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Bibliometric mapping: eight decades of analytical chemistry, with special focus on the use of mass spectrometry Cathelijn Waaijer, and Magnus Palmblad Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ac5040314 • Publication Date (Web): 06 Mar 2015 Downloaded from http://pubs.acs.org on March 19, 2015

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Analytical Chemistry

Bibliometric mapping: eight decades of analytical chemistry, with special focus on the use of mass spectrometry Cathelijn J.F. Waaijer1 and Magnus Palmblad2 1

Centre for Science and Technology Studies, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, the Netherlands

2

Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands Address: Centre for Science and Technology Studies, PO box 905, 2300 AX, Leiden, The Netherlands, tel. +31 71 527 6072, fax. +31 527 3911, email: [email protected]

Biography Cathelijn Waaijer works as a doctoral researcher at the Centre for Science and Technology Studies at Leiden University. She did her M.Sc. in Biomedical Sciences and did a research project on the mass spectrometric analysis of cartilage tumors at the Leiden University Medical Center (LUMC). Magnus Palmblad is associate professor at the LUMC Center for Proteomics and Metabolomics specializing on clinical applications of and informatics solutions for mass spectrometry based proteomics.

Summary In this Feature we use automatic bibliometric mapping tools to visualize the history of analytical chemistry from the 1920s until the present. In particular, we have focused on the application of mass spectrometry in different fields. The analysis shows major shifts in research focus and use of mass spectrometry. We conclude by discussing the application of bibliometric mapping and visualization tools in analytical chemists’ research.

Introduction Bibliometrics is the study of interrelated bodies of documents, a prime example being the scientific literature. One of its best known applications is the comparative evaluation of countries, universities, research institutes, and individual researchers, but it may also be used for other purposes, such as gaining a better understanding of a field’s structure, or determining developments in research topics. It is the latter application that we will highlight in this Feature, by using automatic bibliometric mapping tools to map developments within analytical chemistry. Compared to more traditional historical methods, automatic bibliometric mapping of scientific literature has the advantage of relative ease and low laboriousness. Furthermore, the field 1 ACS Paragon Plus Environment


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structure is established by (almost) automatic methods, producing a more objective result than manual mapping could. Mapping of networks visualizes multiple items (nodes) and their underlying relationships (edges). The nodes can be different entities, e.g., authors, journals, or key terms occurring in research papers. In addition, the edges can be based on different types of data, e.g., in a network of authors one could determine which authors co-author papers (a co-authorship network) or who cites whom (a citation network). The first bibliometric maps were manually constructed citation networks.1 Garfield, Sher and Torpie studied a book on the history of genetics and compared the dependencies between different studies as described by the author to the citational patterns between the studies, and found that the two methods closely mirror each other. Hence, citation networks are able to show structures in knowledge flows. Ipso facto citation networks have a temporal aspect: a publication can only refer to earlier published work. However, the temporal aspect is often not explicit in citation networks as time is not explicitly shown in the visual representation of the networks. Exceptions include main path analysis for citation networks2, and the HistCite3 and CitNetExplorer software tools.4 All of these show chronological maps of the main lines of research through time, but by means of different methods. Although citation networks do represent an underlying structure of (fields of) scientific knowledge, they do not directly represent the content of papers. To this end, co-word maps can be constructed. In this approach terms are extracted from papers (e.g., from titles and abstracts) and for each pair of terms the number of papers in which they both occur is determined. Terms that appear often together are likely to concern the same subject matter, whereas terms that never appear together are unlikely to be related subject-wise. Counting the co-occurrences for every pair of terms yields a co-occurrence matrix of terms. Further steps include the normalization of this matrix and its visualization.5-7 In this Feature we use co-word and citation networks of different bodies of documents concerning analytical chemistry to show shifts in research topics within this field. Special attention is given to a method that became increasingly important in analytical chemistry, mass spectrometry (MS).

