Flavor network and the principles of food pairing

Typeface network and the principle of font pairing

In a field traditionally driven by intuition and subjective judgment, this study presents a data-driven approach to typography, the art of arranging text. Leveraging a comprehensive dataset of font-use cases across diverse mediums, we employed Non-negative Matrix Factorization to extract three fundamental morphological characteristics of fonts: Serif vs. Sans-Serif, Basic vs. Decorative letterforms, and Light vs. Bold. This analysis demonstrated that different mediums preferentially utilize fonts with distinct morphological features. We also predicted variations between single and paired fonts, contrasting these findings with random pairings from several null models, to identify unique font-pairing trends across various mediums. Furthermore, we utilized a network analysis approach to identify the most authentic font pairings, thereby yielding practical insights for typography applications. The primary contribution of our research lies in significantly enhancing the understanding of typographies. Our work lays the groundwork for the scientific exploration of the systematic categorization of fonts and their pairings. This study establishes foundational principles for the application of typography in visual communication.

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.

Recent advances in edible insect processing technologies

Alternative foods have emerged as one of the hot research topics aiming at alleviating food shortage. Insects are one of the alternative foods due to their rich nutrients. Processing is a critical step to develop insect foods, while there is a lack of comprehensive reviews to summarize the main studies. This review aims to demonstrate different processing methods in terms of their impact on insect nutrition and their potential risks. Heat treatments such as boiling and blanching show a negative effect on insect nutrition, but essential to assure food safety. Insects treated by high-pressure hydrostatic technology (HPP) and cold atmospheric pressure plasma (CAPP) can achieve a similar sterilization effect but retain the nutritional and sensory properties. Drying is a practical processing method for industrial insect production, where oven drying serves as a cost-effective method yielding products comparable in quality to freeze-dried ones. In terms of extraction technology, supercritical carbon dioxide and ultrasound-assisted technology can improve the extraction efficiency of proteins and lipids from insects, enhance the production of composite insect-fortified foods, and thus facilitate the development of the insect food industry. To address the widespread negative perceptions and low acceptance towards insect foods among consumers, the primary development direction of the insect food industry may involve creating composite fortified foods and extracting insect-based food components.

Predicting and improving complex beer flavor through machine learning

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Network topology mapping of chemical compounds space

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen . Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.

Introducing SoNHR-Reporting guidelines for Social Networks In Health Research

OBJECTIVE

The overall goal of this work is to produce a set of recommendations (SoNHR-Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research.

METHODS

This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced.

RESULTS

The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines.

CONCLUSIONS

These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas.

Epidemiological Data Mining for Assisting with Foodborne Outbreak Investigation

Diseases caused by the consumption of food are a significant but avoidable public health issue, and identifying the source of contamination is a key step in an outbreak investigation to prevent foodborne illnesses. Historical foodborne outbreaks provide rich data on critical attributes such as outbreak factors, food vehicles, and etiologies, and an improved understanding of the relationships between these attributes could provide insights for developing effective food safety interventions. The purpose of this study was to identify hidden patterns underlying the relations between the critical attributes involved in historical foodborne outbreaks through data mining approaches. A statistical analysis was used to identify the associations between outbreak factors and food sources, and the factors that were strongly significant were selected as predictive factors for food vehicles. A multinomial prediction model was built based on factors selected for predicting "simple" foods (beef, dairy, and vegetables) as sources of outbreaks. In addition, the relations between the food vehicles and common etiologies were investigated through text mining approaches (support vector machines, logistic regression, random forest, and naïve Bayes). A support vector machine model was identified as the optimal model to predict etiologies from the occurrence of food vehicles. Association rules also indicated the specific food vehicles that have strong relations to the etiologies. Meanwhile, a food ingredient network describing the relationships between foods and ingredients was constructed and used with Monte Carlo simulation to predict possible ingredients from foods that cause an outbreak. The simulated results were confirmed with foods and ingredients that are already known to cause historical foodborne outbreaks. The method could provide insights into the prediction of the possible ingredient sources of contamination when given the name of a food. The results could provide insights into the early identification of food sources of contamination and assist in future outbreak investigations. The data-driven approach will provide a new perspective and strategies for discovering hidden knowledge from massive data.

