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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 Chi 1

Orson Chi 2

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

  2. 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

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Article History:

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


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.


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


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This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


Both authors contributed equally to the conception and design of the study.

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