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Computational gastronomy: A data science approach to food

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Abstract

Cooking forms the core of our cultural identity other than being the basis of nutrition and health. The increasing availability of culinary data and the advent of computational methods for their scrutiny are dramatically changing the artistic outlook towards gastronomy. Starting with a seemingly simple question, ‘Why do we eat what we eat?’, data-driven research conducted in our lab has led to interesting explorations of traditional recipes, their flavor composition, and health associations. Our investigations have revealed ‘culinary fingerprints’ of regional cuisines across the world. Application of data-driven strategies for investigating the gastronomic data has opened up exciting avenues, giving rise to an all-new field of ‘computational gastronomy’. This emerging interdisciplinary science asks questions of culinary origin to seek their answers via the compilation of culinary data and their analysis using methods of complex systems, statistics, computer science, and artificial intelligence. Along with complementary experimental studies, these endeavors have the potential to transform the food landscape by effectively leveraging data-driven food innovations for better health and nutrition.

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Acknowledgements

MG and GB thank IIIT-Delhi for the computational resources. MG thanks IIIT-Delhi for the fellowship.

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Authors and Affiliations

  1. Center for Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110 020, India

    Mansi Goel & Ganesh Bagler

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  1. Mansi Goel
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  2. Ganesh Bagler
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Corresponding author

Correspondence to Ganesh Bagler.

Additional information

Communicated by Susmita Roy.

Corresponding editor: Susmita Roy

This article is part of the Topical Collection: Emergent dynamics of biological networks.

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Goel, M., Bagler, G. Computational gastronomy: A data science approach to food. J Biosci 47, 12 (2022). https://doi.org/10.1007/s12038-021-00248-1

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