order to address the issues raised above, this study set out to review all
aspects of sample preparation and the applicability of each technique for the
determination of trace organic contaminants in foods. Following the review,
solid-phase dynamic extraction (SPDE) was evaluated for the determination of
example food contaminants furan and the BTEX compounds (benzene,
toluene, ethyl benzene and 0, rn, and p-xylene). Stir bar sorptive extraction
(SBSE) was also evaluated for the determination of furan, benzene and
toluene. A SBSE method for the determination of furan in food and beverages
was developed which gave the advantage of extraction at ambient
temperature, (thus minimising potential formation) and was comparable to
direct static headspace in performance and sensitivity.
The use of SBSE was then evaluated for use as a generic screening method
for compounds known to cause taints in foods. Twenty example compounds
were chosen based on previously reported taints from a range of origins and
included those most commonly investigated (such as halogenated phenols
and anisoles). The optimised SBSE method was compared to the more
established techniques, direct static headspace and steam distillation
extraction using Likens Nickerson apparatus. The SBSE method provided an
increase in sensitivity for most compounds and further improvements were
demonstrated for more targeted analysis, using a GC-MS, GC-MS/MS and
GC-HRMS instrumentation.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2008
Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.