Hyperspectral image processing for the identification and quantification of lentiviral particles in fluid samples

DOI: 
10.1038/s41598-021-95756-3
Publication date: 
24/08/2021
Main author: 
Gomez-Gonzalez, Emilio
IAA authors: 
Muñoz, Olga;Martin, Juan Carlos Gomez;Aceituno-Castro, Jesus
Authors: 
Gomez-Gonzalez, Emilio;Fernandez-Muñoz, Beatriz;Barriga-Rivera, Alejandro;Navas-Garcia, Jose Manuel;Fernandez-Lizaranzu, Isabel;Munoz-Gonzalez, Francisco Javier;Parrilla-Giraldez, Ruben;Requena-Lancharro, Desiree;Guerrero-Claro, Manuel;Gil-Gamboa, Pedro;Rosell-Valle, Cristina;Gomez-Gonzalez, Carmen;Mayorga-Buiza, Maria Jose;Martin-Lopez, Maria;Muñoz, Olga;Martin, Juan Carlos Gomez;Lopez, Maria Isabel Relimpio;Aceituno-Castro, Jesus;Perales-Esteve, Manuel A.;Puppo-Moreno, Antonio;Cozar, Francisco Jose Garcia;Olvera-Collantes, Lucia;de los Santos-Trigo, Silvia;Gomez, Emilia;Pernaute, Rosario Sanchez;Padillo-Ruiz, Javier;Marquez-Rivas, Javier
Journal: 
Scientific Reports
Publication type: 
Article
Volume: 
11
Pages: 
16201
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.μ ?L<SUP>−1</SUP>. 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.
Database: 
ADS
URL: 
https://ui.adsabs.harvard.edu/#abs/2021NatSR..1116201G/abstract
ADS Bibcode: 
2021NatSR..1116201G