An evaluation tool for backbone extraction techniques in weighted complex networks

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Evaluating the prebiotic effect of oligosaccharides on gut microbiome wellness using in vitro fecal fermentation

We previously proposed the Gut Microbiome Wellness Index (GMWI), a predictor of disease presence based on a gut microbiome taxonomic profile. As an application of this index for food science research, we applied GMWI as a quantitative tool for measuring the prebiotic effect of oligosaccharides. Mainly, in an in vitro anaerobic batch fermentation system, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2'-fucosyllactose (2FL), were mixed separately with fecal samples obtained from healthy adult volunteers. To find out how 24 h prebiotic fermentation influenced the GMWI values in their respective microbial communities, changes in species-level relative abundances were analyzed in the five prebiotics groups, as well as in two control groups (no substrate addition at 0 h and for 24 h). The GMWI of fecal microbiomes treated with any of the five prebiotics (IN (0.48 ± 0.06) > FOS (0.47 ± 0.03) > XOS (0.33 ± 0.02) > GOS (0.26 ± 0.02) > 2FL (0.16 ± 0.06)) were positive, which indicates an increase of relative abundances of microbial species previously found to be associated with a healthy, disease-free state. In contrast, the GMWI of samples without substrate addition for 24 h (-0.60 ± 0.05) reflected a non-healthy, disease-harboring microbiome state. Compared to the original prebiotic index (PI) and α-diversity metrics, GMWI provides a more data-driven, evidence-based indexing system for evaluating the prebiotic effect of food components. This study demonstrates how GMWI can be applied as a novel PI in dietary intervention studies, with wider implications for designing personalized diets based on their impact on gut microbiome wellness.

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret "hairball" layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm's ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

Co-Membership-based Generic Anomalous Communities Detection

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities' sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community's vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

Tea pairings: Impact of aromatic congruence on acceptance and sweetness perception

Food pairing is a relevant tool for the food industry and for culinary professionals to develop successful flavor combinations and memorable experiences, but it could also be useful for encouraging consumers to adhere to a healthier diet. The general purpose of this study was to further investigate the perception of teas and butter cookies with and without aromatic congruence, deepening in sweetness perception. The experimental included: 1) a projective mapping test (30 semi-trained panelists) to group tea samples and choose representatives of each aromatic group; 2) the determination of the main volatile organic compounds using Solid Phase Micro Extraction-Gas Chromatography Mass Spectrometry (SPME-GC-MS) to prove the aromatic congruence of the designed tea-cookie pairings; 3) a consumer study (n = 89) to assess liking, sweetness perception, of the single samples and pairings, and the pairing principles of the congruent and non-congruent parings. Results of the projective mapping showed that the tea samples could be grouped into 3 main categories by their herbal, fruity-sweet, and brown-sweet notes, results also supported by the GCMS data. Harmony was positively correlated to liking, and Balance and Similarity seemed to be related to aromatic "congruence", although all pairings were similarly liked. Sugar content was similar in all the cookie samples and pairings, but sweetness perception was significantly influenced by the aroma of the samples, being the samples and pairings made with spearmint the least sweet ones. Pairing a tea with sweet aromas with the spearmint cookie, independently of the kind of sweet aromatics (e.g.: coconut, almond, vanilla, fruity, tropical), seemed to slightly increase sweetness perception, although significant differences were not detected with other spearmint cookie pairings. Findings of the present research sum knowledge to the food pairing area, but further research is needed in recommending appropriate methodologies for pairing assessment, as well as the potential uses of driven pairings in specific food cultures.

Applications of knowledge graphs for food science and industry

The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.

Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

The human senses shape the life in several aspects, namely well-being, socialization, health status, and diet, among others. However, only recently, the understanding of this highly sophisticated sensory neuronal pathway has gained new advances. Also, it is known that each olfactory receptor cell expresses only one type of odorant receptor, and each receptor can detect a limited number of odorant substances. Odorant substances are typically volatile or semi-volatile in nature, exhibit low relative molecular weight, and represent a wide variety of chemical families. These molecules may be released from foods, constituting clouds surrounding them, and are responsible for their aroma properties. A single natural aroma may contain a huge number of volatile components, and some of them are present in trace amounts, which make their study especially difficult. Understanding the components of food aromas has become more important than ever with the transformation of food systems and the increased innovation in the food industry. Two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-ToFMS) seems to be a powerful technique for the analytical coverage of the food aromas. Thus, the main purpose of this review is to critically discuss the potential of the GC × GC-based methodologies, combined with a headspace solvent-free microextraction technique, in tandem with data processing and data analysis, as a useful tool to the analysis of the chemical aroma clouds of foods. Due to the broad and complex nature of the aroma chemistry subject, some concepts and challenges related to the characterization of volatile molecules and the perception of aromas will be presented in advance. All topics covered in this review will be elucidated, as much as possible, with examples reported in recent publications, to make the interpretation of the fascinating world of food aroma chemistry more attractive and perceptive.