Evolution of topics in analytical chemistry 1929-2012 In this study we use three sources of data as input for bibliometric analysis (Fig. 1). The first is this journal, founded in 1929 as Industrial & Engineering Chemistry Analytical Edition and renamed to Analytical Chemistry in 1947. Most if not all scientometric studies employing mapping methods to analyze the development of research fields have used titles and abstracts obtained from large bibliographic databases, of which the best known are Web of Science (WoS), founded in 1964 by Garfield as the Science Citation Index and acquired by Thomson Reuters in 1992, and Scopus, launched in 2004 by Elsevier. However, abstracts were not regularly included before 1990. To map the evolution of research topics within analytical chemistry over a long period, it was therefore impossible to use ready-to-use databases such 2 ACS Paragon Plus Environment

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as WoS or Scopus. Instead we extracted the titles and abstracts of all Analytical Chemistry papers published between 1929 and 2013, and use this journal as a proxy for the field of analytical chemistry.* A detailed description of the abstract extraction procedure, and of the other methods used in this paper, can be found in the Supporting Information. A glossary of the discussed terms, techniques and software tools is given in Table 1. -- Figure 1 about here -- (274 words) -- Table 1 about here -- (500 words) First, we constructed co-word maps of the field of analytical chemistry in each decade: 19291940, 1941-1950, 1951-1960, 1961-1970, 1971-1980, 1981-1990, 1991-2000, and 2001-2012 and visualized these in the software VOSviewer (click on a decade to download the corresponding map as an interactive Java application; Javascript 6 or higher required).8 On these maps, terms that occur together often are positioned close to each other, whereas terms that co-occur less often are positioned further apart. Furthermore, clustering of terms into four to eight clusters with different colors is applied using an algorithm that finds the clustering solution that fits the co-occurrences between the different terms best (modularity-based clustering).9 The 1929-1940 maps shows four different clusters: apparatuses (in green), gases (in pink), inorganic chemistry (in red), and industrial applications, hydrocarbons and food analysis (in cyan) (Fig. 2). For the other decades, the description of the clusters is given in Table 2. The maps show that inorganic chemistry (red) has been an important topic within analytical chemistry for a long time; from 1929 until 1990 there were one or more clusters on inorganic chemistry. In the 1991-2000 period it was merged with the topics of electrochemistry and sensors. Much attention was given to (the development of) different apparatuses between 1929 and 1980 (green). A cluster on general and editorial issues can be found in almost every period (yellow). Topics that have developed over time include electrochemistry, chromatography and mass spectrometry. Electrochemistry shows up as its own cluster in the 1951-1960 period (sea green), but terms relating to the subject can also be found in the inorganic chemistry and metals cluster from 1941. This suggests the topic of electrochemistry has developed from inorganic chemistry and metals to form its own subfield. Chromatography is apparent in the maps from the 1951-1960 period onwards (cyan); mass spectrometry from the 1971-1980 period. The maps suggest the widespread use of mass spectrometry in analytical chemistry primarily developed through its coupling to chromatography (cyan); for the 1971-1980 period terms relating to mass spectrometry can be discerned in the maps, but the cluster is still dominated by chromatographic techniques and applications. However, from the 1981-1990 period, mass spectrometry broke off and formed its own subfield (blue).

*

Naturally, the use of a single journal as a representation of a field is an oversimplification of all research in a field. However, working with a single journal also has clear advantages, such as the continuity of a single, uninterrupted stream of publications in a journal managed by one editorial board (which changes slowly over time), a finite number of article types and formats and the practical possibility of accessing all articles published over nearly a century. For 1929-1995, only scanned first pages of articles processed by Optical Character Recognition (OCR) to TXT-files were available. Thus, we extracted n words after the article’s title, with n being the average abstract length for a five-year period. Therefore, a limitation of our study is that for 1929-1995 we were not able to precisely extract abstracts, and that OCR errors were present in the texts.

