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               <dc:title>A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction</dc:title>
               <dc:creator>Mishra, Puneet</dc:creator>
               <dc:creator>Albano-Gaglio, Michela</dc:creator>
               <dc:creator>Font-i-Furnols, Maria</dc:creator>
               <dc:contributor>Indústries Alimentàries</dc:contributor>
               <dc:contributor>Qualitat i Tecnologia Alimentària</dc:contributor>
               <dc:description>This study demonstrates a new approach to process hyperspectral images&#xd;
where both the contextual spatial information as well as the spectral&#xd;
information are used to predict sample properties. The deep contextual spatial&#xd;
information is extracted using the deep feature extraction from pretrained&#xd;
resnet-18 deep learning architecture, while the spectral information was&#xd;
readily available as the average pixel values. To fuse the information in a&#xd;
complementary way, a multiblock modeling approach called sequential&#xd;
orthogonalized partial least squares was used. The sequential model guarantees&#xd;
that the information learned is complementary from spatial and spectral&#xd;
domains. The potential of the approach is demonstrated to predict several&#xd;
physical and chemical properties in pork bellies.</dc:description>
               <dc:date>2025-10-22T11:07:39Z</dc:date>
               <dc:date>2025-10-22T11:07:39Z</dc:date>
               <dc:date>2024-04-18</dc:date>
               <dc:type>info:eu-repo/semantics/article</dc:type>
               <dc:identifier>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.</dc:identifier>
               <dc:identifier>0886-9383</dc:identifier>
               <dc:identifier>http://hdl.handle.net/20.500.12327/3035</dc:identifier>
               <dc:identifier>https://doi.org/10.1002/cem.3552</dc:identifier>
               <dc:language>eng</dc:language>
               <dc:relation>Journal of Chemometrics</dc:relation>
               <dc:relation>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/</dc:relation>
               <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <dc:rights>Attribution 4.0 International</dc:rights>
               <dc:publisher>Wiley</dc:publisher>
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