A Graph Theoretic Approach for Multi-Objective Budget Constrained Capsule Wardrobe Recommendation

Traditionally, capsule wardrobes are manually designed by expert fashionistas through their creativity and technical prowess. The goal is to curate minimal fashion items that can be assembled into several compatible and versatile outfits. It is usually a cost and time intensive process, and hence lacks scalability. Although there are a few approaches that attempt to automate the process, they tend to ignore the price of items or shopping budget. In this article, we formulate this task as a multi-objective budget constrained capsule wardrobe recommendation ( MOBCCWR ) problem. It is modeled as a bipartite graph having two disjoint vertex sets corresponding to top-wear and bottom-wear items, respectively. An edge represents compatibility between the corresponding item pairs. The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization. We study the complexity class of MOBCCWR , show that it is NP-Complete, and propose a greedy algorithm for finding a near-optimal solution in real time. We also analyze the time complexity and approximation bound for our algorithm. Experimental results show the effectiveness of the proposed approach on both real and synthetic datasets.

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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.

Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge's weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge's p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.

Analytical Characterization of the Widely Consumed Commercialized Fermented Beverages from Russia (Kefir and Ryazhenka) and South Africa (Amasi and Mahewu): Potential Functional Properties and Profiles of Volatile Organic Compounds

In this study, four commercialized indigenous fermented beverages most highly consumed in Russia (kefir and ryazhenka) and South Africa (amasi and mahewu) were analyzed for their potential health-promoting properties and flavor-forming volatile organic compounds (VOC). The analysis of antioxidant capacity demonstrated superiority of dairy-based beverages (kefir, ryazhenka and amasi) over the corn-based mahewu; however, mahewu outperformed dairy-based beverages in terms of its potential antihypertensive effect (i.e., the ability to inhibit angiotensin I converting enzyme). The fatty acid (FA) content of kefir and ryazhenka were more diverse compared to that of amasi, but included a lesser amount of branched chain FA. In terms of calculated FA nutritional indices (e.g., indices of atherogenicity and thrombogenicity), kefir and ryazhenka performed similarly and significantly better than amasi. The agreement between beverages theoretical flavor profiles, which was obtained based on the flavors of individual VOC, and consumers’ flavor perception allow hypothesizing about the contribution of detected VOC to the overall products’ flavor. The obtained data expand current knowledge regarding traditional fermented beverages and their values in terms of national dietary recommendations. Additionally, reported VOC profiles will promote the inclusion of traditional fermented beverages into the rations based on the flavor pairing concept (which is controversial but widely applied).

Application Research: Big Data in Food Industry

A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

Systematic Review of Methods Used for Food Pairing with Coffee, Tea, Wine, and Beer

The current article is aimed at systematically reviewing the research methods used for food pairing with coffee, tea, wine, and beer. The primary aim of this review was to elucidate the state-of-the-art methods used for analysing food and beverage pairings with coffee, tea, wine, and beer; secondarily, to identify the basis of the selection criteria; and lastly, the method used to evaluate those pairings. The search was performed in three databases: Web of Science, ScienceDirect, and Scopus. Criteria for inclusion were studies with an experimental design, a descriptive analysis (DA), and/or hedonic consumer analysis of beverage and food pairing. The outcome had to be measured on a hedonic Likert scale, a line scale, a just about right (JAR), or a modified JAR scale or other relevant scale measurement method for the given attribute. A total of 24 studies were included in this review—the majority aimed at finding good food and beverage pairings. Most pairings were based on suggestions from experts on popular/common, similar origin, or quality of beverages and foods. The outcomes were measured in several different scales, precluding a direct comparison. The 24 articles used in this review did not provide a so-called “golden standard” of the pairing method. Only three articles provided a more scientifically based approach to investigate why a food and beverage pairing is perceived as a good match, using aromatic similarity, the primary taste, and the sensation of koku as their experimental factors.

