{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T22:12:18Z","timestamp":1788473538805,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USDA\u2014Agricultural Research Service"},{"id":[{"id":"https:\/\/ror.org\/02d2m2044","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007699","name":"University of Georgia","doi-asserted-by":"crossref","award":["58-6040-4-033"],"award-info":[{"award-number":["58-6040-4-033"]}],"id":[{"id":"10.13039\/100007699","id-type":"DOI","asserted-by":"crossref"}]},{"award":["58-6040-4-033"],"award-info":[{"award-number":["58-6040-4-033"]}],"id":[{"id":"https:\/\/ror.org\/00te3t702","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Foreign plastic objects (FPOs) in poultry products present significant food safety risks and cause economic losses for the industry. Conventional detection methods, including X-rays and color imaging, often struggle to identify small or low-density plastics. Hyperspectral imaging (HSI) offers both spatial and spectral information but suffers from high computational cost when applied for FPO identification in industrial environments. This study introduces a parameter-efficient and computationally efficient spatial\u2013spectral transformer framework for pixel-level classification of FPOs on broiler meat using NIR-HSI (1000\u20131700 nm). The framework integrates three innovations: (1) center-focused linear attention (CFLA) to reduce computational complexity from O(n2) to O(n); (2) patch-local mixed-axis 2D rotary position embedding to preserve geometric relationships within hyperspectral patches; and (3) low-rank factorized projection (LRP) matrices to reduce parameters by approximately 50% within projection weight matrices. The framework was trained and evaluated on a dataset of 52 chicken fillets, comprising 295,340 labeled target hyperspectral pixels from 12 common polymer types and 1 fillet class. The model achieved 99.39% overall accuracy, 99.57% average accuracy, and a 99.31 Kappa coefficient across 248,540 test pixels. Per-class precision, recall, and F1-score exceeded 98.05%, 98.59%, and 98.76%, respectively, across all classes. Efficiency analyses showed an 83% reduction in multiply\u2013accumulate operations (MACs), a 22% reduction in trainable parameters, and a model size reduction from 1.72 MB to 1.35 MB relative to the baseline configuration. These gains also translated into practical inference benefits, with the final model achieving a throughput of 212,971.5 hyperspectral patch cubes\/s and a 4.19\u00d7 speedup over the baseline. These results demonstrate that the proposed framework combines strong classification performance with high efficiency, supporting high-throughput inference for real-time monitoring and enabling contamination source traceability and preventive quality control in industrial poultry processing. The approach provides a benchmark for applying transformer-based models to food safety inspection tasks.<\/jats:p>","DOI":"10.3390\/s26082459","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T14:05:36Z","timestamp":1776348336000},"page":"2459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Parameter- and Compute-Efficient Spatial\u2013Spectral Transformer Framework for Pixel-Level Classification of Foreign Plastic Objects on Broiler Meat Using NIR\u2013Hyperspectral Imaging"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3668-3441","authenticated-orcid":false,"given":"Zirak","family":"Khan","sequence":"first","affiliation":[{"name":"School of Computing, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-6559","authenticated-orcid":false,"given":"Seung-Chul","family":"Yoon","sequence":"additional","affiliation":[{"name":"Quality and Safety Research Unit, U.S. National Poultry Research Center, U.S. Department of Agriculture\u2014Agricultural Research Service, Athens, GA 30605, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suchendra M.","family":"Bhandarkar","sequence":"additional","affiliation":[{"name":"School of Computing, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19574","DOI":"10.1016\/j.heliyon.2023.e19574","article-title":"Detection and prevention of foreign material in food: A review","volume":"9","author":"Payne","year":"2023","journal-title":"Heliyon"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barbut, S., and Leishman, E.M. 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