{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:03:23Z","timestamp":1792789403180,"version":"4.5.0"},"reference-count":31,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cukurova University Scientific Research Unit","award":["FBA-2024-16557"],"award-info":[{"award-number":["FBA-2024-16557"]}],"id":[{"id":"https:\/\/ror.org\/05wxkj555","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Artificial intelligence and image-based deep learning offer promising non-destructive approaches for rapid fish quality assessment. This study evaluated the potential of deep learning models to classify freshness-related sensory attributes and predict color parameters of European seabass (Dicentrarchus labrax) fillets using image data acquired under controlled illumination. Seven sensory quality parameters and six color attributes were assessed. A structured image dataset was obtained at a fixed camera-to-sample distance of 45 cm, and 65 independently trained models, comprising 35 classification and 30 regression models, were developed using DenseNet121, InceptionV3, MobileNet, VGG16, and VGG19 architectures. Model training and evaluation were performed using group-based 10-fold cross-validation to ensure that images from the same fillet were not shared between the training and test sets. Classification performance depended strongly on both architecture and target parameter. DenseNet121 achieved the highest accuracy for purchasability (92.60%), water release (90.20%), odor (75.60%), color (77.40%), and disintegration (87.70%), whereas MobileNet performed best for texture (81.20%) and total score (57.70%). Purchasability was the most predictable sensory parameter across architectures, with a mean accuracy of 86.84%, whereas total score was the least predictable at 45.64%. In the regression tasks, DenseNet121 achieved the lowest MAE for a* (0.680), L* (2.467), hue (0.961), and whiteness (2.357), while MobileNet performed best for b* (1.200) and chroma (1.024). Grad-CAM visualizations indicated that model activations were predominantly associated with the fillet region, although more difficult classes showed more fragmented and occasionally edge- or background-associated activation patterns. Overall, DenseNet121 provided the strongest and most consistent performance, ranking first for nine of the 13 prediction targets. These findings demonstrate the potential of RGB imaging and deep learning for rapid, objective, and non-destructive assessment of selected freshness and quality attributes of European seabass fillets.<\/jats:p>","DOI":"10.3390\/foods15193385","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:09:08Z","timestamp":1790161748000},"page":"3385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Assessment of European Seabass (Dicentrarchus labrax) Fillet Freshness and Quality Using Image Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Nermin","family":"Deli\u00e7ay","sequence":"first","affiliation":[{"name":"Department of Seafood Processing Technology, Cukurova University, 01330 Adana, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7769-9225","authenticated-orcid":false,"given":"Yesim","family":"Ozogul","sequence":"additional","affiliation":[{"name":"Department of Seafood Processing Technology, Cukurova University, 01330 Adana, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Sakhi","family":"Mohammad Hamidy","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Istanbul Arel University, 34537 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9450-0135","authenticated-orcid":false,"given":"Fethiye","family":"Takada\u015f","sequence":"additional","affiliation":[{"name":"Department of Chemical Technology, Vocational School, University of Alanya, 07400 Antalya, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9817-1371","authenticated-orcid":false,"given":"Yusuf","family":"Kuvvetli","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Cukurova University, 01330 Adana, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","unstructured":"FAO (2026). The State of World Fisheries and Aquaculture 2026\u2014Blue Transformation: Turning Vision into Impact, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Singh, R.D., Arambam, K., Singh, A.M., and Singh, S.K. (2025). Health and nutritional benefits of fish as human food. Food Security, Nutrition and Sustainability Through Aquaculture Technologies, Springer Nature.","DOI":"10.1007\/978-3-031-75830-0_3"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hanna, S., Greta, K., Snje\u017eana, Z., and Jelka, P. (2026). Nutritional Composition, Health Benefits and Claim Conditions of Fish from Aquaculture: A Narrative Review. Nutrients, 18.","DOI":"10.3390\/nu18081270"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"uoag086","DOI":"10.1093\/bulcsj\/uoag086","article-title":"Evolution of freshness indicators and rapid sensing technologies for seafood: Toward a global platform for quality evaluation","volume":"99","author":"Fuchiwaki","year":"2026","journal-title":"Bull. Chem. Soc. Jpn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rosati, S., Maiuro, L., Lombardi, S.J., Iaffaldano, N., Di Iorio, M., Cariglia, M., Lopez, F., Cofelice, M., Tremonte, P., and Sorrentino, E. (2025). Integrated biotechnological strategies for the sustainability and quality of Mediterranean Sea bass (Dicentrarchus labrax) and Sea bream (Sparus aurata). Foods, 14.","DOI":"10.3390\/foods14061020"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ali, A., Wei, S., Ali, A., Khan, I., Sun, Q., Xia, Q., Wang, Z., Han, Z., Liu, Y., and Liu, S. (2022). Research progress on nutritional value, preservation and processing of fish\u2014A review. Foods, 11.","DOI":"10.3390\/foods11223669"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2363342","DOI":"10.1155\/are\/2363342","article-title":"Evaluation of Post-Mortem Colour Changes in Fish Eye, Gills and Skin Using Hyperspectral Imaging","volume":"2026","author":"Gleni","year":"2026","journal-title":"Aquac. Res."