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Analysis of Food Pairing in Regional Cuisines of India.

Jain A, N K R, Bagler G - PLoS ONE (2015)

Bottom Line: Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine.Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing.Our study also provides insights as to how big data can change the way we look at food.

View Article: PubMed Central - PubMed

Affiliation: Center for System Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India.

ABSTRACT
Any national cuisine is a sum total of its variety of regional cuisines, which are the cultural and historical identifiers of their respective regions. India is home to a number of regional cuisines that showcase its culinary diversity. Here, we study recipes from eight different regional cuisines of India spanning various geographies and climates. We investigate the phenomenon of food pairing which examines compatibility of two ingredients in a recipe in terms of their shared flavor compounds. Food pairing was enumerated at the level of cuisine, recipes as well as ingredient pairs by quantifying flavor sharing between pairs of ingredients. Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine. We find that frequency of ingredient usage is central in rendering the characteristic food pairing in each of these cuisines. Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing. Interestingly while individual spices contribute to negative food pairing, dairy products on the other hand tend to deviate food pairing towards positive side. Our data analytical study highlighting statistical properties of the regional cuisines, brings out their culinary fingerprints that could be used to design algorithms for generating novel recipes and recipe recommender systems. It forms a basis for exploring possible causal connection between diet and health as well as prospection of therapeutic molecules from food ingredients. Our study also provides insights as to how big data can change the way we look at food.

No MeSH data available.


Frequency-Rank distributions.Ingredients ranked as per their frequency of use in the cuisine. Higher the occurrence, better the rank of the ingredient. All the cuisines have similar ingredient distribution profile indicating generic culinary growth mechanism. Inset shows the ingredient frequency-rank distribution for the whole Indian cuisine.
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pone.0139539.g002: Frequency-Rank distributions.Ingredients ranked as per their frequency of use in the cuisine. Higher the occurrence, better the rank of the ingredient. All the cuisines have similar ingredient distribution profile indicating generic culinary growth mechanism. Inset shows the ingredient frequency-rank distribution for the whole Indian cuisine.

Mentions: We started with investigation of preliminary statistics of regional cuisines. All the eight regional cuisines under consideration showed bounded recipe-size distribution (Fig 1). While most cuisines followed uni-modal distribution, Mughlai cuisine showed a strong bimodal distribution and had recipes with large sizes when compared with the rest. This could be an indication of the fact that Mughlai is derivative of a royal cuisine. To understand the ingredient usage pattern, we ranked ingredients according to decreasing usage frequency within each cuisine. As shown in Fig 2, all cuisines showed strikingly similar ingredient usage profile reflecting the pattern of Indian cuisine (Fig 2, inset). While indicating a generic culinary growth mechanism, the distributions also show that certain ingredients are excessively used in cuisines depicting their inherent ‘fitness’ or popularity within the cuisine.


Analysis of Food Pairing in Regional Cuisines of India.

Jain A, N K R, Bagler G - PLoS ONE (2015)

Frequency-Rank distributions.Ingredients ranked as per their frequency of use in the cuisine. Higher the occurrence, better the rank of the ingredient. All the cuisines have similar ingredient distribution profile indicating generic culinary growth mechanism. Inset shows the ingredient frequency-rank distribution for the whole Indian cuisine.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4592201&req=5

pone.0139539.g002: Frequency-Rank distributions.Ingredients ranked as per their frequency of use in the cuisine. Higher the occurrence, better the rank of the ingredient. All the cuisines have similar ingredient distribution profile indicating generic culinary growth mechanism. Inset shows the ingredient frequency-rank distribution for the whole Indian cuisine.
Mentions: We started with investigation of preliminary statistics of regional cuisines. All the eight regional cuisines under consideration showed bounded recipe-size distribution (Fig 1). While most cuisines followed uni-modal distribution, Mughlai cuisine showed a strong bimodal distribution and had recipes with large sizes when compared with the rest. This could be an indication of the fact that Mughlai is derivative of a royal cuisine. To understand the ingredient usage pattern, we ranked ingredients according to decreasing usage frequency within each cuisine. As shown in Fig 2, all cuisines showed strikingly similar ingredient usage profile reflecting the pattern of Indian cuisine (Fig 2, inset). While indicating a generic culinary growth mechanism, the distributions also show that certain ingredients are excessively used in cuisines depicting their inherent ‘fitness’ or popularity within the cuisine.

Bottom Line: Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine.Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing.Our study also provides insights as to how big data can change the way we look at food.

View Article: PubMed Central - PubMed

Affiliation: Center for System Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India.

ABSTRACT
Any national cuisine is a sum total of its variety of regional cuisines, which are the cultural and historical identifiers of their respective regions. India is home to a number of regional cuisines that showcase its culinary diversity. Here, we study recipes from eight different regional cuisines of India spanning various geographies and climates. We investigate the phenomenon of food pairing which examines compatibility of two ingredients in a recipe in terms of their shared flavor compounds. Food pairing was enumerated at the level of cuisine, recipes as well as ingredient pairs by quantifying flavor sharing between pairs of ingredients. Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine. We find that frequency of ingredient usage is central in rendering the characteristic food pairing in each of these cuisines. Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing. Interestingly while individual spices contribute to negative food pairing, dairy products on the other hand tend to deviate food pairing towards positive side. Our data analytical study highlighting statistical properties of the regional cuisines, brings out their culinary fingerprints that could be used to design algorithms for generating novel recipes and recipe recommender systems. It forms a basis for exploring possible causal connection between diet and health as well as prospection of therapeutic molecules from food ingredients. Our study also provides insights as to how big data can change the way we look at food.

No MeSH data available.