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Finally, from 2001 a cluster on separations and microfluidics emerged (mustard). This cluster also contains terms relating to theory and simulations (of such microfluidic systems). -- Figure 2 about here -- (894 words) -- Table 2 about here -- (500 words) Next, we analyzed the development and use of a number of techniques within analytical chemistry. As a proxy, we determined how many articles mentioned the technique in their titles during the 1929-2012 period. It is important to note that this is only a proxy and as we only look for the mention of techniques in the article titles, there is bias towards novel uses and development of the technique. This approach shows that titration techniques reached their publication peak in the 1950s, gas chromatography in the 1960s, and liquid chromatography in the 1980s (Fig. 3). Of these techniques, only the latter was still mentioned in the titles of over 5% of papers published in the 2001-2012 period. On the other hand, microfluidics is an example of a technology not mentioned before 1990 that has really taken off in this 2001-2012 period. A technique not mentioned to a great extent in the titles of Analytical Chemistry papers is nuclear magnetic resonance (NMR), despite the fact that according to historical studies, it became an important physics-based analytical method in the second half of the 20th century.10 Presumably, chemical research using NMR was published in journals other than Analytical Chemistry. As the co-word maps already suggested, the mention of mass spectrometry increased throughout the entire period. Whereas between 1929 and 1940 none of the Analytical Chemistry papers mentioned mass spectrometry in their title, the fraction of papers that did increased to 18% in the 2001-2012 period (Fig. 3), revealing a continuous increase in relative importance of mass spectrometry in analytical chemistry. -- Figure 3 about here -- (93 words) Our bibliometric findings are by and large in accordance with historical studies on the development of analytical chemistry. Historical studies have also shown that analytical chemistry has undergone major changes during the 20th century. The main change has been a shift of focus from chemistry- to physics-based analytical methods.10,11 Before the instrument era, analytical chemists would first have to separate their compound of interest from the sample using chemical reactions, after which they could qualitatively identify the elements present in the compound. The first (now called “classical”) methods of quantitative determination involved gravimetric and volumetric analysis. In the 1940s and the early 50s, these methods were still the most used, together with colorimetric methods.12,13 The classical methods were still used in the late 1950s and 60s, but increasingly supplanted by chromatography, electrophoresis and MS.14,15 Another development during the investigated period was the decrease in the share of papers on inorganic and organic chemistry due to a surge in biochemical research, a finding mirrored in our maps.

Development and use of mass spectrometry As mass spectrometry became an important technique for analytical chemistry, we now zoom in on the use of this technique. Co-words maps of all Analytical Chemistry papers mentioning 4 ACS Paragon Plus Environment

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MS in their title or abstract were constructed (Fig. 4). Topics of clusters in the map are described in Table 3. The maps show a shift from analysis of smaller molecules (gases, hydrocarbons, metals) and isotope analysis to the analysis of larger and more complicated molecules (polymers, proteins). Main topics in the 1941-1960 period are hydrocarbons, structural analysis, quantitation and gases (again, click the period to download an interactive Java application of the map). The 1961-1970 and 1971-1980 periods are characterized by the emergence of software and by the development of apparatuses and interfaces. In addition, the 1971-1980 period saw the establishment of chromatography and chemical ionization as important ancillary technologies. Terms relating to secondary ion mass spectrometry (SIMS) can first be distinguished for the 1971-1980 period (as part of a cluster that also includes quantitation), and also formed a cluster in the 1981-1990 period (also including laser and plasma desorption) and the 1991-2000 period (also including the analysis of polymers). Another main topic that emerged in the 1991-2000 period is proteomics. In this period it formed a cluster with MALDI (while also being positioned close to the cluster on electrospray ionization, quadrupoles, ion traps, FTICR and MS/MS), but became its own cluster in the 2001-2012 period. MALDI then formed a cluster with imaging mass spectrometry. The main finding from these co-word maps is again the shift from the analysis of simple to more complex molecules, as was the case for analytical chemistry generally. The maps also provide the likely explanation that enabled this shift: the improvement of equipment, interfaces, and software, and especially the development of new physical techniques. Examples were the development of chemical ionization and SIMS in the 1970s, and MALDI in the 1990s. In addition, joining of older techniques and MS is evident from these maps, such as the incorporation of chromatography in the 1970s. These developments in turn enabled the applications of MS in a wider area of research, such as proteomics (enabled by several ionization techniques) and polymer analysis (enabled by SIMS), which is also visible in the maps. -- Figure 4 about here – (800 words) -- Table 3 about here -- (500 words) Next, we set out to estimate how often MS was used over time in all research fields, insofar as they are covered by the WoS database. We determined how many articles in the WoS had the term “mass spectrometry” in their title or abstract. The plotted graph of this absolute number shows a large increase from 1981 until 2013 (Fig. 5a; pink plot). The graph is discontinuous between 1990 and 1991 due to abstracts being regularly included into the WoS database only from 1991, including for journals publishing many papers using MS, such as Journal of Biological Chemistry, Journal of Chromatography, and Rapid Communications in Mass Spectrometry. After the 1991 discontinuity, the increase in use of MS is still considerable. In 1991, about 2,600 papers on or including MS were published; by 2013, this figure had increased to around 16,000. However, the WoS database expanded tremendously between 1981 and 2013, due to an increasing number of journals being covered and each journal on average publishing more articles per year (Supplementary Figure S-1). Therefore we also determined the relative number of articles with MS in their titles or abstracts. This analysis shows that MS was indeed increasingly used in relative terms (Fig. 5a; yellow). 5 ACS Paragon Plus Environment