Social Media Mining for an Analysis of Nutrition and Dietary Health in Taiwan

Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly. These online recipes represent different cultures and cooking methods from various regions, and provide important indications on nutritional content. In recent years, the development of data science made data mining a popular research area. However, only a few researches in Taiwan have applied data mining in the studies of recipes and nutrients. Therefore, this work aims at utilizing machine learning models to discover health-related insights from recipes on social media. First, we collected over 15,000 Chinese recipes from the largest recipe website in Taiwan to build a recipe database. We then extracted information from this dataset through natural language processing methodologies so as to better understand the characteristics of various cuisines and ingredients. Thus, we can establish a classification model for the automatic categorization of recipes. We further performed cluster analysis for grouping nutrients to recognize the nutritional differences for each cluster and each cuisine type. The results showed that using the support vector machine (SVM) model can successfully classify recipes with an average F-score of 82%. We also analyzed the nutritional value of different cuisine categories and the possible health effects they may bring to the consumers. Our methods and findings can assist future work on extracting essential nutritional information from recipes and promoting healthier diets.

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.

Mass Spectrometry-Based Flavor Monitoring of Peruvian Chocolate Fabrication Process

Flavor is one of the most prominent characteristics of chocolate and is crucial in determining the price the consumer is willing to pay. At present, two types of cocoa beans have been characterized according to their flavor and aroma profile, i.e., (1) the bulk (or ordinary) and (2) the fine flavor cocoa (FFC). The FFC has been distinguished from bulk cocoa for having a great variety of flavors. Aiming to differentiate the FFC bean origin of Peruvian chocolate, an analytical methodology using gas chromatography coupled to mass spectrometry (GC-MS) was developed. This methodology allows us to characterize eleven volatile organic compounds correlated to the aromatic profile of FFC chocolate from this geographical region (based on buttery, fruity, floral, ethereal sweet, and roasted flavors). Monitoring these 11 flavor compounds during the chain of industrial processes in a retrospective way, starting from the final chocolate bar towards pre-roasted cocoa beans, allows us to better understand the cocoa flavor development involved during each stage. Hence, this methodology was useful to distinguish chocolates from different regions, north and south of Peru, and production lines. This research can benefit the chocolate industry as a quality control protocol, from the raw material to the final product.

Drosophila serotonin 2A receptor signaling coordinates central metabolic processes to modulate aging in response to nutrient choice

It has been recognized for nearly a century that diet modulates aging. Despite early experiments suggesting that reduced caloric intake augmented lifespan, accumulating evidence indicates that other characteristics of the diet may be equally or more influential in modulating aging. We demonstrate that behavior, metabolism, and lifespan in Drosophila are affected by whether flies are provided a choice of different nutrients or a single, complete medium, largely independent of the amount of nutrients that are consumed. Meal choice elicits a rapid metabolic reprogramming that indicates a potentiation of TCA cycle and amino acid metabolism, which requires serotonin 2A receptor. Knockdown of glutamate dehydrogenase, a key TCA pathway component, abrogates the effect of dietary choice on lifespan. Our results reveal a mechanism of aging that applies in natural conditions, including our own, in which organisms continuously perceive and evaluate nutrient availability to promote fitness and well-being.

The foods we eat can affect our lifespan, but it is also possible that thinking about food may have effects on our health. Choosing what to eat is one of the main ways we think about food, and most animals, including the fruit fly Drosophila melanogaster, choose their foods. The effects of these choices can affect health via a chemical in the brain called serotonin. This chemical interacts with proteins called serotonin 2A receptors in the brain, which then likely primes the body to process nutrients.

To understand how serotonin affected the lifespan and health of fruit flies, Lyu et al. compared flies that were offered a single food to those that could choose between several foods. The flies that had a choice of foods lived shorter lives and produced more serotonin, but these effects were reversed when Lyu et al. limited the amount of a protein called glutamate dehydrogenase, which helps cells process nutrients.

These results suggest that choosing what we eat can impact lifespan, ageing and health. Human and fly brains share many similarities, but human brain chemistry is more complex, as is our experience of food. This work demonstrates that food choices can affect lifespan. More research into this phenomenon may shed further light onto how our thoughts and decision-making impact our health.

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.

Umami potential of fermented beverages: Sake, wine, champagne, and beer

The free amino acid (FAA) contents of a special selection of fermented beverages have been measured by ultra-high performance liquid chromatography (UHPLC). The selection, which includes 8 sakes, 9 white, rosé, and sparkling wines, 9 genuine champagnes, as well as 5 types of beer, was made to uncover the umami potential of different types of fermented beverages, in particular whether long yeast contact and ageing may influence the contents of free glutamate that is known to elicit umami sensation. The data show that in particular sakes as well as some beers, wines and champagnes with long yeast contact contain appreciable amounts of free glutamate. The results are discussed in the context of food pairing where umami synergy can be achieved by combining fermented beverages with long yeast contact with food rich in free nucleotides.

