Quantitative Analysis of Food Science Trends

Abstract

We read a recent editorial that discusses the importance of big data in conjunction with analytical methods in food science (Foegeding, 2018). The editorial prompted us to ponder the relationship between big data and food science, and we found that it would be worth examining the relationship in the context of the history of food science. In this letter, we report our findings and share our perspective on the result with the readers of this journal. In today's digital era, the amount of information grows exponentially, generating a massive volume of data every day that humanity has never seen before. The term "big data" has been used to describe those massive data sets (McAfee & Brynjolfsson, 2012). Even though the term "big data" has been widely used, there is no formal consensus on the definition of the term. A group recently proposed a definition for the term based on a literature search: "Big data represents the information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value" (De Mauro, Greco, & Grimaldi, 2015). While big data usage has occurred in numerous fields including business, science, and media industries (Mayer-Schönberger & Cukier, 2014), it was introduced to biological science about ten years ago (Howe et al., 2008). How can big data make sense to the users? Holmes succinctly states in her recent book (Holmes, 2018) that "Big data is only useful if we can extract useful information from it." One of the essential tools in extracting information from big data is quantitative skills, which include mathematical, statistical, and computational techniques (Kepner & Jananthan, 2018; EMC Education Services, 2015; Pierson, 2015). Therefore, we thought that it may be worth evaluating food science with respect to its readiness for the big data era as discussed in the editorial (Foegeding, 2018). How can a certain academic discipline be evaluated, preferentially, quantitatively? In our previous publications (Kang & Purnell, 2011; Kang, Park, Venkat, & Gopinath, 2015), we showed that the number of articles in a certain academic field archived in an indexation system could represent the activity of the academic field. There are many indexation services developed, such as PubMed, EMBASE, and SCOPUS (Balhara, 2012). While it is not an easy task to select the best one among those indexation systems, PubMed, a search engine for biomedical papers archived in MEDLINE (https://www.ncbi.nlm.nih.gov/pubmed), is considered one of the best databases (Farassati, 2017). In our previous research (Kang & Purnell, 2011; Kang et al., 2015), we used PubMed to assess several biomedical sciences including biochemistry, biophysics, bioinformatics, and systems biology. Employing the strategy, we searched the PubMed database to examine how much quantitative skills have been utilized in food science. According to [Eq. 3], the asymptotic maximum value of the proportion of quantitative food science papers is 3.7%. The value of h(2018) is 3.1%, which is 83% of the maximum value. Both Figure 1B and 1C clearly show that the usage of quantitative skills in food science has exponentially grown for the last 30 years. Nonetheless, the magnitude of their contribution is not significant as its projection is just 4% at most (Figure 1C), which means that only 4 out of 100 papers in food science use quantitative skills substantially. As we indicated above, big data analysis requires a great usage of quantitative skills. The current status of big data usage in food science is primitive according to our search of PubMed with a search phrase "food" AND "big data": so far, 117 papers have been published in food science research using big data since the first food science publication with big data analysis in 2013 (the search was done in August 2018 when we revised this letter). The degree of the usage of big data in food science can be compared with that in other fields, for example, bioinformatics or immunology. The number of publications in bioinformatics and immunology using big data is 500 and 156, respectively, as of August 2018. Finally, we would like to comment on the education of food science based on our findings shown in this paper. It is highly desirable to adjust the academic curriculum of any academic disciplines according to their development, especially if the field is growing rapidly (Kang & Purnell, 2011; Kang et al., 2015). Considering the wide and rapid application of big data to other areas (Mayer-Schönberger & Cukier, 2014), we expect that big data will play an important role in food science in the near future as suggested in the editorial (Foegeding, 2018). Therefore, we suggest that the food science community should consider developing courses that introduce quantitative skills to students in food science, who will be the future users and/or producers of "big data" in the 21st century. Sincerely,

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