RT journal article
T1 Optical imaging spectroscopy for rapid, primary screening of SARS-CoV-2: a proof of concept
A1 Gómez‑González, Emilio
A1 Barriga‑Rivera, Alejandro
A1 Fernández‑Muñoz, Beatriz
A1 Navas‑García, José Manuel
A1 Fernández‑Lizaranzu, Isabel
A1 Munoz‑González, Francisco Javier
A1 Parrilla‑Giráldez, Rubén
A1 Requena‑Lancharro, Desiree
A1 Gil‑Gamboa, Pedro
A1 Rosell‑Valle, Crsitina
A1 Gómez‑González, Carmen
A1 Mayorga‑Buiza, Maria José
A1 Martín‑López, María
A1 Muñoz, Olga
A1 Gómez Martín, Juan Carlos
A1 Relimpio López, María Isabel
A1 Aceituno‑Castro, Jesús
A1 Perales‑Esteve, Manuel A.
A1 Puppo‑Moreno, Antonio
A1 García Cózar, Francisco José
A1 Olvera Collantes, Lucía
A1 Gómez‑Díaz, Raquel
A1 de los Santos‑Trigo, Silvia
A1 Huguet‑Carrasco, Montserrat
A1 Rey, Manuel
A1 Gómez, Emilia
A1 Sánchez Pernaute, Rosario
A1 Padillo‑Ruiz, Javier
A1 Márquez‑Rivas, Javier
A2 BiomedicinaBiotecnología y Salud Pública
AB Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 mu L fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.
PB NATURE RESEARCH
SN 2045-2322
YR 2022
FD 2022-02
LK http://hdl.handle.net/10498/26562
UL http://hdl.handle.net/10498/26562
LA eng
NO The authors would like to gratefully acknowledge the assistance of the members of the Explosive Ordnance Disposal-Chemical, Biological, Radiological & Nuclear (EOD-CBRN) Group of the Spanish National Police, whose identities cannot be disclosed, and who are represented here by JMNG. The authors also thank Manuel Guerrero-Claro for his technical assistance with the implementation of the neural networks, and the companies CAMBRICO BIOTECH (Sevilla, Spain), CER "Dr. Gregorio Medina Blanco" (Espartinas, Sevilla, Spain) and SAMU (Sevilla, Spain) for their collaboration in the collection of human samples. This research was funded by Grants Number COV20-00080 and COV20-00173 of the 2020 Emergency Call for Research Projects about the SARS-CoV-2 virus and the COVID-19 disease of the Institute of Health 'Carlos III', Spanish Ministry of Science and Innovation, and by Grant Number EQC2019-006240-P funded by MICIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". ABR was supported by Grant Number RTI2018-094465-J-I00 funded by MICIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". This work has been supported by the European Commission through the Joint Research Center (JRC) HUMAINT project.
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026