dc.creator | Gómez-González, Emilio | es |
dc.creator | Fernández Muñoz, Beatriz | es |
dc.creator | Barriga-Rivera, Alejandro | es |
dc.date.accessioned | 2021-08-11T10:31:38Z | |
dc.date.available | 2021-08-11T10:31:38Z | |
dc.date.issued | 2021-08 | |
dc.identifier.citation | Gómez-González, E., Fernández Muñoz, B. y Barriga-Rivera, A. [et al.] (2021). Hyperspectral image processing for the identification and quantification of lentiviral particles in fluid samples. Scientific Reports, 11, 16201. | |
dc.identifier.issn | 2045-2322 | es |
dc.identifier.uri | https://hdl.handle.net/11441/116730 | |
dc.description.abstract | Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks. The detection was successfully achieved in preparations of phosphate buffered solution and artificial saliva, with an equivalent pixel volume of 4 nL and lowest concentration of 800 TU·μL−1. This method constitutes an innovative approach that could be potentially used at point of care for rapid mass screening of viral infectious diseases and monitoring of the SARS-CoV-2 pandemic. | es |
dc.description.sponsorship | Instituto de Salud Carlos III COV20-00080 and COV20-00173 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación EQC2019-006240-P | es |
dc.description.sponsorship | Comisión Europea JRC HUMAINT project | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer Nature | es |
dc.relation.ispartof | Scientific Reports, 11, 16201. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Near-infrared spectroscopy | es |
dc.subject | SARS-CoV-2 | es |
dc.subject | Viral infection | es |
dc.title | Hyperspectral image processing for the identification and quantification of lentiviral particles in fluid samples | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Física Aplicada III | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | |
dc.relation.projectID | EQC2019-006240-P | es |
dc.relation.publisherversion | https://www.nature.com/articles/s41598-021-95756-3#article-info | es |
dc.identifier.doi | 10.1038/s41598-021-95756-3 | es |
dc.contributor.group | Universidad de Sevilla. TEP203: Física Interdisciplinar, Fundamentos y Aplicaciones | es |
dc.journaltitle | Scientific Reports | es |
dc.publication.volumen | 11 | es |
dc.publication.initialPage | Article number: 16201 | es |