Hostname: page-component-7dd5485656-frp75 Total loading time: 0 Render date: 2025-10-29T23:29:43.756Z Has data issue: false hasContentIssue false
  • English
  • Français

A recipe-based, diet-planning modelling system

Published online by Cambridge University Press:  09 March 2007

Pingsun Leung ,
Kulavit Wanitprapha  and
Lynne A. Quinn
Pingsun Leung
Affiliation:
Department of Agricultural and Resource Economics University of Hawaii at Manoa, 3050 Maile Way, Gilmore 115, Honolulu, Hawaii 96822, USA
Kulavit Wanitprapha
Affiliation:
Department of Agricultural and Resource Economics University of Hawaii at Manoa, 3050 Maile Way, Gilmore 115, Honolulu, Hawaii 96822, USA
Lynne A. Quinn
Affiliation:
School of Travel Industry Management, University of Hawaii at Manoa, 3050 Maile Way, Gilmore 115, Honolulu, Hawaii 96822, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In a recent article in the British Journal of Nutrition, Sklan & Dariel (1993) presented a method for diet planning employing a mixed-integer programming algorithm for meeting nutritional requirements at minimum costs for institutions or individuals. They recognized that most food items are generally consumed in whole units and as such they are represented as integer variables. However, as in most previous studies, they derived the minimum cost diets by optimizing over purchased food items. The present paper presents a computer-assisted, diet-planning modelling system for individuals by optimizing over recipes instead of food items. This is accomplished by restricting the integer programming solutions to those bundles of food that represent reasonably popular meal recipes. The modelling system is composed of three main components: recipe data entry, database management, and the model. The recipe data entry component creates and stores recipes. It aiso provides nutritional analysis of the recipes. The database management component creates and maintains several databases necessary to build the modelling data file. The modelling component solves the user-specified model. Currently, the model component can solve for the optimal diet by minimizing cost or minimizing cooking and preparation time. The optimal diet is prepared to satisfy the recommended nutritional guidelines for a predefined group of individuals for 1 week. The system currently has 895 popular recipes found in Hawaii. Diet plans generated using this modelling system with differing objectives are discussed and compared.

Keywords

Diet-planning modelRecipesMinimum costMinimum time

Information

Type
Recipe-based diet-planning
Information
British Journal of Nutrition , Volume 74 , Issue 2 , August 1995 , pp. 151 - 162
Copyright
Copyright © The Nutrition Society 1995

References

REFERENCES

Balintfy, J. L. (1964). Menu planning by computer. The Communications of ACM 7, 255–259.Google Scholar
Balintfy, J. L., Duffy, W. J. & Sinha, P. (1974). Modeling food preferences over time. Operations Research 22, 711–727.CrossRefGoogle Scholar
Bassi, L. J. (1976). The diet problem revisited. American Economist 20, 35–39.CrossRefGoogle Scholar
Britten, P. J. (1989). Nutritional Analysis of Hawaii School Lunch Menus for 1989–90. Research Extension Series 111, Hawaii Institute of Tropical Agriculture and Human Resources. Honolulu, Hawaii: Hawaii Institute of Tropical Agriculture and Human Resources, University of Hawaii at Manoa.Google Scholar
Foytik, J. (1981 a). Devising and using computerized diet: an exploratory study. Journal of Consumer Affairs 15, 158–169.CrossRefGoogle Scholar
Foytik, J. (1981 b). Very low-cost nutritious diet plans designed by linear programming. Journal of Nutrition Education 13, 63–66.Google Scholar
Lai, M., Kaneshiro, N., Thuele, M., Britten, P. J., Shimabukuro, S. & Derrickson, J. (1994). Hawaii Nutrition Education Needs Assessment 1990–1992: Final Report. Honolulu, Hawaii: Curriculum Research and Development Group, College of Education, University of Hawaii at Manoa.Google Scholar
Lancaster, L. M. (1992 a). The evolution of the diet model in managing food systems. Interfaces 22, 59–68.Google Scholar
Lancaster, L. M. (1992 b). The history of the application of mathematical programming to menu planning. European Journal of Operational Research 57, 339–347.Google Scholar
Leung, P. S., Miklius, W., Wanitprapha, K., Garrod, P. & Johnson, N. (1992). Minimum-cost palatable diet: a pilot study. Agricultural Systems 38, 275–299.CrossRefGoogle Scholar
Microsoft (1992). Windows Version 3·1. Redmond, Washington: Microsoft Corporation.Google Scholar
Microsoft (1994). FoxPro for Windows Version 2·6. Redmond, Washington: Microsoft Corporation.Google Scholar
National Research Council (1989). Recommended Dietary Allowances, 10th ed. Washington, DC: National Academy of Sciences.Google Scholar
Nutritionist IV (1993). Version 2·0, Diet Analysis. N-Squared Computing. San Bruno, California: DataBank Division, The Hearst Corporation.Google Scholar
Prato, A. A. (1973). A proposal for reducing nutrient deficiencies with economical diets. Canadian Journal of Agricultural Economics 21, 15–22.Google Scholar
Quinn, L. A., Leung, P. S. & Wanitprapha, K. (1995). Nutritional comparison of diets generated with differin Objectives. Journal of Nutrition in Recipe and Menu Development 1, 5–26.CrossRefGoogle Scholar
Statistical, Analysis Systems (1993). SAS and SAS/OR under Microsoft Windows, Release 6·08. Cary, NC: SAS Institute Inc.Google Scholar
Silberberg, E. (1985). Nutrition and the demand for tastes. Journal of Political Economy 93, 881–900.CrossRefGoogle Scholar
Sklan, D. & Dariel, I. (1993). Diet planning for humans using mixed-integer linear programming. British Journal of Nutrition 70, 27–35.CrossRefGoogle ScholarPubMed
Smith, V. E. (1959). Linear programming models for determination of palatable human diets. Journal of Farm Economics 41, 272–283.CrossRefGoogle Scholar
Stigler, G. L. (1945). The cost of subsistence. Journal of Farm Economics 27, 303–314.CrossRefGoogle Scholar