Introduction
Computational gastronomy is a data-driven scientific approach to food that uses computation and data to study cooking and food to achieve data-driven food innovations and solutions. This branch of food technology aims to transform the food landscape to ensure better public health and nutrition. Hence, it can counter the increasing malnutrition and overnutrition globally.
Computational Gastronomy: Understanding Applications and Its Innovations
Gastronomy is the study of culture and food focusing on gourmet cuisine. It includes cooking techniques, food science, nutritional facts, and taste applications. The study involves tasting, discovering, researching, experiencing, writing, and understanding food preparation and sensory characteristics of human nutrition. The increasing availability of structured data and the advent of computational methods have changed the artistic outlook toward gastronomy. The combination of both Gastronomy and data science has led to the establishment of Computational Gastronomy.
Applications: Studying Food as a Complex System
Computational Gastronomy has emerged as the data science of food, flavors, nutrition, and health. This modern gastronomy science helps in exploring phenomena in nutritional profiles, building recipe repositories, named entity recognition in recipes, novel recipe generation, and dish detection models to predict food processing classes.
‘Food System’ As A Complex System
Food is a ‘Complex System’. It comprises a large number of entities that are intricately intertwined with each other. There are various aspects of food, such as the recipe compositions, the experience of the flavor, the nutritional outcome, the health consequences arising out of food, and the carbon footprints of the food system. These aspects exhibit complex systems phenomena.
Personalized Nutrition
A data-driven and evidence-based food investigation formulates ‘personalized nutrition’. Computational Gastronomy can help to formulate such tailor-made micro–level nutrition for catering specific needs of the individuals. In this, personal features such as the nature of gut microbes, blood reports, body measures, and food habits are collected from a large cohort of people.
Personalized Nutrition Predictor
When an individual’s meal is substituted with one of their meals with a standardized diet, these features are correlated with post-meal glucose levels. Hence, a ‘personalized nutrition predictor’ can be made which accurately predicts the expected rise in glucose levels. Based on this, the system also generates personalized dietary recommendations. It can mitigate glucose levels successfully. These tailor-made diets can act as solutions for diet-linked diseases.
Generating Novel Recipes
Computational Gastronomy can help to create recipes. It leverages the power of large language models (LLMs) to generate novel recipes. These recipes have immense applications for culinary creativity, sustainability, diet management, allergy mitigation, and reducing food wastage. LLMs have an extensive resource of recipes from global cuisines, flavor compounds from natural ingredients, nutritional correlates of recipes, empirical evidence for food-disease associations, and estimated carbon footprints of recipes. These techniques have enabled the creation of novel recipe-generation algorithms. These novel recipes are not just palatable but are tasty and nutritious.
Tailor Made Cuisines
The use of LLMs by Computational Gastronomy has helped in the generation of recipes of desired culinary style, nutritional profile, and ingredient choices.
Sustainable Production
This technology can also be leveraged to minimize the price and ecological impact of the recipe.
Conclusion
Increasing population and Climate Change are likely to impact the food production system shortly. It will create a challenge of sustainably feeding an anticipated population of 10 billion people. Further, the food system also contributes to global greenhouse gas emissions central to climate change and global warming. Hence, there is a need to overhaul the food system from farm to fork. Hence, Computational Gastronomy can help to minimize waste, promote a ‘Circular Economy’ in the food system, sustainable production by using the best ingredients in local and seasonal recipes, and produce alternative protein sources.