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A method based on laser induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification and discrimination of specific bacteria strains (Pseudomonas aeroginosa, Escherichia coli and Salmonella typhimurium). Instant identification of the samples is achieved using a spectral library, which was obtained by analysis using a single laser pulse of representative samples and treatment by neural networks. The samples used in this study were divided into three groups, which were prepared on three different days. The results obtained allow the identification of the bacteria tested with a certainty of over 95%, and show that only a difference between the bacteria can cause identification. Single-shot measurements were sufficient for clear identification of the bacterial strains studied. The method can be developed for automatic real time, fast, reliable and robust measurements and can be packaged in portable systems for non-specialist users.

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An integrated amperometric fructose biosensor based on a gold electrode (AuE) modified with a self-assembled monolayer (SAM) of 3-mercaptopropionic acid (MPA) on which fructose dehydrogenase (FDH) and the mediator tetrathiafulvalene (TTF) are co-immobilized by cross-linking with glutaraldehyde is reported. Variables concerning the behavior of the biosensor were optimized by taking the slope value obtained for the fructose calibration plot in the 0.1-1.0 mM concentration range as a criterion of selection.At an applied potential of +0.20 V, a good repeatability of such slope values (RSD=6.7%, n=10) was obtained with no need to apply a cleaning or pretreatment procedure to the modified electrode. Moreover, results from five different TTF-FDH-MPA-AuEs yielded a RSD of 5.8%. The useful lifetime of one single biosensor was approximately 30 days, exhibiting a 93% of the original response on the 33rd day. A linear calibration graph was obtained for fructose over the 1.0×10 -5 -1.0×10 -3 M range, with a limit of detection of 2.4×10 -6 M. The effect of potential interferents was evaluated. The TTF-FDH-MPA-AuE also performed well in the flow-injection mode. The biosensor was used for the determination of fructose in real samples, and the results compared with those provided by using a commercial enzyme test kit.

An amperometric magnetoimmunosensor for the determination of human p53 protein is described in this work using a sandwich configuration involving the covalent immobilization of a specific capture antibody onto activated carboxylic-modified magnetic beads (HOOC-MBs) and incubation of the modified MBs with a mixture of the target protein and horseradish peroxidase-labeled antibody (HRP-anti-p53). The resulting modified MBs are captured by a magnet placed under the surface of a disposable carbon screen-printed electrode (SPCE) and the amperometric responses are measured at −0.20 V (vs. an Ag pseudo-reference electrode), upon addition of hydroquinone (HQ) as a redox mediator and H2O2 as the enzyme substrate. The magnetoimmunosensing platform was successfully applied for the detection of p53 protein in different cell lysates without any matrix effect after a simple sample dilution. The results correlated accurately with those provided by a commercial ELISA kit, thus confirming the immunosensor as an attractive alternative for rapid and simple determination of this protein using portable and affordable instrumentation.

The adulteration and traceability of olive oils are serious problems in the olive oil industry. In this work, a method based on laser-induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification, quality control, traceability, and adulteration detection of extra virgin olive oils. Instant identification of the samples is achieved using a spectral library, which was obtained by analysis of representative samples using a single laser pulse and treatment by NNs. The samples used in this study belong to four countries. The study also included different regions of each country. The results obtained allow the identification of the oils tested with a certainty of more than 95%. Single-shot measurements were enough for clear identification of the samples. The method can be developed for automatic real-time, fast, reliable, and robust measurements, and the system can be packed into portable form for non-specialist users.

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