},{"key":"ref_8","unstructured":"Das, A., Dutta, M.L., and Sarma, K.K. (2025). Fish Freshness Monitoring Technology: A Review. Sustainable Development, Innovation and Green Technology (ICASDIGT-2024), Nova Science Publishers, Incorporated."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3576183","DOI":"10.1155\/ijfo\/3576183","article-title":"Reviewing the correlation of fish quality alteration and in-package headspace composition: Evidence from a pH freshness indicator case study","volume":"2025","author":"Basdeki","year":"2025","journal-title":"Int. J. Food Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108687","DOI":"10.1016\/j.fbio.2026.108687","article-title":"Combined Analysis of Microbial Diversity, Water Migration, and Volatile Spoilage Profile Changes in Modified Atmosphere Packaged Crucian Carp (Carassius auratus) Fillets During Cold Storage","volume":"79","author":"Chen","year":"2026","journal-title":"Food Biosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s13197-025-06497-4","article-title":"Instrumental detection of fish freshness in research and food industry: A review","volume":"63","author":"Kundu","year":"2026","journal-title":"J. Food Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, G., Thongkaew, K., and Chaiprapat, S. (2026). A Review of Non-Destructive Technologies for Quality Assessment in Aquaculture. Aquac. J., 6.","DOI":"10.3390\/aquacj6010003"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s12393-024-09385-3","article-title":"A comprehensive review of advanced deep learning approaches for food freshness detection","volume":"17","author":"Singh","year":"2025","journal-title":"Food Eng. Rev."},{"key":"ref_14","first-page":"101734","article-title":"Enhancing optical non-destructive methods for food quality and safety assessments with machine learning techniques: A survey","volume":"19","author":"Wang","year":"2025","journal-title":"J. Agric. Food Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hamidy, S.M., Kuvvetli, Y., Sakarya, Y., \u00d6zk\u00fct\u00fck, S.T., and \u00d6zo\u011ful, Y. (2025). Deep learning-based prediction of fish freshness and purchasability using multi-angle image data. Foods, 15.","DOI":"10.3390\/foods15010068"},{"key":"ref_16","unstructured":"Nazir, A., Hussain, A., and Assad, A. (2025). CNN in food industry: Current. Artificial Intelligence in the Food Industry: Enhancing Quality and Safety, CRC Press."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ding, H., Hou, H., Wang, L., Cui, X., Yu, W., and Wilson, D.I. (2025). Application of convolutional neural networks and recurrent neural networks in food safety. Foods, 14.","DOI":"10.3390\/foods14020247"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.foodcont.2005.10.019","article-title":"Development of Quality Index Method (QIM) scheme for fresh cod (Gadus morhua) fillets and application in shelf life study","volume":"18","author":"Bonilla","year":"2007","journal-title":"Food Control"},{"key":"ref_19","unstructured":"Hunter, R.S. (1975). Scales for the measurements of color difference. The Measurement of Appearance, Wiley."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_22","unstructured":"Sakib, S., Ahmed, N., Kabir, A.J., and Ahmed, H. (2026, September 18). An Overview of Convolutional Neural Network: Its Architecture and Applications. Available online: https:\/\/www.preprints.org\/manuscript\/201811.0546\/v1."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zorgui, S., Chaabene, S., Bouaziz, B., Batatia, H., and Chaari, L. (2020). A convolutional neural network for lentigo diagnosis. International Conference on Smart Homes and Health Telematics, Springer International Publishing.","DOI":"10.1007\/978-3-030-51517-1_8"},{"key":"ref_24","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hasanah, S.A., Pravitasari, A.A., Abdullah, A.S., Yulita, I.N., and Asnawi, M.H. (2023). A deep learning review of resnet architecture for lung disease Identification in CXR Image. Appl. Sci., 13.","DOI":"10.3390\/app132413111"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.procs.2022.08.050","article-title":"Sentiment Analysis from Images using VGG19 based Transfer Learning Approach","volume":"204","author":"Meena","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1080\/00405000.2022.2124629","article-title":"Artificial neural networks approach for prediction of CIELab values for yarn after dyeing and finishing process","volume":"114","year":"2023","journal-title":"J. Text. Inst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Robeson, S.M., and Willmott, C.J. (2023). Decomposition of the mean absolute error (MAE) into systematic and unsystematic components. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0279774"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"132343","DOI":"10.1016\/j.foodchem.2022.132343","article-title":"Real-time monitoring the color changes of large yellow croaker (Larimichthys crocea) fillets based on hyperspectral imaging empowered with artificial intelligence","volume":"382","author":"Wang","year":"2022","journal-title":"Food Chem."}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/19\/3385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:36:23Z","timestamp":1790163383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/19\/3385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":31,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["foods15193385"],"URL":"https:\/\/doi.org\/10.3390\/foods15193385","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]}}}