Analysis of Food Pairing in Regional Cuisines of India

Exploring food and beverage pairing from a cross-cultural projective mapping

Culture is a well-known driver of food choices, and therefore, it could also impact food pairing preferences. Food pairing has been studied from different approaches; however, little cross-cultural research has been done. This work explored food and beverage pairing using projective mapping (PM) to create maps of food-beverage combinations. Four countries (Mexico, Argentina, France, and Norway), thirty foods, and six beverages were selected. PM was carried out through an online study in each country. Participants were asked to map foods together with beverages following the instruction that foods and beverages closer together represented a good combination. The coordinates of each product were analyzed through Multiple Factorial Analyses (MFA) by countries. The first four factors of each MFA were used to perform RV coefficients to test similarities in food-beverage pairings between the countries. Finally, a k-means clustering was performed on the beverage coordinates of each MFA. PM provided maps representing food and beverage pairings for each country in which the proximity between food-beverages represented a good combination according to consumers. RV coefficients between countries were low, showing that food-beverage pairings were not similar across countries, evidencing the cultural effect in food-drink combinations. Results from the k-means clustering showed some similarities and differences between countries. In general, the food-beverage pairing was effectively explored with PM, from which several differences and similarities were found within cultures.

Computational gastronomy: capturing culinary creativity by making food computable

Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet. Computational Gastronomy is a novel approach for investigating food through a data-driven paradigm. It offers a systematic, rule-based understanding of culinary arts by scrutinizing recipes for taste, nutritional value, health implications, and environmental sustainability. Probing the art of cooking through the lens of computation will open up a new realm of possibilities for culinary creativity. Amidst the ongoing quest for imitating creativity through artificial intelligence, an interesting question would be, 'Can a machine think like a Chef?' Capturing the experience and creativity of a chef in an AI algorithm presents an exciting opportunity for generating a galaxy of hitherto unseen recipes with desirable culinary, flavor, nutrition, health, and carbon footprint profiles.

Consumer's perception of food pairing products with usual, novel and unusual flavour combinations: A segmentation approach

In saturated markets, companies are continually launching new products. Food innovations particularly play a decisive role in this case. One new concept is food pairing, which signifies that the more aromatic compounds two foods have in common, the better they taste together. Food pairing offers an opportunity to develop innovative foods. However, some consumers are risk-averse or exhibit food neophobia. Studies on food neophobia indicate that innovative foods could face rejection. The factor that represents a marketing barrier is not only the sensory rejection of the products when tasting them but also the refusal to even try such innovative products. Therefore, the idea of whether consumers are generally open to food pairing is important to examine. Nonetheless, research into this issue is lacking thus far. The subject of how consumers judge usual, novel, and unusual pairing principles was investigated in this study. The topic of whether a target group for food pairing products exists and characterized the target group was also analysed. To achieve the objective of the study, an online survey of German consumers (n = 1,064) was conducted; these consumers judged the five flavour combinations of each category (usual, novel, unusual). The results revealed a four-cluster solution, with one-third of the sample expressing an openness to food pairing. The whole sample judged the usual combinations as suitable; by contrast, the novel and unusual combinations were deemed to be mainly appropriate for the food pairing cluster. The proposed measurement methodology for testing the openness of food pairing, which distinguishes between usual, novel, and unusual pairings, has demonstrated its usefulness. Those consumers who are open to food pairing have a high level of food involvement and a low degree of food neophobia. Furthermore, they show the highest organic food purchase frequency.

The role and therapeutic implication of protein tyrosine phosphatases in Alzheimer's disease

Protein tyrosine phosphatases (PTPs) are important regulator of neuronal signal transduction and a growing number of PTPs have been implicated in Alzheimer's disease (AD). In the brains of patients with AD, there are a variety of abnormally phosphorylated proteins, which are closely related to the abnormal expression and activity of PTPs. β-Amyloid plaques (Aβ) and hyperphosphorylated tau protein are two pathological hallmarks of AD, and their accumulation ultimately leads to neurodegeneration. Studies have shown that protein phosphorylation signaling pathways mediates intracellular accumulation of Aβ and tau during AD development and are involved in synaptic plasticity and other stress responses. Here, we summarized the roles of PTPs related to the pathogenesis of AD and analyzed their therapeutic potential in AD.

