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MENU PLANNING AND NUTRIENT ANALYSIS WITH CONTEMPORARY AI TECHNOLOGIES: A CHATGPT-BASED APPROACH

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 16 - 35, 20.07.2025

Özlem Özer Altundağ

Abstract

This study aims to evaluate the nutrient content, adherence to the MIND diet, and nutrient profiling (NRF9.3) score of AI-generated menus for nursing home residents. ChatGPT was utilized as the AI program to generate one-month summer and winter menus, and the nutrient composition of these menus was subsequently analyzed. The menus were assessed in terms of their ability to meet the nutritional needs of older adults and their alignment with healthy eating guidelines. According to the results, although the AI-generated menus largely adhered to the MIND diet, certain limitations prevented a perfect score. Specifically, the consumption of berries was low, olive oil was not sufficiently included as the primary fat source, and there was no wine consumption. In terms of nutrient density, the NRF9 scores were high, whereas the NRF9.3 scores were significantly lower. This suggests that while the menus were nutrient-rich, they were not optimally healthy due to their high content of saturated fat, added sugars, and sodium. When analyzing the nutrient composition, the winter menu was found to have significantly higher levels of carbohydrates, fiber, vitamin A, vitamin C, vitamin B1, folic acid, sodium, potassium, magnesium, zinc, and iron compared to the summer menu (p<0.05). However, the winter menu had lower levels of protein and essential amino acids. In conclusion, while AI-generated menus were generally nutritious, they require adjustments to balance saturated fat, sugar, and sodium content. Future studies should explore AI models with greater variability and examine the effects of AI-generated menus on the cognitive performance of older adults.

Keywords

Elderly Menu Artificial Intelligence MIND diet Nutrient Pattern

References

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  • Baykasoğlu, A., Taşkıran, D., & Akkoyun, H. G. (2016). Toplu beslenme için menü planlama karar destek sistemi geliştirilmesi ve uygulanması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 31(1).
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GÜNÜMÜZ YAPAY ZEKA TEKNOLOJİLERİ İLE MENÜ PLANLAMASI VE BESİN ÖĞELERİ ANALİZİ: CHATGPT TABANLI BİR YAKLAŞIM

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 16 - 35, 20.07.2025

Özlem Özer Altundağ

Abstract

Bu çalışma, huzurevlerindeki bireyler için yapay zeka tarafından oluşturulan menüleri besin öğesi içeriği, MIND diyeti uyumu ve besin öğesi örüntü profiline (NRF9.3) göre değerlendirilmesini amaçlamaktadır. Çalışmada yapay zeka programı olarak ChatGPT kullanılmış olup, programa birer aylık yaz ve kış menüleri oluşturtulmuş ve ardından bu menülerin besin öğesi içerikleri analiz edilmiştir. Menülerin, yaşlı bireylerin besin gereksinimlerini karşılama ve sağlıklı beslenmeye uygunluğu değerlendirilmiştir. Sonuçlara göre, program her iki menüde de MIND diyetine büyük ölçüde uyum sağlasa da, özellikle üzümsü meyve tüketiminin düşük olması, zeytinyağının birincil yağ kaynağı olarak yeterince yer almaması ve şarap tüketiminin bulunmaması tam skora ulaşılamamasına neden olmuştur. Besin yoğunluğu açısından, NRF9 puanları yüksek, ancak NRF9.3 puanları oldukça düşük bulunmuştur. Bu durum, menülerin besin öğesi açısından zengin olmasına rağmen yüksek doymuş yağ, eklenmiş şeker ve sodyum içermesi nedeniyle sağlık açısından optimal olmadığını göstermektedir. Besin öğesi içerikleri incelendiğinde ise; kış menüsünün karbonhidrat, posa, A vitamini, C vitamini, B1 vitamini, folik asit, sodyum, potasyum, magnezyum, çinko ve demir içeriğinin yaz menüsüne göre anlamlı derecede yüksek olduğu görülmüştür (p<0.05). Ancak, kış menüsü protein ve elzem amino asitler açısından daha düşüktür. Sonuç olarak, yapay zeka ile oluşturulan menüler genel olarak besleyici olsa da menülerin doymuş yağ, şeker ve sodyum içeriği açısından dengelenmesi gerekmektedir. Gelecek çalışmalarda daha fazla varyasyon içeren yapay zeka modellerinin test edilmesi ve menülerin yaşlı bireylerin bilişsel performansına etkisinin incelenmesi önerilmektedir.