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This raises the question which scientific disciplines work on (and with) MS. We determined which disciplines mainly contribute to research involving MS. To this end we used the NOWT classification of journals, which classifies journals according to scientific disciplines (see Supplementary Table S-1 for a complete list).16 This reveals that most research using MS has been published in journals from the chemistry, physics and astronomy category (Fig. 5b).† However, the emphasis of these disciplines has decreased over time as the use of MS in the life sciences increased dramatically between 1991 and 2013. Furthermore, there has been a slight decrease in the share of papers published in engineering journals. This analysis, however, only measures the share of scientific disciplines in the total output of research using MS. Therefore, we also determined to what extent MS was used per scientific discipline. Results of the latter analysis show that the use of MS in the chemistry, physics and astronomy category has still been increasing over the past fifteen years, albeit slowly (Fig. 5c). In comparison, there has been a large increase in the use of MS in the life sciences – from about 0.75% of all papers in life sciences journals to over 2.5%. The use of MS in the medical sciences has also increased, as it has in earth and environmental sciences, but in the latter, the rate of growth has decreased. As mentioned above, it is important to keep in mind that text mining from titles and abstracts is more likely to pick up new applications of novel technologies rather than routine, established use, where they may only appear in the methods section. -- Figure 5 about here -- (617 words) Finally, we investigated which lines of research have been the most important in research employing MS and which papers have been most influential. To this end, we visualized a longitudinal citation network using CitNetExplorer.4 All articles, reviews and letters with the term “mass spectrometry” in the title, abstract or listed keywords published in the online version of the WoS, along with their cited references, were included into the analysis. The inclusion of cited references makes it possible to also include scientific work that is not included in the WoS, such as textbooks, older articles, and articles not employing MS but cited by mass spectrometrists, in the citation network. For a short explanation on mapping and clustering, see the glossary (Table 1). The citation network displays ten clusters, of which the largest is on peptides and proteins (Fig. 6). The network shows a tightly knit group of clusters on peptides and proteins (dark blue) and metabolites (orange), and part of a cluster on technical aspects of MS (dark green). Clusters of research on carbohydrates (red) and lipids, plants and neonatal metabolism (brown) are also quite closely related to this group. The food analysis cluster (pink) is connected both to the aforementioned metabolite cluster and to a cluster on environmental research (purple). Further right on this map is part of the cluster on surfaces and polymers (bright green); the other part on imaging mass spectrometry is mapped more closely to the biological group. Finally, there are discrete clusters on atmospheric and geological science (cyan), and isotopes (yellow).

†

Most research using MS in this category has been published in chemistry and physics journals, not in astronomy journals.


