Final project for the Building AI course.
Cooking is an essential part of our lives and new, healthier, dishes can be created by intelligent use of ingredients. The objective of this project is to classify foods based on their consitituents and macronutrients.
Currently the number of recorded recipies pales in comparision to that of all potential recipies and the combination of ingredients to create delicious dishes can be perceived as illogical or outright surprising. For example, blue cheese with gingersnap cookies, ice cream with balsamic vinegar of Modena and pineapple on pizza. Categorisation based on constituents that give foods their flavour, color, taste, texture and aroma creates a scientic base to create derivative recipies and explore novel food combinations. The classified data can then be utilised by another AI systems to generate recommendations and new recipies.
Several uses of the categorised data can be envisioned.
- Creation of derivative recipes by substituting ingredients by their alternatives with similar characteristics.
- Modifying recipies based on dietary restrictions.
- Modifying recipies with seasonal produce.
- Creation of novel recipies.
The most promising source is FooDB - the world’s largest and most comprehensive resource on food constituents, chemistry and biology. The data is free for non-commercial use.
Creation of model for the categorisation can be addressed after understanding of the data and model features has been completed. Software package scikit-learn will be utilised in the categorisation.
Defining the model features will be challenging and the physiology of taste is extremely complex.
Explore the data and utilise PCA (Principal Componen Analysis) to gain understanding how to proceed in creating the features for the categorisation.
Thanks to University of Helsinki and Reactor for excellent educational material and guidance.
-
This, H. Molecular gastronomy: exploring the science of flavor (Columbia University Press, 2005).
-
FooDB - https://foodb.ca
-
Chun-Yuen Teng, Yu-Ru Lin, and Lada A. Adamic. 2012. Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference (WebSci '12). Association for Computing Machinery, New York, NY, USA, 298–307. DOI https://doi.org/10.1145/2380718.2380757
-
Ahn, YY., Ahnert, S., Bagrow, J. et al. Flavor network and the principles of food pairing. Sci Rep 1, 196 (2011). DOI https://doi.org/10.1038/srep00196
-
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. JMLR 12, pp. 2825-2830, 2011.