Composing recipes based on nutrients in food in a machine learning context

Zhenfeng Lei, Anwar ul Haq, Mohsen Dorraki, Defu Zhang, Derek Abbott

Research output: Contribution to journalArticlepeer-review

Abstract

In the past few decades, numerous studies have demonstrated the effectiveness of diet modification in preventing and controlling different chronic diseases, bringing increased attention to the creation of nutritional recipes. Currently, the development of recipes is primarily based on personal experience rather than nutritional specifications. To this end, this paper first uses kernel canonical correlation analysis to demonstrate the relationship between nutrients in food and recipes. Based on this relationship, a new recipe expression method is put forward, which objectively reflects differing importance of an ingredient in differing recipes. Then, recipes are composed using an auto-encoder in deep learning, and a fusion model of two auto-encoders is proposed for better concocting recipes. This paper uses two machine learning methods, namely, non-negative matrix factorization and two-step regularized least squares, to form recipes. To tackle overfitting and instability in non-negative matrix factorization during the training of recipe model, we introduce the Frobenius norm to redefine the objective function and add non-smooth sparse matrices. Similar food has similar taste, but their nutrients might differ. This paper also considers nutrients as a kernel matrix of the two-step regularized least squares, which can effectively avoid the occurrence of different food combinations with similar taste. Experimental results show that developing recipes based on nutrients in food is feasible and effective in the context of machine learning.

Original languageEnglish
Pages (from-to)382-396
Number of pages15
JournalNeurocomputing
Volume415
DOIs
Publication statusPublished or Issued - 20 Nov 2020
Externally publishedYes

Keywords

  • Data mining
  • Deep learning
  • Nutrition
  • Recipe creation

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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