Umami synergy as the scientific principle behind taste-pairing champagne and oysters

Food and flavour pairing are commonly used as an empirically based phenomenology by chefs and food innovators for creating delicious dishes. However, there is little if any science behind the pairing systems used, and it appears that pairing is determined by food culture and tradition rather than by chemical food composition. In contrast, the pairing implied by the synergy in the umami taste, elicited by free glutamate and free nucleotides, is scientifically founded on an allosteric action at the umami receptor, rendering eggs-bacon and cheese-ham delicious companions. Based on measurement of umami compounds in champagnes and oysters we suggest that a reason why champagne and oysters are considered good companions may be the presence of free glutamate in champagne, and free glutamate and 5′-nucleotides in oysters. By calculations of the effective umami potential we reveal which combinations of oysters and champagnes lead to the strongest umami taste. We also show that glutamate levels and total amount of free amino acids are higher in aged champagnes with long yeast contact, and that the European oyster (Ostrea edulis) has higher free glutamate and nucleotide content than the Pacific oyster (Crassostrea gigas) and is thus a better candidate to elicit synergistic umami taste.

Community detection in bipartite networks with stochastic block models

In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. Here we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks which parsimoniously chooses the number of communities. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of sqrt[2], and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where nonhierarchical models outperform their more flexible counterpart.

A conceptual framework of evolutionary novelty and innovation

Since 1990 the recognition of deep homologies among metazoan developmental processes and the spread of more mechanistic approaches to developmental biology have led to a resurgence of interest in evolutionary novelty and innovation. Other evolutionary biologists have proposed central roles for behaviour and phenotypic plasticity in generating the conditions for the construction of novel morphologies, or invoked the accessibility of new regions of vast sequence spaces. These approaches contrast with more traditional emphasis on the exploitation of ecological opportunities as the primary source of novelty. This definitional cornucopia reflects differing stress placed on three attributes of novelties: their radical nature, the generation of new taxa, and ecological and evolutionary impact. Such different emphasis has led to conflating four distinct issues: the origin of novel attributes (genes, developmental processes, phenotypic characters), new functions, higher clades and the ecological impact of new structures and functions. Here I distinguish novelty (the origin of new characters, deep character transformations, or new combinations) from innovation, the ecological and evolutionary success of clades. Evidence from the fossil record of macroevolutionary lags between the origin of a novelty and its ecological success demonstrates that novelty may be decoupled from innovation, and only definitions of novelty based on radicality (rather than generativity or consequentiality) can be assessed without reference to the subsequent history of the clade to which a novelty belongs. These considerations suggest a conceptual framework for novelty and innovation, involving: (i) generation of the potential for novelty; (ii) the formation of novel attributes; (iii) refinement of novelties through adaptation; (iv) exploitation of novelties by a clade, which may coincide with a new round of ecological or environmental potentiation; followed by (v) the establishment of innovations through ecological processes. This framework recognizes that there is little empirical support for either the dominance of ecological opportunity, nor abrupt discontinuities (often caricatured as 'hopeful monsters'). This general framework may be extended to aspects of cultural and social innovation.

Going Vegan: The Role(s) of ICT in Vegan Practice Transformation

With the debate on climate change, topics of diet change and the reduction of animal products have become increasingly important in both public and academic discourses. However, sustainable ICT studies have so far focused on individual aspects, in particular investigating the criticized persuasive design approach. We argue for a broader perspective on the role(s) of ICT, one that helps in identifying opportunities to support consumer practice transformation, beyond motivational aspects. Based on retrospective interviews with 16 vegans, we argue to understand practice transformation as co-evolution of practices and ICT artefacts, as this perspective helps to understand how tensions arising from complex entanglements of practices, socio-material contexts, and communities can be resolved. Rather than a motivational process, we observe various roles of ICT artefacts co-evolving with practices: Ranging from initial irritation, to access to information about vegan practices, to the learning of vegan food literacy, to the negotiation of a vegan identity, and vegan norms at the intersection of the ‘odd’ and the ‘norm’.

Visual Cultural Biases in Food Classification

This article investigates how visual biases influence the choices made by people and machines in the context of online food. To this end the paper investigates three research questions and shows (i) to what extent machines are able to classify images, (ii) how this compares to human performance on the same task and (iii) which factors are involved in the decision making of both humans and machines. The research reveals that algorithms significantly outperform human labellers on this task with a range of biases being present in the decision-making process. The results are important as they have a range of implications for research, such as recommender technology and crowdsourcing, as is discussed in the article.

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.