{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:03:21Z","timestamp":1784783001063,"version":"3.55.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018693","name":"HORIZON EUROPE Framework Programme","doi-asserted-by":"publisher","award":["101061023"],"award-info":[{"award-number":["101061023"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:p>\n                    Translating nutritional recommendations into practical day-to-day meal choices remains a challenging task, particularly when personalization, nutritional adequacy, dietary diversity, allergies, seasonal availability, and food-group constraints must be simultaneously satisfied. This study presents and evaluates the PLAN'EAT Nutrition Advisor, an Artificial Intelligence (AI)-driven, expert rule-based nutrition recommendation system, designed to generate personalized and nutritionally balanced weekly meal plans aligned with established dietary guidelines and food-group recommendations derived from Sustainable Healthy Diet (SHD) principles. The proposed approach is built upon the PLAN'EAT Expert-Curated Meal Database, a nutritionist-designed repository introduced in this work, comprising 401 expert-curated meals spanning Irish, Spanish, and Hungarian cuisines. Meal-plan generation follows a four-stage pipeline: (1)\n                    <jats:italic>meal filtering<\/jats:italic>\n                    based on country-specific cuisine, seasonality, dietary preferences, and allergies, (2)\n                    <jats:italic>daily meal plan generation<\/jats:italic>\n                    through large-scale sampling and scoring against expert-defined nutritional targets, (3)\n                    <jats:italic>weekly meal plan assembly<\/jats:italic>\n                    and optimization under nutritional and food-group constraints, and (4)\n                    <jats:italic>diversity optimization<\/jats:italic>\n                    to promote dietary variety while preserving nutritional validity. The proposed approach was validated through a large-scale\n                    <jats:italic>in-silico<\/jats:italic>\n                    validation involving 1,000 synthetic user profiles and the generation of 16,000 weekly meal plans corresponding to 112,000 daily meal plans. Adherence to daily and weekly nutritional targets, food-group constraints, and overall meal-plan validity at scale were assessed. In addition, we performed a preliminary descriptive evaluation against state-of-the-art Large Language Model (LLM)-based approaches under two complementary settings: Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT). Experimental results demonstrate that the proposed system can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization. Furthermore, the descriptive evaluation against LLM approaches suggests that the proposed system achieves more consistent energy and macronutrient adherence under the evaluated conditions.\n                  <\/jats:p>","DOI":"10.3389\/fnut.2026.1894893","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:32:21Z","timestamp":1784781141000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines"],"prefix":"10.3389","volume":"13","author":[{"given":"Dimitris","family":"Tsolakidis","sequence":"first","affiliation":[{"name":"The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (ITI\u2013CERTH)","place":["Thessaloniki, Central Macedonia, 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Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di\u00e1na","family":"Szakal","sequence":"additional","affiliation":[{"name":"Environmental Social Science Research Group (ESSRG)","place":["Budapest, Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alba Lara","family":"Valtue\u00f1a","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, Open University of Catalonia (Universitat Oberta de Catalunya, UOC)","place":["Barcelona, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris","family":"Bl\u00e1zquez","sequence":"additional","affiliation":[{"name":"FoodLab Research Group (2021SGR01357), Faculty of Health Sciences, Open University of Catalonia (Universitat Oberta de Catalunya, UOC)","place":["Barcelona, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick S.","family":"Elliott","sequence":"additional","affiliation":[{"name":"Institute of Food and Health, School of Agriculture and Food Science, University College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren D.","family":"Devine","sequence":"additional","affiliation":[{"name":"Institute of Food and Health, School of Agriculture and Food Science, University College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eileen R.","family":"Gibney","sequence":"additional","affiliation":[{"name":"Institute of Food and Health, School of Agriculture and Food Science, University College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aifric M.","family":"O'Sullivan","sequence":"additional","affiliation":[{"name":"Institute of Food and Health, School of Agriculture and Food Science, University College Dublin","place":["Dublin, 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