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In addition we determined which research paper per cluster is the most cited (excluding reviews and book chapters). This analysis attempts to find the main papers influencing applications of mass spectrometry in different fields. It turns out that for a considerable number of clusters, the most cited research paper is not one specifically on MS, e.g., Laemmli’s paper on protein quantitation, Arthur and Pawliszyn’s on the solid phase microextraction of organochlorides, Bligh and Dyer’s on lipid extraction, Van den Dool and Kratz’s on gas chromatography, and Guenther et al.’s on gas emission (Fig. 5b), again illustrating MS is frequently combined with other methods. -- Figure 6 about here -- (585 words) In conclusion, analysis of the scientific papers in which MS is mentioned reveals a shift from development of the technology by the physics and chemistry communities to application in the life, medical, and earth and environmental sciences. This shift is evident from a slight increase in the use, or further development, of MS within physics and chemistry, but a much larger increase in the more applied fields. The most cited papers in research applying MS are often not concerned with MS, but with allied technologies, illustrating the interdisciplinary nature of much of the research using MS. The results from this bibliometric analysis trace the common historical narrative of mass spectrometry, e.g., as described in Grayson’s Measuring Mass: From Positive Rays to Proteins.17 This book also cites the commercialization of mass spectrometers as the main enabling factor behind the expansion of applications of MS. This commercialization was first fueled by the oil industry and the Manhattan Project, but later driven by a need for pharmaceutical and environmental analysis.

Conclusions Bibliometric mapping reveals clear shifts in analytical chemistry research topics from inorganic and (small-molecule) organic chemistry to biochemistry and complex biomolecules. Furthermore, a sequence of new methods emerging and sometimes replacing older ones is apparent in the time series analyses. It is important to note that our findings are, for the most part, neither new nor even unexpected, but rather support historical research and the results from other bibliometric methods that have been used to investigate the development of analytical chemistry. However, we show here how these findings can be obtained using semiautomatic bibliometric mapping methods to visualize the evolution of research fields in an unsupervised manner.

Bibliometric visualization tools for your own research The use of bibliometric visualization tools is not limited to historical analyses such as the one presented here. The tools can also be used to obtain a comprehensive view other research fields. This is especially useful for junior researchers who are getting started in the field and would like to have a first glance at its structure. Furthermore, by charting time series new topics of potential interest to the researcher can be found. Co-word maps are especially wellsuited to obtain an overview of a field, as they show the main terms used in that field.



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However, there are limitations to co-word analysis, e.g., researchers use different writing styles, and terminology, homonyms and synonyms all affect the co-occurrence of terms. In addition, mapping by definition is a simplification, which causes loss of information.18 For example, electrospray ionization has arguably been a major development in the development of MS.19 It was first developed for recording mass spectra of large biomolecules by Yamashita and Fenn already in 1984. However, the terms “electrospray ionization” and “ESI” are not depicted on our 1981-1990 maps on MS, but only from 1991-2000, presumably because the number of minimum occurrences had not reached the set threshold for the 19811990 period. Hence, only the largest subfields are generally visible on the map, whereas the smaller ones (that might actually hold the most exciting developments) are not. Furthermore, the division of terms into clusters is to some extent subject to the chosen clustering parameters, making clustering dependent on a certain subjectivity. Still, the main structure of a research field is easily mapped. Bibliometric visualization tools may be useful to uncover unexpected linkages to other fields and scientific literature as well. Citation network visualization is useful to analyze how a body of documents is related and which other work it draws upon. The ability to uncover linkages in the scientific literature may be especially of use when writing a review of the literature. CitNetExplorer is a useful tool in this regard, as it can also show references to articles not included in the input data. In this work we used two different bibliometric visualization tools that suited our purposes, VOSviewer and CitNetExplorer.4,8 However, there are many other mapping tools (also freely available) that have more extensive functionality for other purposes. Examples include CiteSpaceII, and Sci2.5,6 CiteSpaceII provides a built-in database for data handling, as well as geospatial analysis features. Sci2 has functionality for geospatial analysis and network analysis, such as calculation of in-degree, k-core, and community detection.

Acknowledgements We would like to express our gratitude to the American Chemical Society for making data available for this study and technical support. In particular, we would like to thank Catherine Boylan, Emma Moore, David Martinsen, and Jeffrey Krugman. We also thank Rob Marissen (LUMC) and Bjorn Victor (Institute for Tropical Medicine, Antwerp) for technical assistance. Finally, we would like to thank Nees Jan van Eck and Ludo Waltman (both CWTS), and Michael Grayson for fruitful discussions.

