Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.S., Shanthini; George, Sudhish N.; Francis, Jobin; George, Sony Title A name given to the resource SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Food Control;Volume;182;Issue;;Article No.;111870; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.foodcont.2025.111870" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.foodcont.2025.111870</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022807709?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022807709?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.S. S., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; George S.N., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; Francis J., Department of Computer Science, Christ University Bangalore, Karnataka, India; George S., Department of Computer Science, Norwegian University of Science and Technology Gjik, Norway Description An account of the resource Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models a custom spectral CNN, ResNet 101, and a bidirectional LSTM model are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method's superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder. 2025 Elsevier Ltd Subject The topic of the resource Bruise categorization; Fruit quality analysis; Postharvest; Spatial spectral CNN; Vis-NIR imaging Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 9567135; CODEN: FOOCE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online