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Journal of Food Technology Research

June 2020, Volume 7, 1, pp 78-87

Understanding Mcdonalds Nutrition Facts using Discriminant Analysis and Neural Network

Yeong Nain Chi

Orson Chi

Yeong Nain Chi¹

Orson Chi²

Department of Agriculture, Food and Resource Sciences University of Maryland Eastern Shore, USA. ¹

CHI Analytical Consulting Services Baton Rouge, LA, USA. ²

on Google Scholar

on PubMed

Pages: 78-87

DOI: 10.18488/journal.58.2020.71.78.87

Article History:

Received: 23 January, 2020
Revised: 28 February, 2020
Accepted: 02 April, 2020
Published: 11 May, 2020

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Abstract:

Using data extracted from MacDonald’s nutrition facts for targeted popular menu items, this study tried to classify groups exhibiting common patterns of nutrition facts from the targeted popular menu items. The one-way ANOVA results showed that significant differences in saturated fat, trans fat, cholesterol and protein were found with the three types of the targeted popular menu items. In this study, group means were significantly different using the Wilk’s Lambda scores for both discriminant functions, respectively. The canonical correlation results also supported that there were strong relationships between the discriminant score and the group membership. The multilayer perceptron neural network model was utilized as a predictive model in deciding the classification of MacDonald’s nutrition facts for targeted popular menu items. The predictive model developed had excellent classification accuracy. From an architectural perspective, it showed a 10-2-2-3 neural network construction. Results of this study may provide insight into the understanding of the importance of MacDonald’s nutrition facts for targeted popular menu for consumer references.

Contribution/ Originality
This study is one of very few studies which have classified groups of nutrition facts from McDonalds popular menu items. This study also addresses that discriminant analysis and multilayer perceptron neural network model can be utilized to detect the classification of MacDonald’s nutrition facts for targeted popular menu items.

Keywords:

McDonalds , Nutrition facts, One-Way ANOVA, Discriminant analysis, Neural network, Multilayer perceptron.

Reference:

Fryar, C. D., Hughes, J. P., Herrick, K. A., & Ahluwalia, N. (2018). Fast food consumption among adults in the United States, 2013–2016. NCHS Data Brief, No. 322. 8. Retrieved from: https://www.cdc.gov/nchs/data/databriefs/db322-h.pdf .

Haykin, S. S. (2009). Neural networks and learning machines (3rd ed.). Upper Saddle River, New Jersey: Pearson Education, Inc.

IBM. (2019). IBM SPSS neural networks 26. Armonk, NY: IBM Corporation.

Lesser, L. I., Kayekjian, K. C., Velasquez, P., Tseng, C.-H., Brook, R. H., & Cohen, D. A. (2013). Adolescent purchasing behavior at McDonald's and Subway. Journal of Adolescent Health, 53(4), 441-445.Available at: https://doi.org/10.1016/j.jadohealth.2013.02.014.

McCrory, M. A., Harbaugh, A. G., Appeadu, S., & Roberts, S. B. (2019). Fast-food offerings in the United States in 1986, 1991, and 2016 show large increases in food variety, portion size, dietary energy, and selected micronutrients. Journal of the Academy of Nutrition and Dietetics, 119(6), 923-933.Available at: https://doi.org/10.1016/j.jand.2018.12.004.

Petimar, J., Ramirez, M., Rifas-Shiman, S. L., Linakis, S., Mullen, J., Roberto, C. A., & Block, J. P. (2019). Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: a natural experiment. International Journal of Behavioral Nutrition and Physical Activity, 16(1), 1-11.Available at: https://doi.org/10.1186/s12966-019-0865-7.

Samsudin, A., Jusoff, K., Zaini, Z., Musa, M., Khalid, K., Ngali, N., & Hamid, M. (2011). Customer’s perception towards McDonald’s icon-based nutritional labels. World Applied Sciences Journal, 12, 1-07.

Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 425740, 1-11.Available at: https://doi.org/10.1155/2013/425740.

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Upper Saddle River, New Jersey: Pearson Education, Inc.

Yunus, R., Arif, O., Afzal, H., Amjad, M., Abbas, H., Bokhari, H., & Nawaz, R. (2019). A framework to estimate the nutritional value of food in real time using deep learning techniques. IEEE Access, 7, 2643-2652.Available at: https://doi.org/10.1109/access.2018.2879117.