References (1) Garfield, E.; Sher, I. H.; Torpie, R. J. The use of citation data in writing the history of science; Institute for Scientific Information Inc.: Philadelphia, PA, 1964. (2) Hummon, N. P.; Doreian, P. Soc. Networks 1989, 11, 39-63. (3) Garfield, E. J. Inf. Sci. 2004, 30, 119-145. (4) van Eck, N. J.; Waltman, L. J. Informetr. 2014, 8, 802-823. (5) Chen, C. M. J. Am. Soc. Inf. Sci. Tec. 2006, 57, 359-377.


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(6) Sci2 team. Science of Science (Sci2) Tool; Indiana University and SciTech Strategies, https://sci2.cns.iu.edu, 2009. (7) van Eck, N. J.; Waltman, L.; Dekker, R.; van den Berg, J. J. Am. Soc. Inf. Sci. Tec. 2010, 61, 2405-2416. (8) van Eck, N. J.; Waltman, L. Scientometrics 2010, 84, 523-538. (9) Waltman, L.; van Eck, N. J.; Noyons, E. C. M. J. Informetr. 2010, 4, 629-635. (10) Reinhardt, C.; Shifting and Rearranging: Physical Methods and the Transformation of Modern Chemistry; Science History Publications/USA: Sagamore Beach, MAs, 2006; pp 321. (11) Baird, D. Ann. Sci. 1993, 50, 267-290. (12) Strong, F. C. Anal. Chem. 1947, 19, 968-971. (13) Fischer, R. B. Anal. Chem. 1956, 28, 9A-14A. (14) Brooks, R. R.; Smythe, L. E. Talanta 1975, 22, 495-504. (15) Berezkin, V. G.; Chernysheva, T. Y. J. Chromatogr. 1977, 141, 241-246. (16) Nederlands Observatorium van Wetenschap en Technologie; Wetenschaps- en Technologie-Indicatoren 2010; Ministry of Education, Culture and Science: The Hague, Netherlands, 2010, Supplementary Table B. (17) American Society for Mass Spectrometry; Measuring Mass: From Positive Rays to Proteins; Grayson, M. A., Ed.; Chemical Heritage Press: Philadelphia, PA, 2002. (18) van Eck, N. J. Ph.D. Thesis, Erasmus University Rotterdam, Rotterdam, Netherlands, 2011, p 13. (19) Braun, T.; Zsindely, S. Trac-Trend. Anal. Chem. 1992, 11, 307-309. (20) White, H. D.; McCain, K. W. In Annual Review of Information Science and Technology; Williams, M. E., Ed.; Elsevier: Amsterdam, Netherlands, 1989, pp 119-186. (21) Laemmli, U. K. Nature 1970, 227, 680 – 685. (22) Tanaka, K.; Waki, H.; Ido, Y.; Akita, S.; Yoshida, Y.; Matsuo, T. Rapid Commun. Mass Spectrom. 1988, 2, 151-153. (23) Arthur, C. L.; Pawliszyn, J. Anal. Chem. 1990, 62, 2145–2148. (24) Matuszewski, B. K.; Constanzer, M. L.; Chavez-Eng, C. M. Anal. Chem. 2003, 75, 30193030. (25) Maréchal, C. N.; Télouk, P.; Albarède, F. Chem. Geol. 1999, 156, 251-273. (26) Bligh, E. G.; Dyer, W. J. Can. J. Biochem. Physiol. 1959, 37, 911-917. (27) van den Dool, H.; Kratz, P. D. J. Chromatogr. 1963, 11, 463-471. (28) Guenther, A.; Hewitt, C. N.; Erickson, D.; Fall, R.; Geron, C.; Graedel, T.; Harley, P.; Klinger, L.; Lerdau, M.; Mckay, W. A.; Pierce, T.; Scholes, B.; Steinbrecher, R.; Tallamraju, R.; Taylor, J.; Zimmerman, P., J. Geophys. Res.-Atmospheres, 1995, 100, 8873–8892. (29) Weibel, D.; Wong, S;. Lockyer, N.;, Blenkinsopp, P.; Hill, R.; Vickerman, J. C. Anal. Chem. 2003, 75, 1754-1764. (30) Domon, B.; Costello, C.E. Glycoconjugate J. 1988, 5, 397-409.



