How coupled are capillary electrophoresis and mass spectrometry

The understanding of how science works can contribute to making scientific development more effective. In this paper, we report an analysis of the organization and the interconnection between unbalanced areas of study. More specifically, we considered two important subareas in analytical chemistry, namely capillary electrophoresis (CE) and mass spectrometry (MS). These areas are particularly interesting because MS is employed in a bigger range of applications than CE. Consequently, these different portions of papers can interfere in the quality of the searches for papers devoted to CE–MS. Here, we considered a citation network in which the nodes and connections represent papers and citations, respectively. The network clusters were detected by employing the Infomap algorithm. By considering the clusters and the respective abstracts, the subjects were identified. Interesting results were found, including a marked separation between some clusters of articles devoted to instrumentation techniques and applications, which was quantified from the abstract contents. For instance, the most well-defined community was assigned as CE (Instru.), with 73.8% of the papers having abstracts that include the word “electrophoresis” and not the word “mass”. However, the papers that describe CE–MS did not lead to a well-defined cluster. In order to better understand the organization of the citation network, we considered a multi-scale analysis, in which we used the information regarding sub-clusters. Firstly, we analyzed the sub-cluster that contains the first article devoted to the coupling between CE and MS, whose subject was found to be a good representation of its sub-cluster. The second analysis was about the sub-cluster of a seminal paper known to be the first that dealt with protein analysis by using CE–MS and a similar result was obtained. By considering the proposed methodologies, our paper can contribute to researchers working with similar scenarios, since it shows that a given subject can be spread on many clusters of the network, therefore, lead to better literature reviews.