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dc.contributor.author Gutiérrez, Pablo
dc.contributor.author Godoy, Sebastián E.
dc.contributor.author Torres, Sergio
dc.contributor.author Oyarzún, Patricio
dc.contributor.author Sanhueza, Ignacio
dc.contributor.author Díaz-García, Victor
dc.contributor.author Contreras-Trigo, Braulio
dc.contributor.author Coelho, Pablo
dc.date.accessioned 2024-09-26T00:27:19Z
dc.date.available 2024-09-26T00:27:19Z
dc.date.issued 2020-08-02
dc.identifier.issn 1424-8220
dc.identifier.uri https://repositorio.uss.cl/handle/uss/12209
dc.description Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.description.abstract In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were designed and synthesized by chemically bonding gold nanoparticles (AuNPs) with aptamer bioreceptors highly selective for four widely used antibiotics in the field of veterinary medicine, namely, Kanamycin, Ampicillin, Oxytetracycline and Sulfadimethoxine. When molecules of the antibiotics are present in the milk sample, the interaction with the aptamers induces random AuNP aggregation. This phenomenon modifies the initial absorption spectrum of the milk sample without antibiotics, producing spectral features that indicate both the presence of antibiotics and, to some extent, its concentration. Secondly, we designed and constructed an electro-opto-mechanic device that performs automatic high-resolution spectral data acquisition in a wavelength range of 400 to 800 nm. Thirdly, the acquired spectra were processed by a machine-learning algorithm that is embedded into the acquisition hardware to determine the presence and concentration ranges of the antibiotics. Our approach outperformed state-of-the-art standardized techniques (based on the 520/620 nm ratio) for antibiotic detection, both in speed and in sensitivity. en
dc.language.iso eng
dc.relation.ispartof vol. 20 Issue: no. 16 Pages: 1-13
dc.source Sensors (Switzerland)
dc.title Improved antibiotic detection in raw milk using machine learning tools over the absorption spectra of a problem-specific nanobiosensor en
dc.type Carta
dc.identifier.doi 10.3390/s20164552
dc.publisher.department Facultad de Ingeniería y Tecnología
dc.publisher.department Facultad de Ingeniería, Arquitectura y Diseño
dc.publisher.department Facultad de Ciencias de la Naturaleza


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