Flavour network-based analysis of food pairing: Application to the recipes of the sub-cuisines from Northeast India

The flavour network-based analysis of food pairing was applied to the sub-cuisines from Northeast India to examine the food pairing behaviour in terms of the co-occurrence of ingredients with the shared flavouring compounds in food recipes. The method applied was based on an existing procedure in computational gastronomy, wherein the preference for positive pairing is attributed to dairy-based ingredients and negative pairing behaviour is attributed primarily to spice based ingredients. Recipe data was subjected to backbone extraction, projection of the recipe-ingredient-compound tri-partite network, and analysis for prevalence and authenticity of ingredients. Further, the average flavour sharing index of the cuisine was determined with the help of the flavour profiles of the ingredients. The extent of deviation for the original cuisine in comparison to a random cuisine was used to determine the degree of bias in the food pairing behaviour, with the sign as the indicator of the nature of pairing. The analysis identified the ingredients responsible to exhibit a positive or negative pairing pattern in the sub-cuisines. The ingredients from the spice category were the most prevalent and have resulted in the negative pairing behaviour in the cuisines. This role of spices in effecting a negative pairing behaviour is in line with the earlier reports for other Indian regional cuisines.

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Network theory was applied to explore the flavour pairing behaviour in recipes from Northeast regional sub-cuisines.

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Cooking oil and ingredients from the spice category were the prevalent ingredients.

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Prevalence of spices have led to negative food pairing patterns in most of the regional sub-cuisines.

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Limited usage of dairy ingredients is also a reason for the non - positive food pairing behaviors in the sub-cuisines.

What Is the Relationship between the Presence of Volatile Organic Compounds in Food and Drink Products and Multisensory Flavour Perception?

This narrative review examines the complex relationship that exists between the presence of specific configurations of volatile organic compounds (VOCs) in food and drink products and multisensory flavour perception. Advances in gas chromatography technology and mass spectrometry data analysis mean that it is easier than ever before to identify the unique chemical profile of a particular food or beverage item. Importantly, however, there is simply no one-to-one mapping between the presence of specific VOCs and the flavours that are perceived by the consumer. While the profile of VOCs in a particular product undoubtedly does tightly constrain the space of possible flavour experiences that a taster is likely to have, the gustatory and trigeminal components (i.e., sapid elements) in foods and beverages can also play a significant role in determining the actual flavour experience. Genetic differences add further variation to the range of multisensory flavour experiences that may be elicited by a given configuration of VOCs, while an individual’s prior tasting history has been shown to determine congruency relations (between olfaction and gustation) that, in turn, modulate the degree of oral referral, and ultimately flavour pleasantness, in the case of familiar foods and beverages.

There is little evidence that spicy food in hot countries is an adaptation to reducing infection risk

Spicier food in hot countries has been explained in terms of natural selection on human cultures, with spices with antimicrobial effects considered to be an adaptation to increased risk of foodborne infection. However, correlations between culture and environment are difficult to interpret, because many cultural traits are inherited together from shared ancestors, neighbouring cultures are exposed to similar conditions, and many cultural and environmental variables show strong covariation. Here, using a global dataset of 33,750 recipes from 70 cuisines containing 93 different spices, we demonstrate that variation in spice use is not explained by temperature and that spice use cannot be accounted for by diversity of cultures, plants, crops or naturally occurring spices. Patterns of spice use are not consistent with an infection-mitigation mechanism, but are part of a broader association between spice, health, and poverty. This study highlights the challenges inherent in interpreting patterns of human cultural variation in terms of evolutionary pressures.

The role of traditional restaurants in tourist destination loyalty

The aim of this study is to examine the effect that visitor satisfaction with traditional restaurants has on perceptions of the local gastronomy, the overall image of a city and loyalty to that destination. Fieldwork has been carried out in Córdoba, a city in southern Spain famous for being a UNESCO World Heritage city and for its traditional gastronomy. The methodology used is based on structural equation modeling (PLS-SEM). This paper makes a novel contribution in that no previous studies to date have explored satisfaction with traditional restaurants, with respect to the food, the service and the atmosphere. To achieve the proposed objective, a structured questionnaire has been used to find out the opinions of diners in renowned restaurants that base their cuisine on traditional dishes made with quality local ingredients. The results obtained confirm that a satisfactory experience with the food of a traditional restaurant has a positive effect on the image of the destination and the gastronomy of the place, as well as on visitors' intentions to recommend and repeat the visit to said destination. Based on the analysis carried out, effective strategies are suggested to help manage these types of restaurants. The study provides theoretical and practical implications from a gastronomic perspective, which can enable tourism managers to employ new strategies to retain tourists visiting a city, based on increasing their post-experience satisfaction with restaurants featuring local cuisine.

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings

Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.