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Figure and tables


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Table 1. Glossary of terms, techniques and software tools Term, Explanation technique or tool Bibliometrics The quantitative study of literatures as they are reflected in bibliographies20 Citation network Network of citation relations between items (e.g., publications, authors or journals) CiteSpaceII Software tool developed by Chen for “detecting and visualizing emerging trends and transient patterns in scientific literature”5 CitNetExplorer Software tool developed by Van Eck and Waltman “for visualizing and analyzing citation networks of scientific publications”4 Mapping Positioning of a subset of the publications in a citation network (usually selected based on their citation frequency) in a two-dimensional map in which the vertical dimension indicates time (i.e., the year of publication) and the horizontal dimension indicates the closeness of publications in the citation network. Clustering Partitioning of the publications in a citation network into a number of groups (clusters). Publications assigned to the same group are closely connected to each other in the citation network. Co-word map Map of words (or terms), usually extracted from the titles and abstracts of scientific publications, showing the co-occurrence relations of the words (i.e., the number of publications in which two words occur together). HistCite Software tool developed by Eugene Garfield to “generate chronological maps” of scientific literature based on WoS input3 Sci2 Software tool developed by a team led by Börner and Boyack that “is a modular toolset specifically designed for the study of science. It supports the temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), and macro (global) levels.” VOSviewer Software tool developed by Van Eck and Waltman “for analyzing bibliometric networks”8, in particular networks based on citation and co-occurrence relations Mapping Positioning of the items in a network in a two-dimensional map in such a way that strongly connected items tend to be located close to each other while weakly connected items tend to be located further away from each other. The horizontal and vertical axes have no special meaning. Only the relative distances between items carry meaning in a map. Clustering Partitioning of the items in a network into a number of groups (clusters). Items assigned to the same group are closely connected to each other in the network. Web of Science Multidisciplinary bibliographic database produced by Thomson Reuters (WoS)



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Figure 1. Overview of data and methods. The first data source was all articles published in Analytical Chemistry between 1929 and 2012. Titles were extracted from metadata in XMLformat and these titles were used to find the start of each article on the scanned and OCR:ed first pages of each article. By finding the start of each article the abstracts could be extracted. For 1996-2012, the abstracts were available in XML-format and extracted from these files. Titles and abstracts were used to make co-word maps using the VOSviewer and to determine the use of several techniques. The second data source was the Centre for Science and Technology Studies (CWTS) version of the Web of Science, which applies the NOWT classification to group journals into scientific fields. This source was used to determine the contribution of different scientific fields in MS research and determine its relative use in each scientific field. The third source was also the Web of Science, but the online version (Web of Knowledge), which holds the metadata of scientific articles going back to 1945. This source was used to construct a longitudinal citation network of MS research.


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Figure 2. Evolution of the field of analytical chemistry. Maps based on all texts published in Analytical Chemistry except for advertisements (1929-1995) or on all articles, letters and reviews published in Analytical Chemistry (1996-2012). The colors depict the cluster the term belongs to (cf. Table 2). The size of the circle is proportional to the number of occurrences. The distance of two terms on the map reflects the relatedness of the two terms i.e., how often they co-occur.



