The rapidly growing body of publicly available data on food chemistry and food usage can be analysed using data mining and network analysis methods. Here we discuss how these approaches can yield new insights both into the sensory perception of food and the anthropology of culinary practice. We also show that this development is part of a larger trend. Over the past two decades large-scale data analysis has revolutionized the biological sciences, which have experienced an explosion of experimental data as a result of the advent of high-throughput technology. Large datasets are also changing research methodologies in the social sciences due to the data generated by mobile communication technology and online social networks. Even the arts and humanities are seeing the establishment of 'digital humanities' research centres in order to cope with the increasing digitization of literary and historical sources. We argue that food science is likely to be one of the next beneficiaries of large-scale data analysis, perhaps resulting in fields such as 'computational gastronomy'. Keywords: Networks, Data mining, Sensory science, Computational gastronomy, Flavour compounds The past two decades have seen the advent of highthroughput technologies in biology, making it possible to sequence genomes cheaply and quickly, to measure gene expression for thousands of genes in parallel, and to test large numbers of potential regulatory interactions between genes in a single experiment. The large amounts of data created by these technologies have given rise to entire new research areas in biology, such as computational biology and systems biology. The latter, which attempts to understand biological processes at a 'systems' level, is particularly indicative of the potential advantage that large datasets and their analysis can offer to biology, and to other fields of research. This advantage is a 'birds-eye' perspective, which, with the right kind of analysis, can complement the more established research methods that take place 'on the ground' and investigate the system in much more detail. An example would be the analysis of high-throughput gene expression data of tumour tissues in order to highlight a set of potential candidate genes that may play a role in causing a particular cancer. These candidates would then be investigated one by one, for instance by creating mutant organisms in which one of these genes is deactivated. Similar large-scale data analysis methods have more recently arrived in the social sciences as a result of rapidly growing mobile communications networks and online social networking sites. Here too data analysis offers a birds-eye perspective of large social networks and the opportunity to study social dynamics and human mobility on an unprecedented scale. The most recent research areas to be transformed by information technology are the Arts and Humanities, which have witnessed the emergence of 'digital humanities'. As more and more literary and historical documents are digitized, it becomes possible to uncover fundamental relationships that underlie large corpora of literary texts, or long-term historical and political developments. A striking example is the discovery by Lieberman et al. [1] that the regularisation of verbs across 12 centuries of English is governed by a simple quantitative relationship between the frequency of verb usage and the speed at which it is regularised.
The availability of large datasets now makes it possible to study health outcomes at unprecedented scales REF .
Network analysis and data mining in food science: the emergence of computational gastronomy
Flavour
Flavour
Psychology