{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:12:35Z","timestamp":1784200355443,"version":"3.55.0"},"reference-count":14,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":196,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Food Science"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Personalized nutrition recommendations encounter difficulties in aligning users\u2032 dietary preferences with appropriate foods in extensive nutritional databases comprising thousands of products. This research introduces a multinutrient clustering framework that examines 8790 foods from the USDA National Nutrient Database for Standard Reference, Release 28, utilizing 23 nutritional attributes, including macronutrients, vitamins, and minerals. We thoroughly analyze\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means and agglomerative clustering algorithms across various configurations (\n                    <jats:italic>k<\/jats:italic>\n                    = 2 \u2212 8), finding that\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means with eight clusters yields optimal performance, achieving a silhouette score of 0.273 and semantically interpretable dietary categories. The suggested technique shows a 46.4% improvement over suggestions based on popularity and a 273.0% improvement over recommendations based on a single nutrient. This was shown by a thorough evaluation utilizing Precision@K, NDCG, and fivefold cross\u2010validation (mean silhouette 0.265 \u00b1 0.006). Users can set their own dietary preferences using nutrient sliders, six preset configurations (high protein, low carb, high fiber, low sodium, high calcium, and low calorie), and three configurable weighting strategies (equal, prioritized 3\u00d7, and focus\u2010only). The system then gives them real\u2010time recommendations (in less than 2\u2009s) with clear similarity scores. A systematic examination with 50 automated test questions shows that the system works well in a wide range of dietary situations. The huge effect sizes (46.4% and 273.0% improvements) suggest that the results are statistically significant. This research connects computational nutrition studies with real\u2010world dietary advice. It offers an open\u2010source, understandable system for evidence\u2010based meal planning that fills important holes in current prediction and classification methods by allowing personalized, multinutrient food suggestions with clear reasoning behind the choices.\n                  <\/jats:p>","DOI":"10.1155\/ijfo\/9969543","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:18:46Z","timestamp":1784197126000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multinutrient Clustering Framework for Personalized Food Recommendations"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3985-6533","authenticated-orcid":false,"given":"Rajkumar","family":"Sarker","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2747-0506","authenticated-orcid":false,"given":"Kazi Farhan Hasan","family":"Tanjim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"e_1_2_15_1_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-981-95-1069-6_2","volume-title":"Proceedings of the Sixth International Conference on Trends in Computational and Cognitive Engineering","author":"Tanjim K. F. H.","year":"2026"},{"key":"e_1_2_15_2_2","first-page":"1","volume-title":"in 2023 26th International Conference on Computer and Information Technology (ICCIT)","author":"Tanjim K. F. H.","year":"2023"},{"key":"e_1_2_15_3_2","first-page":"446","volume-title":"European Conference on Computer Vision (ECCV)","author":"Bossard L.","year":"2014"},{"key":"e_1_2_15_4_2","first-page":"3020","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Salvador A.","year":"2017"},{"key":"e_1_2_15_5_2","volume-title":"Composition of Foods Raw, Processed, Prepared USDA National Nutrient Database for Standard Reference, Release 28","author":"Haytowitz D. B.","year":"2015"},{"key":"e_1_2_15_6_2","doi-asserted-by":"publisher","DOI":"10.1006\/jfca.1999.0867"},{"key":"e_1_2_15_7_2","first-page":"22","article-title":"Sampling Plan Design for the USDA\u2019s National Food and Nutrient Analysis Program","volume":"44","author":"Ahuja J. K. C.","year":"2015","journal-title":"Journal of Food Composition and Analysis"},{"key":"e_1_2_15_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/1750-3841.17421"},{"key":"e_1_2_15_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.crfs.2023.100500"},{"key":"e_1_2_15_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37457-1"},{"key":"e_1_2_15_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_2_15_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_2_15_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610927408827101"},{"key":"e_1_2_15_14_2","first-page":"1027","volume-title":"Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms","author":"Arthur D.","year":"2007"}],"container-title":["International Journal of Food Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/ijfo\/9969543","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/ijfo\/9969543","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/ijfo\/9969543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:18:49Z","timestamp":1784197129000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/ijfo\/9969543"}},"subtitle":[],"editor":[{"given":"Senem","family":"Kamiloglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":14,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/ijfo\/9969543"],"URL":"https:\/\/doi.org\/10.1155\/ijfo\/9969543","archive":["Portico"],"relation":{},"ISSN":["2356-7015","2314-5765"],"issn-type":[{"value":"2356-7015","type":"print"},{"value":"2314-5765","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2026-01-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9969543"}}