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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.


ΔNs and its statistical significance.The variation in ΔNs for regional cuisines and corresponding random controls signifying the extent of bias in food pairing. Statistical significance of ΔNs is shown in terms of Z-score. ‘Regional cuisine’ refers to each of the eight cuisines analyzed; ‘Ingredient frequency’ refers to the frequency controlled random cuisine; ‘Ingredient category’ refers to ingredient category controlling random cuisine; and ‘Category + Frequency’ refers to random control preserving both ingredient frequency and category. Among all regional cuisines, Mughlai cuisine showed least negative food paring (ΔNs = −0.758) while Maharashtrian cuisine had most negative food pairing (ΔNs = −4.523).
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pone.0139539.g004: ΔNs and its statistical significance.The variation in ΔNs for regional cuisines and corresponding random controls signifying the extent of bias in food pairing. Statistical significance of ΔNs is shown in terms of Z-score. ‘Regional cuisine’ refers to each of the eight cuisines analyzed; ‘Ingredient frequency’ refers to the frequency controlled random cuisine; ‘Ingredient category’ refers to ingredient category controlling random cuisine; and ‘Category + Frequency’ refers to random control preserving both ingredient frequency and category. Among all regional cuisines, Mughlai cuisine showed least negative food paring (ΔNs = −0.758) while Maharashtrian cuisine had most negative food pairing (ΔNs = −4.523).

Mentions: We found that all regional cuisines are invariantly characterized by average food pairing lesser than expected by chance. This characteristic negative food pairing, however, varied in its extent across cuisines. Mughlai cuisine, for example, displayed the least inclination towards negative pairing ( and Z-score of -10.232). Whereas, Maharashtrian cuisine showed the most negative food pairing ( and Z-score of -52.047). Fig 4 depicts the generic food pairing pattern observed across regional cuisines of India. We found that the negative food pairing is independent of recipe size as shown in Fig 5. This indicates that the bias in food pairing is not an artifact of averaging over recipes of all sizes and is a quintessential feature of all regional cuisines of India. Note that, across cuisines, majority of recipes are in the size-range of around 3 to 12. Hence the significance of food pairing statistics is relevant below the recipe size cut-off of ∼ 12.


Analysis of Food Pairing in Regional Cuisines of India.

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

ΔNs and its statistical significance.The variation in ΔNs for regional cuisines and corresponding random controls signifying the extent of bias in food pairing. Statistical significance of ΔNs is shown in terms of Z-score. ‘Regional cuisine’ refers to each of the eight cuisines analyzed; ‘Ingredient frequency’ refers to the frequency controlled random cuisine; ‘Ingredient category’ refers to ingredient category controlling random cuisine; and ‘Category + Frequency’ refers to random control preserving both ingredient frequency and category. Among all regional cuisines, Mughlai cuisine showed least negative food paring (ΔNs = −0.758) while Maharashtrian cuisine had most negative food pairing (ΔNs = −4.523).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139539.g004: ΔNs and its statistical significance.The variation in ΔNs for regional cuisines and corresponding random controls signifying the extent of bias in food pairing. Statistical significance of ΔNs is shown in terms of Z-score. ‘Regional cuisine’ refers to each of the eight cuisines analyzed; ‘Ingredient frequency’ refers to the frequency controlled random cuisine; ‘Ingredient category’ refers to ingredient category controlling random cuisine; and ‘Category + Frequency’ refers to random control preserving both ingredient frequency and category. Among all regional cuisines, Mughlai cuisine showed least negative food paring (ΔNs = −0.758) while Maharashtrian cuisine had most negative food pairing (ΔNs = −4.523).
Mentions: We found that all regional cuisines are invariantly characterized by average food pairing lesser than expected by chance. This characteristic negative food pairing, however, varied in its extent across cuisines. Mughlai cuisine, for example, displayed the least inclination towards negative pairing ( and Z-score of -10.232). Whereas, Maharashtrian cuisine showed the most negative food pairing ( and Z-score of -52.047). Fig 4 depicts the generic food pairing pattern observed across regional cuisines of India. We found that the negative food pairing is independent of recipe size as shown in Fig 5. This indicates that the bias in food pairing is not an artifact of averaging over recipes of all sizes and is a quintessential feature of all regional cuisines of India. Note that, across cuisines, majority of recipes are in the size-range of around 3 to 12. Hence the significance of food pairing statistics is relevant below the recipe size cut-off of ∼ 12.

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.