Keywords

Yaşlı Menü Yapay Zeka MIND Diyeti Besin Öğesi Örüntüsü

References

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  • Alhammadi, K., Santos-Roldán, L., & Cabeza-Ramírez, L. J. (2021). A Theoretical framework on the determinants of food purchasing behavior of the elderly: a bibliometric review with scientific mapping in web of science. Foods, 10(3), 688. https://doi.org/10.3390/foods10030688
  • Arslan, S. (2024). Decoding dietary myths: The role of ChatGPT in modern nutrition. Clinical Nutrition ESPEN, 60, 285-288. https://doi.org/10.1016/j.clnesp.2024.02.022
  • Arslan, S. (2025). ChatGPT is no nutrition encyclopedia, but does it need to be?. Clinical Nutrition ESPEN, 66, 213-214. https://doi.org/10.1016/j.clnesp.2025.01.055
  • Başer, M. Y., & Olcay, A. (2022). Akıllı turizmde yapay zekâ teknolojisi. Gaziantep University Journal of Social Sciences, 21(3), 1795-1817. https://doi.org/10.21547/jss.1084783
  • Baykasoğlu, A., Taşkıran, D., & Akkoyun, H. G. (2016). Toplu beslenme için menü planlama karar destek sistemi geliştirilmesi ve uygulanması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 31(1).
  • Benvenuti, L., & De Santis, A. (2020). Making a sustainable diet acceptable: an emerging programming model with applications to schools and nursing homes menus. Frontiers in Nutrition, 7, 562833. https://doi.org/10.3389/fnut.2020.562833
  • Beslenme Bilgi Sistemi (BeBiS). (2021). Versiyon 9, İstanbul.
  • Berendsen, A. A., Kramer, C. S., & De Groot, L. C. (2019). The newly developed elderly nutrient-rich food score is a useful tool to assess nutrient density in European older adults. Frontiers in nutrition, 6, 119. https://doi.org/10.3389/fnut.2019.00119
  • Berendsen, A. M., Kang, J. H., Feskens, E. J., de Groot, C. P. G. M., Grodstein, F., & van de Rest, O. (2018). Association of long-term adherence to the mind diet with cognitive function and cognitive decline in American women. The Journal of Nutrition, Health & Aging, 22(2), 222-229. https://doi.org/10.1007/s12603-017-0909-0
  • Buckinx, F., Allepaerts, S., Paquot, N., Reginster, J. Y., De Cock, C., Petermans, J., & Bruyère, O. (2017). Energy and nutrient content of food served and consumed by nursing home residents. The Journal of Nutrition, Health & Aging, 21, 727-732.
  • Burger, C., Kiesswetter, E., Gietl, A., Pfannes, U., Arens-Azevêdo, U., Sieber, C. C., & Volkert, D. (2017). Size matters! Differences in nutritional care between small, medium and large nursing homes in Germany. The Journal of Nutrition, Health & Aging, 21(4), 464-472. https://doi.org/10.1007/s12603-016-0767-1
  • Burger, C., Kiesswetter, E., Alber, R., Pfannes, U., Arens-Azevedo, U., & Volkert, D. (2019). Texture modified diet in German nursing homes: availability, best practices and association with nursing home characteristics. BMC geriatrics, 19, 1-11. https://doi.org/10.1186/s12877-019-1286-9
  • Carrier, N., West, G. E., & Ouellet, D. (2009). Dining experience, foodservices and staffing are associated with quality of life in elderly nursing home residents. JNHA-The Journal of Nutrition, Health and Aging, 13, 565-570. https://doi.org/10.1007/s12603-009-0108-8
  • Davutoğlu, N. A. C. İ., & Yıldız, E. (2020). Turizm 4.0'dan gastronomi 4.0'a giden yolda: Geleceğin restoranlari ve yönetimi. Akademik Sosyal Araştırmalar Dergisi, 8(109). https://doi.org/10.29228/asos.45504
