dc.contributor.author | Mishra, Puneet | |
dc.contributor.author | Albano-Gaglio, Michela | |
dc.contributor.author | Font-i-Furnols, Maria | |
dc.contributor.other | Indústries Alimentàries | ca |
dc.date.accessioned | 2024-06-06T12:27:00Z | |
dc.date.available | 2024-06-06T12:27:00Z | |
dc.date.issued | 2024-04-18 | |
dc.identifier.citation | Mishra, Puneet, Michela Albano‐Gaglio, and Maria Font‐i‐Furnols. 2024. “A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction”. Journal of Chemometrics, April. doi:10.1002/cem.3552. | ca |
dc.identifier.issn | 0886-9383 | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12327/3035 | |
dc.description.abstract | This study demonstrates a new approach to process hyperspectral images
where both the contextual spatial information as well as the spectral
information are used to predict sample properties. The deep contextual spatial
information is extracted using the deep feature extraction from pretrained
resnet-18 deep learning architecture, while the spectral information was
readily available as the average pixel values. To fuse the information in a
complementary way, a multiblock modeling approach called sequential
orthogonalized partial least squares was used. The sequential model guarantees
that the information learned is complementary from spatial and spectral
domains. The potential of the approach is demonstrated to predict several
physical and chemical properties in pork bellies. | ca |
dc.description.sponsorship | he authors would like to thank the researchers Begonya Marcos (IRTA) and Juan Florencio Tejeda (UEX) and the IRTA technicians Albert Brun, Agustí Quintana, Albert Rossell, Adrià Pacreu, Cristina Canals, and Joel González, for their help in the execution of the project. Thanks also given to José M. Martínez for their contribution in the analysis of fatty acids. The authors thank the Spanish National Institute of Agricultural Research (INIA) for the scholarship to Michela Albano-Gaglio (PRE2019-089669). This work was partly funded by the Spanish Ministry of Science and Innovation, project number RTI2018-096993-B-I00. The CERCA programme from the Generalitat de Catalunya is also acknowledged. | ca |
dc.format.extent | 9 | ca |
dc.language.iso | eng | ca |
dc.publisher | Wiley | ca |
dc.relation.ispartof | Journal of Chemometrics | ca |
dc.rights | Attribution 4.0 International | ca |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction | ca |
dc.type | info:eu-repo/semantics/article | ca |
dc.description.version | info:eu-repo/semantics/publishedVersion | ca |
dc.rights.accessLevel | info:eu-repo/semantics/openAccess | |
dc.embargo.terms | cap | ca |
dc.relation.projectID | MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/RTI2018-096993-B-I00/ES/CLASIFICACION Y EVALUACION DE LA CALIDAD GLOBAL DE LA PANCETA DE CERDO MEDIANTE TECNOLOGIAS NO DESTRUCTIVAS Y PERCEPCION POR PARTE DE LOS CONSUMIDORES/ | ca |
dc.subject.udc | 663/664 | ca |
dc.identifier.doi | https://doi.org/10.1002/cem.3552 | ca |
dc.contributor.group | Qualitat i Tecnologia Alimentària | ca |