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In this work, a simple and cost-efficient aptasensor strip is developed for the rapid detection of OTA in food samples. The biosensor is based on the lateral flow assay concept using an OTA-specific aptamer for biorecognition of the target analyte. The strip consists of a sample pad, a conjugate pad, a nitrocellulose membrane (NC) and an absorbent pad. The conjugate pad is loaded with the OTA-specific aptamer conjugated with gold nanoparticles (AuNPs). The test line of the NC membrane is loaded with a specific OTA-aptamer probe and the control line is loaded with a control probe. The assay is based on a competitive format, where the OTA present in the sample combines with the OTA aptamer-AuNP conjugate and prevents the interaction between the specific probe immobilized on the test line and the OTA aptamer-AuNP conjugates; therefore, the color intensity of the test line decreases as the concentration of OTA in the sample increases. Qualitative detection of OTA is performed visually, while quantification is performed by reflectance colorimetry using a commercial scanner and image analysis. All the parameters of the assay are investigated in detail and the analytical features are established. The visual limit of detection (LOD) of the strip is 0.05 ng mL−1, while the LOD for semi-quantitative detection using reflectance colorimetry is 0.02 ng mL−1. The lateral flow strip aptasensor is applied to the detection of OTA in wine, beer, apple juice and milk samples with recoveries in the range from 91 to 114%. The assay exhibits a satisfactory selectivity for OTA with respect to other mycotoxins and lasts 20 min. Therefore, the lateral flow strip aptasensor could be useful for the rapid, low-cost and fit-for-purpose on-site detection of OTA in food samples. [ABSTRACT FROM AUTHOR]

In an attempt to expand the coordination chemistry of N,N′-bis(2,4-dicarboxyphenyl)-oxalamide (H6L) ligand, we isolated and structurally characterized two new Fe(II) Metal-Organic Frameworks (MOFs), namely [Fe2(H2L)(H2O)5] (3D-Fe-MOF) and [Fe(H4L)(H2O)2]∙2H2O, (2D-Fe-MOF) by carefully adjusting the reaction conditions to achieve the optimal degree of deprotonation of the bridging ligand. Both MOFs were found stable in water, as evidenced by powder X-ray diffraction data and their ability to sorb glucose (GLU) from either an aqueous solution or artificial sweat was investigated only to show negligible sorption. A graphite paste sensor (GPE) using the 3D-Fe-MOF as a modifier was fabricated. The 3D-Fe-MOF modified GPE was assessed for non-enzymatic GLU detection in aqueous solution at pH 6 via differential pulse voltammetry and the preliminary results were discussed. [ABSTRACT FROM AUTHOR]

A label-free optical biosensor for the fast simultaneous determination of three mycotoxins, aflatoxin B 1 (AFB 1 ), fumonisin B 1 (FB 1 ) and deoxynivalenol (DON), in beer samples is presented. The biosensor is based on an array of ten Mach-Zehnder interferometers (MZIs) monolithically integrated along with their respective broad-band silicon light sources onto a single chip. Multi-analyte determination is accomplished by functionalizing the sensing arms of individual MZIs with mycotoxin-protein conjugates. Assay is performed by pumping over the chip mixtures of calibrators or samples with a mixture of specific monoclonal antibodies, followed by reaction with a secondary anti-mouse IgG antibody. Reactions are monitored in real-time by continuously recording the MZI output spectra, which are then subjected to Discrete Fourier Transform to convert spectrum shifts to phase shifts. The detection limits achieved for AFB 1 , FB 1 and DON were 0.8, 5.6 and 24 ng/ml, respectively, while the assay duration was 12 min. Recovery values ranging from 85 to 115% were determined in beer samples spiked with known concentrations of the three mycotoxins. In addition, beers of different types and origin were analysed with the biosensor developed and the results were compared with those provided by established laboratory methods, further supporting the accuracy of the proposed device., (Copyright © 2018 Elsevier B.V. All rights reserved.)