  • Demirel, Y., Bilici, S., & Köksal, E. (2018). Özel ve devlet huzurevleri menülerinin kalite ve yeterlilik açısından değerlendirmesi. Beslenme ve Diyet Dergisi, 46(1), 24-29. https://doi.org/10.33076/2018.BDD.284
  • Dikmen, D., & Pekcan, G. (2013). Besin ögesi örüntü profili: toplu beslenme hizmeti veren kuruluşlarda uygulanan menülerin değerlendirilmesi. Beslenme ve Diyet Dergisi, 41(3), 234- 241.
  • Doğan, M. (2022). Menü planlama ve standart reçeteler. In Toplu Beslenme Sistemleri ve Catering Hizmetleri Yönetimi. Nobel Akademik Yayınları, Ankara.
  • Dülgaroğlu, O. (2024). Turizmde teknolojileri ve robotlaşma yapay zekâ: Oğuzhan Dülgaroğlu. Uluslararası Turizm, Ekonomi ve İşletme Bilimleri Dergisi (IJTEBS) E-ISSN: 2602-4411, 8(2), 82-95.
  • Drewnowski, A. (2009). Defining nutrient density: development and validation of the nutrient rich foods ındex. Journal of the American College of Nutrition, 28(4), 421S-426S. doi: https://doi.org/10.1080/07315724.2009.10718106
  • EFSA (2009). European food safety authority. review of labelling reference intake values-scientific opinion of the panel on dietetic products, nutrition and allergies on a request from the commission related to the review of labelling reference intake values for selected nutritional elements. EFSA J. 7:1008. https://doi.org/10.2903/j.efsa.20 09.1008
  • Ercan, F. (2020). Turizm pazarlamasında yapay zekâ teknolojilerinin kullanımı ve uygulama örnekleri. Ankara Hacı Bayram Veli Üniversitesi Turizm Fakültesi Dergisi, 23(2), 394-410. 1 https://doi.org/0.34189/tfd.23.02.009
  • Ersoy, S., & Derin, D. Ö. (2023). Yapay zekanın beslenme biliminde kullanımı. Sağlık Bilimlerinde Yapay Zeka Dergisi, 3(2), 9-13.
  • Erul, E., & Işın, A. (2023). ChatGPT ile sohbetler: Turizmde ChatGPT’nin önemi (Chats with ChatGPT. Journal of Tourism and Gastronomy Studies, 11(1), 780-793. https://doi.org/10.21325/jotags.2023.1217
  • Fonseca, S. C. F., Barroso, S. C., & Santos, M. C. T. (2024). Enhancing elderly nutrition: a qualitative evaluation of menus in a social solidarity ınstitution in the North of Portugal. Nutrients, 16(5), 753. https://doi.org/10.3390/nu16050753
  • Grøndahl, V. A., & Aagaard, H. (2016). Older people's involvement in activities related to meals in nursing homes. International Journal of Older People Nursing, 11(3), 204-213. https://doi.org/10.1111/opn.12111
  • Grieger, J. A., & Nowson, C. A. (2007). Nutrient intake and plate waste from an Australian residential care facility. European journal of clinical nutrition, 61(5), 655-663. https://doi.org/10.1038/sj.ejcn.1602565
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Details

Primary Language Turkish
Subjects Consumption and Everyday Life, Accounting, Auditing and Accountability (Other)
Journal Section Articles
Authors

Özlem Özer Altundağ 0000-0001-7117-6335

Publication Date July 20, 2025
Submission Date February 18, 2025
Acceptance Date June 2, 2025
Published in Issue Year 2025 Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ

Cite

APA Özer Altundağ, Ö. (2025). GÜNÜMÜZ YAPAY ZEKA TEKNOLOJİLERİ İLE MENÜ PLANLAMASI VE BESİN ÖĞELERİ ANALİZİ: CHATGPT TABANLI BİR YAKLAŞIM. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ), 16-35. https://doi.org/10.35379/cusosbil.1642390
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