{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T06:33:27Z","timestamp":1793082807127,"version":"4.5.0"},"reference-count":34,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ift.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Food Science"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      Image\u2010based, nondestructive classification of hourly meat\u2010freshness stages is important for food\u2010quality monitoring and supply\u2010chain management. However, detailed audits of open\u2010source datasets and their preprocessed subsets can reveal methodological defects that artificially inflate model performance. In this study, a Mendeley Data dataset for hourly meat\u2010freshness classification was audited at the file and original\u2010image levels. Two major defects were identified: The 36\u2010 and 48\u2010h beef classes contained byte\u2010identical images, as verified by MD5 hashes, and offline augmentation performed before data partitioning distributed variants of the same original images across the training, validation, and test sets, causing cross\u2010partition leakage. A leak\u2010free nine\u2010class benchmark containing 1849 verified original images was therefore constructed. The duplicated beef stages were represented as a single 36+\u00a0h beef class, whereas the distinct 36 and 48\u00a0h mutton stages were retained separately. MobileNetV3\u2010Large, EfficientNet\u2010B0, ShuffleNetV2, and MobileNetV2 were trained using online augmentation, focal loss, inverse\u2010frequency class weights, and five prespecified random seeds. MobileNetV3\u2010Large achieved the highest mean test accuracy (98.51%\u00a0\u00b1\u00a00.58%) and macro\n                      <jats:italic>F<\/jats:italic>\n                      1 score (98.54%\u00a0\u00b1\u00a00.55%) and ranked first in four of five runs. However, its advantage over EfficientNet\u2010B0 was not significant in any seed\u2010wise exact McNemar comparison after Holm correction. These findings demonstrate the risks of uncritical reuse of open\u2010source datasets and provide a leakage\u2010controlled, reproducible basis for evaluating compact models intended for mobile and edge deployment.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.71554","type":"journal-article","created":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T04:19:10Z","timestamp":1791692350000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Filtering Out Defects in Open\u2010Source Meat Freshness Datasets: A New Leak\u2010Free Benchmark and a Comparative Analysis of Four Different Mobile Deep Learning Models"],"prefix":"10.1111","volume":"91","author":[{"given":"Ahmet Can","family":"Citak","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Faculty of Technology Selcuk University  Konya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2737-2360","authenticated-orcid":false,"given":"Murat","family":"Koklu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Faculty of Technology Selcuk University  Konya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"Ahn S. S. X.Hu A.Damianou N. D.Lawrence andZ.Dai.2019. \u201cVariational Information Distillation for Knowledge Transfer.\u201d InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE.https:\/\/doi.org\/10.1109\/CVPR.2019.00938.","DOI":"10.1109\/CVPR.2019.00938"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2020.107332"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6060041"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3520029"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/info11020125"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2024.138755"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14145979"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2025.100851"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj\u2010cs.3200"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2025.100785"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263\u2010021\u201001453\u2010z"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2025.146430"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Howard A. M.Sandler G.Chu et\u00a0al.2019. \u201cSearching for MobileNetV3.\u201d InProceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV).IEEE.https:\/\/doi.org\/10.1109\/ICCV.2019.00140.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2382577.2382579"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25103198"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1021\/acssensors.4c02213"},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"Lin T. Y. P.Goyal R.Girshick K.He andP.Doll\u00e1r.2017. \u201cFocal Loss for Dense Object Detection.\u201d InProceedings of the IEEE International Conference on Computer Vision (ICCV).IEEE.https:\/\/doi.org\/10.1109\/ICCV.2017.324.","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_2_9_20_1","unstructured":"Loshchilov I. andF.Hutter.2017. \u201cSGDR: Stochastic Gradient Descent With Warm Restarts.\u201d InProceedings of the International Conference on Learning Representations (ICLR).https:\/\/OpenReview.net.https:\/\/doi.org\/10.48550\/arXiv.1608.03983."},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meatsci.2005.03.003"},{"key":"e_1_2_9_23_1","doi-asserted-by":"crossref","unstructured":"Park W. D.Kim Y.Lu andM.Cho.2019. \u201cRelational Knowledge Distillation.\u201d InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE.https:\/\/doi.org\/10.1109\/CVPR.2019.00409.","DOI":"10.1109\/CVPR.2019.00409"},{"key":"e_1_2_9_24_1","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke A.","year":"2019"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49430-8_3"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"Sandler M. A.Howard M.Zhu A.Zhmoginov andL. C.Chen.2018. \u201cMobileNetV2: Inverted Residuals and Linear Bottlenecks.\u201d InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE.https:\/\/doi.org\/10.1109\/CVPR.2018.00474.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.5851\/kosfa.2021.e25"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537\u2010019\u20100197\u20100"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537\u2010025\u201001151\u20104"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"e_1_2_9_31_1","unstructured":"Tan M. andQ.Le.2019. \u201cEfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.\u201d InProceedings of the International Conference on Machine Learning (ICML).https:\/\/arxiv.org\/abs\/1905.11946.PMLR (Proceedings of Machine Learning Research)."},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.24843\/JIM.2024.v12.i01.p01"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods11223713"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crfs.2024.100784"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods13070973"}],"container-title":["Journal of Food Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ift.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1750-3841.71554","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ift.onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/1750-3841.71554","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ift.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1750-3841.71554","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T05:47:42Z","timestamp":1793080062000},"score":1,"resource":{"primary":{"URL":"https:\/\/ift.onlinelibrary.wiley.com\/doi\/10.1111\/1750-3841.71554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1111\/1750-3841.71554"],"URL":"https:\/\/doi.org\/10.1111\/1750-3841.71554","archive":["Portico"],"relation":{},"ISSN":["0022-1147","1750-3841"],"issn-type":[{"value":"0022-1147","type":"print"},{"value":"1750-3841","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"2026-07-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e71554"}}