The contribution of network science to the study of food recipes. A review paper

Recently, scholars have applied network science, which examines interconnected systems, to the study of food recipes. By examining the current literature, this review paper analyses the contribution of network science to recipes, and finds evidence of two main contributions. First, the pioneer studies showed the potential of network science for reducing the complexity of recipes and proposing theories to model ingredient pairings and recipe evolution. The second contribution built upon the previous theories and expanded them to cover different regions of the world as the field moved towards understanding the granularity of human culture with rich and detailed studies that examine different cuisines as more recipe datasets became available. Network science studies of recipes allowed researchers to discover insights in recipes to explain sociocultural aspects of cuisines, and include large numbers of recipes in the analysis, which would be nearly impossible using other techniques.

RecipeDB: a resource for exploring recipes

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment. As we stand on the verge of an epidemic of diet-linked disorders, it is eminently important to investigate the culinary correlates of recipes to probe their association with sensory responses as well as consequences for nutrition and health. RecipeDB (https://cosylab.iiitd.edu.in/recipedb) is a structured compilation of recipes, ingredients and nutrition profiles interlinked with flavor profiles and health associations. The repertoire comprises of meticulous integration of 118 171 recipes from cuisines across the globe (6 continents, 26 geocultural regions and 74 countries), cooked using 268 processes (heat, cook, boil, simmer, bake, etc.), by blending over 20 262 diverse ingredients, which are further linked to their flavor molecules (FlavorDB), nutritional profiles (US Department of Agriculture) and empirical records of disease associations obtained from MEDLINE (DietRx). This resource is aimed at facilitating scientific explorations of the culinary space (recipe, ingredient, cooking processes/techniques, dietary styles, etc.) linked to taste (flavor profile) and health (nutrition and disease associations) attributes seeking for divergent applications. Database URL:  https://cosylab.iiitd.edu.in/recipedb.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Multisensory Flavour Perception: Blending, Mixing, Fusion, and Pairing Within and Between the Senses

This review summarizes the various outcomes that may occur when two or more elements are paired in the context of flavour perception. In the first part, I review the literature concerning what happens when flavours, ingredients, and/or culinary techniques are deliberately combined in a dish, drink, or food product. Sometimes the result is fusion but, if one is not careful, the result can equally well be confusion instead. In fact, blending, mixing, fusion, and flavour pairing all provide relevant examples of how the elements in a carefully-crafted multi-element tasting experience may be combined. While the aim is sometimes to obscure the relative contributions of the various elements to the mix (as in the case of blending), at other times, consumers/tasters are explicitly encouraged to contemplate/perceive the nature of the relationship between the contributing elements instead (e.g., as in the case of flavour pairing). There has been a noticeable surge in both popular and commercial interest in fusion foods and flavour pairing in recent years, and various of the 'rules' that have been put forward to help explain the successful combination of the elements in such food and/or beverage experiences are discussed. In the second part of the review, I examine the pairing of flavour stimuli with music/soundscapes, in the emerging field of 'sonic seasoning'. I suggest that the various perceptual pairing principles/outcomes identified when flavours are paired deliberately can also be meaningfully extended to provide a coherent framework when it comes to categorizing the ways in which what we hear can influence our flavour experiences, both in terms of the sensory-discriminative and hedonic response.

Food and beverage flavour pairing: A critical review of the literature

The recent explosion of interest in the topic of flavour pairing has been driven, at least in part, by the now-discredited food-pairing hypothesis, along with the emergence of the new field of computational gastronomy. Many chefs, sommeliers, mixologists, and drinks brands, not to mention a few food brands, have become increasingly interested in moving the discussions that they have with their consumers beyond the traditional focus solely on food and wine pairings. Here, two key approaches to pairing that might help to explain/justify those food and beverage combinations that the consumer is likely to appreciate are outlined. Historically-speaking, many conventional pairings emerged naturally from cultural/geographical matches, presumably internalized as semantic knowledge amongst consumers. In this review, such conventional pairings are framed as but one example of a cognitive/intellectual food-beverage strategy. The alternative approach to pairing that has become increasingly popular in recent years involves experts/commentators making recommendations based on the perceptual relationship, or interaction, between the component stimuli, be it one of perceived similarity, contrast, harmony, emergence, or modulation (either suppression or enhancement). Physicochemical accounts of pairing, based on the presence of shared flavour molecules (e.g., aromatic volatiles) in the to-be-combined flavours or ingredients, have also gained in popularity. Here, though, the latter approach is framed as an ultimately unsuccessful attempt to predict matches based on perceived similarity. This review summarizes the available evidence concerning food-beverage pairing and proposes a new dichotomy between intellectual/cognitive and perceptual pairing principles in the case of food-beverage matching.

