bibliometric mapping eight decades of analytical chemistry

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Bibliometric Mapping: Eight Decades of Analytical Chemistry, With Special Focus on the Use of Mass Spectrometry 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 dierent elds. 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 chemistsresearch. Cathelijn J. F. Waaijer* ,and Magnus Palmblad Centre for Science and Technology Studies, Faculty of Social and Behavioural Sciences, Leiden University, P.O. Box 905, 2300 AX Leiden, The Netherlands Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands * S Supporting Information B ibliometrics is the study of interrelated bodies of docu- ments, a prime example being the scientic 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 elds structure or deter- mining developments in research topics. It is the latter applica- tion that we will highlight in this Feature, by using automatic bibliometric mapping tools to map developments within ana- lytical chemistry. Compared to more traditional historical methods, automatic bibliometric mapping of scientic literature has the advantage of relative ease and low laboriousness. Furthermore, the eld struc- ture 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 dif- ferent entities, e.g., authors, journals, or key terms occurring in research papers. In addition, the edges can be based on dierent 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 rst bibliometric maps were manually constructed cita- tion networks. 1 Gareld, Sher, and Torpie studied a book on the history of genetics and compared the dependencies between dierent 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 ows. 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 networks 2 and the HistCite 3 and CitNetExplorer software tools. 4 All of these show chronological maps of the main lines of research through time, but by means of dierent methods. Although citation networks do represent an underlying structure of (elds of) scientic 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 dierent bodies of documents concerning analytical chemistry to show shifts in research topics within this eld. 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 (Figure 1). The rst 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 elds have used titles and abstracts obtained from large bibliographic databases, of which the best Published: March 6, 2015 Feature pubs.acs.org/ac © 2015 American Chemical Society 4588 DOI: 10.1021/ac5040314 Anal. Chem. 2015, 87, 4588-4596

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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.

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  • Bibliometric Mapping: Eight Decades of Analytical Chemistry, WithSpecial Focus on the Use of Mass SpectrometryIn this Feature we use automatic bibliometric mapping tools to visualize the history of analyticalchemistry from the 1920s until the present. In particular, we have focused on the application of massspectrometry in dierent elds. The analysis shows major shifts in research focus and use of massspectrometry. We conclude by discussing the application of bibliometric mapping and visualizationtools in analytical chemists research.

    Cathelijn J. F. Waaijer*, and Magnus Palmblad

    Centre for Science and Technology Studies, Faculty of Social and Behavioural Sciences, Leiden University, P.O. Box 905,2300 AX Leiden, The Netherlands

    Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands

    *S Supporting Information

    Bibliometrics is the study of interrelated bodies of docu-ments, a prime example being the scientic literature. Oneof its best known applications is the comparative evaluation ofcountries, universities, research institutes, and individualresearchers, but it may also be used for other purposes, such asgaining a better understanding of a elds structure or deter-mining developments in research topics. It is the latter applica-tion that we will highlight in this Feature, by using automaticbibliometric mapping tools to map developments within ana-lytical chemistry.Compared to more traditional historical methods, automatic

    bibliometric mapping of scientic literature has the advantage ofrelative ease and low laboriousness. Furthermore, the eld struc-ture is established by (almost) automatic methods, producing amore objective result than manual mapping could.Mapping of networks visualizes multiple items (nodes) and

    their underlying relationships (edges). The nodes can be dif-ferent entities, e.g., authors, journals, or key terms occurring inresearch papers. In addition, the edges can be based on dierenttypes of data, e.g., in a network of authors one could determinewhich authors co-author papers (a co-authorship network) orwho cites whom (a citation network).The rst bibliometric maps were manually constructed cita-

    tion networks.1 Gareld, Sher, and Torpie studied a book on thehistory of genetics and compared the dependencies betweendierent studies as described by the author to the citationalpatterns between the studies, and found that the two methods

    closely mirror each other. Hence, citation networks are able toshow structures in knowledge ows.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 astime is not explicitly shown in the visual representation of thenetworks. Exceptions include main path analysis for citationnetworks2 and the HistCite3 and CitNetExplorer software tools.4

    All of these show chronological maps of the main lines ofresearch through time, but by means of dierent methods.Although citation networks do represent an underlying

    structure of (elds of) scientic knowledge, they do not directlyrepresent the content of papers. To this end, co-word maps canbe constructed. In this approach terms are extracted from papers(e.g., from titles and abstracts) and for each pair of terms thenumber of papers in which they both occur is determined. Termsthat appear often together are likely to concern the same subjectmatter, whereas terms that never appear together are unlikely tobe related subject-wise. Counting the co-occurrences for everypair of terms yields a co-occurrence matrix of terms. Further stepsinclude the normalization of this matrix and its visualization.57

    In this Feature we use co-word and citation networks ofdierent bodies of documents concerning analytical chemistry toshow shifts in research topics within this eld. Special attentionis given to a method that became increasingly important inanalytical chemistry, mass spectrometry (MS).

