Mycotoxins are a group of secondarymetabolites produced by different species of filamentous
fungi and pose serious threats to food safety due to their serious human and animal health impacts
such as carcinogenic, teratogenic and hepatotoxic effects. Conventional methods for the detection
of mycotoxins include gas chromatography and high-performance liquid chromatography coupled
with mass spectrometry or other detectors (fluorescence or UV detection), thin layer chromatography
and enzyme-linked immunosorbent assay. These techniques are generally straightforward and yield
reliable results; however, they are time-consuming, require extensive preparation steps, use large-scale
instruments, and consume large amounts of hazardous chemical reagents. Rapid detection of
mycotoxins is becoming an increasingly important challenge for the food industry in order to effectively
enforce regulations and ensure the safety of food and feed. In this sense, several studies have been
done with the aim of developing strategies to detect mycotoxins using sensing devices that have
high sensitivity and specificity, fast analysis, low cost and portability. The latter include the use
of microarray chips, multiplex lateral flow, Surface Plasmon Resonance, Surface Enhanced Raman
Scattering and biosensors using nanoparticles. In this perspective, thin film sensors have recently
emerged as a good candidate technique to meet such requirements. This review summarizes the
application and challenges of thin film sensor devices for detection of mycotoxins in food matrices.
This work was funded by Project POCI-01–0145-FEDER-006984—Associate Laboratory LSRE-LCM,
Project UID/BIO/04469/2013—CEB and strategic project PEst-OE/AGR/UI0690/2014—CIMO all funded by
European Regional Development Fund (ERDF) through COMPETE2020—Programa Operacional Competitividade
e Internacionalização (POCI)—and by national funds through FCT—Fundação para a Ciência e a Tecnologia
I.P. Andréia O. Santos also acknowledges the research grant provided by the Associate Laboratory LSRE-LCM
under the Projects UID/EQU/50020/2013 and POCI-01-0145-FEDER-006984. Andreia Vaz acknowledges the
research grant provided by the Portuguese Foundation for Science and Technology (FCT), reference number
SFRH/BD/129775/2017. The APC was kindly waived by MDPI.
