{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T12:16:34Z","timestamp":1772021794413,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP23488904"],"award-info":[{"award-number":["AP23488904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The growing demand for customized pet diets highlights the shortcomings of commercial dog foods designed for all breeds, especially when it comes to addressing breed-specific diseases, metabolic disorders, and health risks. This research presents the development and evaluation of a hybrid system for formulating wet canine food recipes. The system combines data on ingredients, veterinary feeds, and breed-related diseases; the architecture includes a recommendation module for ingredient selection and a linear programming block for recipe optimization, considering veterinary nutrient restrictions. The evaluation of the system included automatic classification of foods by specialization, visual analysis of recipe clustering, and comparison of formulas obtained by different models. The average precision of label recovery was 85.4% for TF-IDF and 88.2% for the E5 model. A comparison of ingredient extraction methods showed that machine learning produces more stable recipes, while the statistical approach provides greater variability. The developed system demonstrates potential for automating recipe creation, filling in missing data, and developing veterinary decision support platforms aimed at personalized diet selection based on the physiological needs of animals.<\/jats:p>","DOI":"10.3390\/informatics13030034","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T10:59:16Z","timestamp":1772017156000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Canine Diet Generation Using Machine Learning and Constraint Optimization"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5641-3797","authenticated-orcid":false,"given":"Aliya","family":"Kalykulova","sequence":"first","affiliation":[{"name":"Research and Innovation Center \u201cAgroTech\u201d, Astana IT University, 010000 Astana, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8059-7240","authenticated-orcid":false,"given":"Kuanysh","family":"Bakirov","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cAgroTech\u201d, Astana IT University, 010000 Astana, Kazakhstan"}]},{"given":"Aruzhan","family":"Shoman","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cAgroTech\u201d, Astana IT University, 010000 Astana, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4128-6482","authenticated-orcid":false,"given":"Kadyrzhan","family":"Makangali","sequence":"additional","affiliation":[{"name":"Department of Technology of Food and Processing Industries, S. Seifullin Kazakh Agrotechnical Research University, 010000 Astana, Kazakhstan"}]},{"given":"Gulzhan","family":"Tokysheva","sequence":"additional","affiliation":[{"name":"Department of Technology of Food and Processing Industries, S. Seifullin Kazakh Agrotechnical Research University, 010000 Astana, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"ref_1","unstructured":"FEDIAF (2025). Facts & Figures Based on Aggregated Data from 2023, European Pet Food Industry Federation. Available online: https:\/\/europeanpetfood.org\/about\/statistics\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"445","DOI":"10.3945\/an.115.011718","article-title":"Foods, nutrients, and dietary patterns: Interconnections and implications for dietary guidelines","volume":"7","author":"Tapsell","year":"2016","journal-title":"Adv. Nutr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.2460\/javma.24.05.0358","article-title":"Dog and owner demographics impact dietary choices in Dog Aging Project cohort","volume":"262","author":"Tolbert","year":"2024","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hoummady, S., Fantinati, M., Maso, D., Bynens, A., Banuls, D., Santos, N.R., Roche, M., and Priymenko, N. (2022). Comparison of canine owner profile according to food choice: An online preliminary survey in France. BMC Vet. Res., 18.","DOI":"10.1186\/s12917-022-03258-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ionic\u0103, C., Daina, S., Pop, R., and Macri, A. (2025). Home-prepared dog food: Benefits and downsides. Front. Anim. Sci., 6.","DOI":"10.3389\/fanim.2025.1506003"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Morelli, G., Bastianello, S., Catellani, P., and Ricci, R. (2019). Raw meat-based diets for dogs: Survey of owners\u2019 motivations, attitudes and practices. BMC Vet. Res., 15.","DOI":"10.1186\/s12917-019-1824-x"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pomar, C., Andretta, I., and Remus, A. (2021). Feeding strategies to reduce nutrient losses and improve the sustainability of growing pigs. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.742220"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Akintan, O., Gebremedhin, K.G., and Uyeh, D.D. (2024). Animal feed formulation\u2014Connecting technologies to build a resilient and sustainable system. Animals, 14.","DOI":"10.3390\/ani14101497"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.48184\/2304-568X-2025-1-55-63","article-title":"Machine learning in pet food: A comprehensive review of applications, challenges, and future directions","volume":"147","author":"Kumar","year":"2025","journal-title":"J. Almaty Technol. Univ."},{"key":"ref_10","unstructured":"National Genomics Data Center (2025, September 24). Dog Breed Information. NGDC. Available online: https:\/\/ngdc.cncb.ac.cn\/idog\/."},{"key":"ref_11","unstructured":"(2025, September 24). Canine Inherited Disorders Database (CIDD). Disorder Types. CIDD. Available online: https:\/\/cidd.discoveryspace.ca\/disorder-types.html."},{"key":"ref_12","unstructured":"The Kennel Club (2025, September 24). Breeds. Available online: https:\/\/www.thekennelclub.org.uk\/search\/breeds-a-to-z\/."