Using social media to explore regional cuisine preferences in China

Purpose

Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media.

Design/methodology/approach

Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately.

Findings

Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity.

Originality/value

Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

FlavorDB: a database of flavor molecules

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

Social success of perfumes

We study data on perfumes and their odour descriptors-notes-to understand how note compositions, called accords, influence successful fragrance formulas. We obtain accords which tend to be present in perfumes that receive significantly more customer ratings. Our findings show that the most popular notes and the most over-represented accords are different to those that have the strongest effect to the perfume ratings. We also used network centrality to understand which notes have the highest potential to enhance note compositions. We find that large degree notes, such as musk and vanilla as well as generically-named notes, e.g. floral notes, are amongst the notes that enhance accords the most. This work presents a framework which would be a timely tool for perfumers to explore a multidimensional space of scent compositions.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

A study on food-medicine continuum among the non-institutionally trained siddha practitioners of Tiruvallur district, Tamil Nadu, India

BACKGROUND

Medicinal properties of the food species are one of the poorly documented and important areas of ethnopharmacology. The present survey quantitatively documented the medicinal foods prescribed by the non-institutionally trained siddha practitioners of Tiruvallur district of Tamil Nadu.

METHODS

Field work was carried out between December 2014 and April 2017 using a questionnaire. The illnesses mentioned by the informants were grouped as illness categories on the basis of emic perceptions. Sufficiency of sampling of this survey was assessed by plotting the cumulative number of UR and Shannon-Wiener's index. The indices such as informant consensus factor (FIC), Index of Agreement on Remedies (IAR), and Cultural Food Significance Index (CFSI) were calculated.

RESULTS

This study documented 165 medicinal foods used by 82 non-institutionally trained siddha practitioners of Tiruvallur district, and 73.93% of these preparations were plant based. Among the animal taxa, 82.05% were represented by fish taxa. The illness category gastrointestinal ailments is the majorly cited illness category treated with plant-based formulations. The illness categories viz., gastrointestinal ailments, hemorrhoids, and neural ailments had high consensus under the group of plant-based medicinal foods. In animal-based medicinal foods, kapha ailments had gained 23.07% of UR. The illness categories such as bone fractures, male reproductive ailments, blood ailments, and anabolic had high FIC values.

CONCLUSIONS

Deeper studies on different dietary cultures of India may help to derive better interpretations on food-medicine continuum. This study identified some important claims such as the use of citron, pomegranate and Solanum americanum (gastrointestinal ailments), Abutilon indicum, onions and elephant foot yam (hemorrhoids), Boerhavia diffusa (urinary ailments), Moringa oleifera (anemia), Aloe vera (gynecological ailments), Eclipta prostrata (liver ailments), ivy gourd (diabetes), citron (hypertension), Centella asiatica (psychological ailments), spade nose shark (lactogogue), reticulate whipray (wheezing and bronchitis), Katelysia opima (impotence), Indian squid (anemia), and Indian oil sardine (anabolic). More studies on these claims will help identify novel functional foods to add to the field of medical nutrition therapy, with traditional brand identity. Robust studies on the documentation of the traditional knowledge on marine resources will yield a good database for various stakeholders and policy makers.

Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices

Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence. Systematic investigation of empirical evidence to enumerate the health consequences of culinary herbs and spices can provide valuable insights into their therapeutic utility. We implemented a text mining protocol to assess the health impact of spices by assimilating, both, their positive and negative effects. We conclude that spices show broad-spectrum benevolence across a range of disease categories in contrast to negative effects that are comparatively narrow-spectrum. We also implement a strategy for disease-specific culinary recommendations of spices based on their therapeutic tradeoff against adverse effects. Further by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. Our study provides a systems perspective on health effects of culinary spices and herbs with applications for dietary recommendations as well as identification of phytochemicals potentially involved in underlying molecular mechanisms.

Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns

The increasingly large volumes of publicly available sensory descriptions of wine raises the question whether this source of data can be mined to extract meaningful domain-specific information about the sensory properties of wine. We introduce a novel application of formal concept lattices, in combination with traditional statistical tests, to visualise the sensory attributes of a big data set of some 7,000 Chenin blanc and Sauvignon blanc wines. Complexity was identified as an important driver of style in hereto uncharacterised Chenin blanc, and the sensory cues for specific styles were identified. This is the first study to apply these methods for the purpose of identifying styles within varietal wines. More generally, our interactive data visualisation and mining driven approach opens up new investigations towards better understanding of the complex field of sensory science.