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• W4385330897 abstract "In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures." @default.
• W4385330897 created "2023-07-29" @default.
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• W4385330897 date "2023-07-28" @default.
• W4385330897 modified "2023-09-26" @default.
• W4385330897 title "Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis" @default.
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• W4385330897 doi "https://doi.org/10.1021/acssensors.3c01114" @default.
• W4385330897 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37505186" @default.
• W4385330897 hasPublicationYear "2023" @default.
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