},{"key":"ref_13","unstructured":"(2025, September 24). Hill\u2019s Pet Nutrition. Dog Food. Available online: https:\/\/www.hillspet.co.uk\/products\/dog-food."},{"key":"ref_14","first-page":"6","article-title":"Development of a cost-optimal nutritionally balanced homemade canine diet through linear programming","volume":"1","author":"Aguilar","year":"2023","journal-title":"LACCEI"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"236","DOI":"10.18100\/ijamec.828440","article-title":"Cost optimization of homemade diet for dogs","volume":"8","author":"Joban","year":"2020","journal-title":"Int. J. Appl. Math. Electron. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pavlova, K., Trichkova-Kashamova, E., and Dimitrov, S. (2024). Applying a mathematical model for calculating the ideal nutrition for sheep. Mathematics, 12.","DOI":"10.3390\/math12081270"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.31080\/ASVS.2023.05.0688","article-title":"Broiler chicken feeds cost optimization using linear programming technique under Egyptian conditions","volume":"5","author":"Metwally","year":"2023","journal-title":"Acta Sci. Vet. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alqaisi, O., and Schlecht, E. (2020). Feeding models to optimize dairy feed rations in view of feed availability, feed prices and milk production scenarios. Sustainability, 13.","DOI":"10.3390\/su13010215"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.3168\/jds.2021-20817","article-title":"The application of nonlinear programming on ration formulation for dairy cattle","volume":"105","author":"Li","year":"2022","journal-title":"J. Dairy Sci. Dairy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109173","DOI":"10.1016\/j.compag.2024.109173","article-title":"Feed formulation using multi-objective Bayesian optimization","volume":"224","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Uribe-Guerra, G.D., M\u00fanera-Ram\u00edrez, D.A., and Arias-Londo\u00f1o, J.D. (2024). Swine diet design using multi-objective regionalized Bayesian optimization. arXiv.","DOI":"10.1016\/j.compag.2024.109173"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e12623","DOI":"10.1111\/exsy.12623","article-title":"A dog food recommendation system based on nutrient suitability","volume":"38","author":"Song","year":"2020","journal-title":"Expert Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"363","DOI":"10.31572\/inotera.Vol9.Iss2.2024.ID378","article-title":"Animal nutrition selection recommendation system with content-based filtering method","volume":"9","author":"Putra","year":"2024","journal-title":"J. Inotera"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e319","DOI":"10.1002\/pra2.319","article-title":"Healthy diet recommendation via food-nutrition-recipe graph mining","volume":"57","author":"Li","year":"2020","journal-title":"Proc. Assoc. Inf. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, W., Li, M., Dai, J., Ding, J., Chu, Z., and Chen, H. (2024). Nutrition-related knowledge graph neural network for food recommendation. Foods, 13.","DOI":"10.3390\/foods13132144"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guo, Y., Fan, Q., Zhang, Q., and Dong, Y. (2023). Health-aware food recommendation based on knowledge graph and multi-task learning. Foods, 12.","DOI":"10.3390\/foods12102079"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52508","DOI":"10.1109\/ACCESS.2022.3175317","article-title":"A novel time-aware food recommender system based on deep learning and graph clustering","volume":"10","author":"Rostami","year":"2022","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3466886","article-title":"Self-supervised calorie-aware heterogeneous graph networks for food recommendation","volume":"19","author":"Song","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Majumder, B.P., Li, S., Ni, J., and McAuley, J. (2019). Generating personalized recipes from historical user preferences. arXiv.","DOI":"10.18653\/v1\/D19-1613"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012127","DOI":"10.1088\/1755-1315\/613\/1\/012127","article-title":"Modeling the recipe composition of food products and recommendations for their use in individual nutrition","volume":"613","author":"Sadovoy","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.procs.2019.05.018","article-title":"Application of artificial neural network (ANN) for animal diet formulation modeling","volume":"152","author":"Saxena","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","unstructured":"(2025, October 11). Food Nutrition Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/shrutisaxena\/food-nutrition-dataset\/data."},{"key":"ref_33","unstructured":"U.S. Department of Agriculture (2025, September 24). FoodData Central, Available online: https:\/\/fdc.nal.usda.gov\/."},{"key":"ref_34","unstructured":"Kuanysh, B. (2025, October 13). Canine Diet Datasets. Figshare. Dataset. Available online: https:\/\/figshare.com\/articles\/dataset\/Canine_diet_datasets\/30344017\/1."},{"key":"ref_35","unstructured":"FEDIAF (2024). Nutritional Guidelines for Complete and Complementary Pet Food for Cats and Dogs, FEDIAF. Available online: https:\/\/europeanpetfood.org\/wp-content\/uploads\/2024\/09\/FEDIAF-Nutritional-Guidelines_2024.pdf."},{"key":"ref_36","unstructured":"Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., and Wei, F. (2024). Multilingual E5 text embeddings: A technical report. arXiv."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/3\/34\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T11:37:43Z","timestamp":1772019463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/3\/34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,25]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["informatics13030034"],"URL":"https:\/\/doi.org\/10.3390\/informatics13030034","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,25]]}}}