Data driven delights: AI and data science revolutionising the spice industry

Have you ever wondered how chefs and food scientists come up with new and exciting flavour combinations in the spice industry? The answer is artificial intelligence and data science approaches. Imagine a world where your favourite spices are paired together in a symphony of flavours, each note complementing the other to create a symphony of taste. This is the world of computational gastronomy and food pairing, where data and science come together to create culinary magic.

What is Computational Gastronomy and Food Pairing?

Computational gastronomy and food pairing are revolutionising the way we create and think about flavours. These cutting-edge techniques involve the use of data science, machine learning, and molecular gastronomy to analyse the chemical compounds in food and identify the perfect pairings that will tantalise your taste buds. Food pairing is the practice of pairing different foods together in order to enhance the overall taste and flavour of a meal. It is based on the idea that certain foods have chemical compounds that complement or enhance each other when consumed together. Food pairing is another area where computational gastronomy is being used in the spice industry. 

How they are being used in the spice industry?

One of the key ways that computational gastronomy is being used in the spice industry is through the analysis of large datasets of sensory data and chemical analysis data. Machine learning algorithms can be trained on datasets of sensory data and chemical analysis data to identify which foods and flavours pair well together. This information can be used to create new food pairings, and also to help chefs and food scientists who develop new dishes and recipes.

One example of computational food pairing is using natural language processing (NLP) techniques to extract ingredient lists from recipe data, and use that data to determine whether there are any common ingredient pairs within the dataset that occur more frequently than expected by chance alone (i.e., statistical significance). Another example is using molecular gastronomy techniques to understand the chemical compounds in food and identify which ingredients have similar compounds, and thus pair well together.

What are the benefits?

• Creating new and exciting flavour combinations: With the help of computational gastronomy, chefs and food scientists can now understand the science behind different spice compounds and identify which spices pair well together. This means that they can create new flavour combinations that were never thought possible before.

• Tailoring spices to specific taste preferences: By analysing large datasets of customer purchase data, researchers can identify patterns and correlations between different spices and use this information to develop new products and flavours that are tailored to specific consumer preferences. This can help to improve the overall taste and quality of spice-based products.

• Optimising food pairing process: With the use of computational gastronomy, the food pairing process becomes more efficient by reducing trial and error and understanding the science behind the flavour combinations.

Who are the pioneers?

There are several leading labs and scientists who are researching on food pairing in spices. Some of the notable ones include:

• The Department of Food Science and Technology at the University of California, Davis, led by Professor David Mills. The department is known for its research on food pairing and the use of computational methods to understand the science behind flavour combinations.

• The Center for Taste and Feeding Behaviour at the French National Institute for Agricultural Research (INRA). The centre is known for its research on taste perception and the use of molecular gastronomy techniques to understand the underlying mechanisms of flavour pairing.

• The Laboratory of Sensory Analysis and Consumer Science at the Catholic University of Leuven. The laboratory is known for its research on food pairing, and the use of computational methods to understand the science behind flavour combinations and how the human perception of flavours interact with food pairing.

• The Division of Food Sciences at the University of Nottingham, led by Dr. Richard Hartel. The division is known for its research on understanding and controlling quality and safety, starting with raw materials through processing to consumer preference and subsequent effects on the body.

• Complex Systems Laboratory at Indraprastha Institute of Information Technology, New Delhi under the leadership of Dr. Ganesh Bagler. His lab applies computational methods to understand the science behind flavour combinations in food. He has developed a number of computational resources like “CulinaryDB“, “FlavorDB“, “SpiceRx“, “DietRx” and “CulinaryDB” which contain information on the chemical compounds present in different foods and their flavour attributes.

There are a number of private spice companies that are using computational approaches to research and develop new spice blends and food pairing techniques. Some examples include McCormick & Company, Givaudan, Firmenich, International Flavors and Fragrances etc. There is a flavour intelligence company called Foodpairing.Com offering several online AI tools to professional chefs and CPG companies.

These are just a few examples of the many scientists and labs that are actively researching on food pairing in spices. It’s important to note that there are many other researchers and institutions that are also working in this field.

Conclusion

In conclusion, computational gastronomy and food pairing approaches are opening up a whole new world of possibilities in the spice industry. They are helping to create new and exciting flavour combinations and improve the overall taste and quality of spice-based products. So, the next time you enjoy your favourite dish, remember the magic of computational gastronomy and food pairing that made it all possible.

Additional Reading

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

• Malavolta, M., Pallante, L., Mavkov, B. et al. 2022. A survey on computational taste predictors. Eur. Food Res. Technol. 248, 2215–2235. https://doi.org/10.1007/s00217-022-04044-5

• Eetemadi, A., Rai, N., Pereira, B.M.P. et al. 2020. computational diet: A review of computational methods across diet, microbiome, and health. Front. Microbiol.11 – 2020. https://doi.org/10.3389/fmicb.2020.00393