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Table 2. Main topics in analytical chemistry (cf. Figure 1) Color

Description

1929-1940

Color

Description

1941-1950

Color

Description

1951-1960

Color

Description

1961-1970

Green

Apparatuses

Green

Apparatuses

Cyan

Chromatography

Cyan

Chromatography

Pink

Gases

Pink

Inorganic chemistry: gases/halogens

Sea green

Electrochemistry

Red

Inorganic chemistry

Red

Inorganic chemistry

Red

Inorganic chemistry: metals

Red

Inorganic chemistry: metals

Sea green

Electrochemistry

Cyan

Industrial applications, hydrocarbons and food

Dark blue

Organic and food chemistry

Green

Apparatuses

Yellow

General/editorial and "informatics"

Yellow

General/editorial

Yellow

General/editorial

Cyan

Industrial applications and hydrocarbons

1971-1980

1981-1990

1991-2000

2001-2012

Cyan

Chromatography

Yellow

General/editorial

Cyan

Chromatography

Sea green

Detection, electrochemistry and (bio)sensors

Red

Inorganic chemistry

Sea green

Electrochemistry

Purple

Electrophoresis

Brown

Small molecules and quantitation

Sea green

Electrochemistry

Red

Inorganic chemistry

Sea green

Inorganic chemistry, electrochemistry and (bio)sensors

Blue

Mass spectrometry

Yellow

General/editorial

Cyan

Chromatography

Yellow

General/editorial

Mustard

Separations, microfluidics, and theory and simulations

Green

Apparatuses

Blue

Mass spectrometry

Blue

Mass spectrometry and proteomics

Pink

Gases

14

ACS Paragon Plus Environment

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Figure 3. Use of different techniques in Analytical Chemistry. Search terms used were “mass spectro*”, “nuclear magnetic resonance” or “NMR”, “titration”, “gas chromato*”, “liquid chromato*”, and “microfluid*”, searched against the titles of Analytical Chemistry papers.



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Figure 4. Evolution of MS within analytical chemistry based on co-word maps. Maps based on all texts with the term “mass spectro*” in the title and/or abstract published in Analytical Chemistry except for advertisements (1929-1995) or on all articles, letters and reviews with “mass spectro*” published in Analytical Chemistry (1996-2012). The colors depict the cluster the term belongs to (cf. Table 3).


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Table 3. Main topics in mass spectrometry within the field of analytical chemistry (cf. Figure 4) Color

Description

Color

1941-1960

Description

Color

1961-1970

Description

1971-1980

Yellow

General and editorial

Mustard

Software

Purple

Hydrocarbons

Green-brown

Sample preparation, separations Brown and derivatization

Compound quantification and secondary ion MS

Dark blue

Structural analysis

Purple

Hydrocarbons chemistry

Apparatuses and interfaces (incl. informatics)

Brown

Quantitation

Green

Apparatuses and interfaces

Pink

Gases

Blue

General MS

Grey

Non-discernible

Red

Inorganic chemistry, metals and isotope ratio MS

Yellow

Editorial

1981-1990

Cyan

and

organic Green

1991-2000

Sea green

Chromatography

Chemical ionization

2001-2012

Brown

Compound quantification

Dark blue

MALDI-TOF and proteomics

Cyan

Chromatography

Purple

Chromatography, quantitation Sea green and isotope ratio MS

Direct analysis (DART etc.), ESI and ICPMS

Sea green

Chemical ionization

Red

SIMS, surfaces and polymers

Quantitation (GCMS, LCMS)

Pink

FAB, FD/mass MS/MS

Greenbrown

Secondary ion mass spectrometry, laser desorption and plasma desorption

Grey

Non-discernible

analyzers

and Sea green

Blue

Brown

Electrospray ionization, Green-brown quadrupoles, ion traps, FTICR and MS/MS Dark blue

17

ACS Paragon Plus Environment

MALDI and spectrometry

imaging

mass

Sample preparation (labeling, enrichment, purification) Proteomics


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Figure 5. Use of mass spectrometry in scientific literature and by scientific discipline, 19812013. (a) Number of MS papers in WoS database. The search term used was “mass spectrometry”, which was searched for in the title and/or the abstract. (b) Share of scientific disciplines in MS research. (c) Percentage of papers using the term “mass spectrometry” in title and/or abstract, per scientific discipline. Scientific disciplines are based on NOWT medium categories, fractionally counted.


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Figure 6. Longitudinal citation network of mass spectrometry research, 1945-2013. The colors represent the cluster a publication belongs to; the colored numbers represent the cluster numbers in the table. Labels show the last name of the last author of a publication.



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for TOC only


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Bibliometric Mapping - ACS Publications - American Chemical Society

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