    EVOLUTION OF TOPICS IN ANALYTICALCHEMISTRY 19292012

    In this study we use three sources of data as input for bibliometricanalysis (Figure 1). The rst is this journal, founded in 1929as Industrial & Engineering Chemistry Analytical Edition andrenamed to Analytical Chemistry in 1947. Most if not allscientometric studies employing mapping methods to analyzethe development of research elds have used titles and abstractsobtained from large bibliographic databases, of which the best

    Published: March 6, 2015

    Feature

    pubs.acs.org/ac

    2015 American Chemical Society 4588 DOI: 10.1021/ac5040314Anal. Chem. 2015, 87, 45884596

  • known are Web of Science (WoS), founded in 1964 by Gareldas the Science Citation Index and acquired by Thomson Reutersin 1992, and Scopus, launched in 2004 by Elsevier. However,abstracts were not regularly included before 1990. To map theevolution of research topics within analytical chemistry over along period, it was therefore impossible to use ready-to-usedatabases such as WoS or Scopus. Instead we extracted the titlesand abstracts of all Analytical Chemistry papers publishedbetween 1929 and 2013, using this journal as a proxy forthe eld of analytical chemistry.8 A detailed description of theabstract extraction procedure, and of the other methods used inthis paper, can be found in the Supporting Information.

    A glossary of the discussed terms, techniques, and softwaretools is given in Table 1.First, we constructed co-word maps of the eld of analytical

    chemistry in each decade: 19291940, 19411950, 19511960,19611970, 19711980, 19811990, 19912000, and 20012012 and visualized these in the software VOSviewer (clickon a decade to download the correspondingmap as an interactiveJava application; JavaScript 6 or higher required).9 On thesemaps, terms that occur together often are positioned closeto each other, whereas terms that co-occur less often arepositioned further apart. Furthermore, clustering of terms intofour to eight clusters with dierent colors is applied using analgorithm that nds the clustering solution that ts the co-occurrences between the dierent terms best (modularity-basedclustering).10

    The 19291940 map shows four dierent clusters:apparatuses (in green), gases (in pink), inorganic chemistry (inred), and industrial applications, hydrocarbons, and food analysis(in cyan) (Figure 2). For the other decades, the description of theclusters is given in Table 2. The maps show that inorganicchemistry (red) has been an important topic within analyticalchemistry for a long time; from 1929 until 1990 there were one ormore clusters on inorganic chemistry. In the 19912000 periodit was merged with the topics of electrochemistry and sensors.Much attention was given to (the development of) dierentapparatuses between 1929 and 1980 (green). A cluster ongeneral and editorial issues can be found in almost every period(yellow). Topics that have developed over time includeelectrochemistry, chromatography, and mass spectrometry.Electrochemistry shows up as its own cluster in the 19511960 period (sea green), but terms relating to the subject can alsobe found in the inorganic chemistry and metals cluster from1941. This suggests the topic of electrochemistry has developedfrom inorganic chemistry and metals to form its own subeld.Chromatography is apparent in the maps from the 19511960period onward (cyan); mass spectrometry from the 19711980period. The maps suggest the widespread use of mass spec-trometry in analytical chemistry primarily developed through itscoupling to chromatography (cyan); for the 19711980 periodterms relating to mass spectrometry can be discerned in themaps, but the cluster is still dominated by chromatographictechniques and applications. However, from the 19811990period, mass spectrometry broke o and formed its own subeld(blue). Finally, from 2001 a cluster on separations and micro-uidics emerged (mustard). This cluster also contains termsrelating to theory and simulations (of such microuidic systems).Next, we analyzed the development and use of a number of

    techniques within analytical chemistry. As a proxy, we deter-mined how many articles mentioned the technique in their titlesduring the 19292012 period. It is important to note that this isonly a proxy and as we only look for the mention of techniques inthe article titles, there is bias toward novel uses and developmentof 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 (Figure 3). Of thesetechniques, only the latter was still mentioned in the titles of over5% of papers published in the 20012012 period. On the otherhand, microuidics is an example of a technology not mentionedbefore 1990 that has really taken o in this 20012012 period.A technique not mentioned to a great extent in the titles ofAnalytical Chemistry papers is nuclear magnetic resonance(NMR), despite the fact that according to historical studies, it

    Figure 1. Overview of data and methods. The rst data source was allarticles published in Analytical Chemistry between 1929 and 2012. Titleswere extracted from metadata in XML-format and these titles were usedto nd the start of each article on the scanned and OCRed rst pages ofeach article. By nding the start of each article the abstracts could beextracted. For 19962012, the abstracts were available in XML-formatand extracted from these les. Titles and abstracts were used to make co-word maps using the VOSviewer and to determine the use of severaltechniques. The second data source was the Centre for Science andTechnology Studies (CWTS) version of the Web of Science, whichapplies the NOWT classication to group journals into scientic elds.This source was used to determine the contribution of dierent scienticelds in MS research and determine the relative use of MS in eachscientic eld. The third source was also the Web of Science, but theonline version (Web of Knowledge), which holds the metadata ofscientic articles going back to 1945. This source was used to construct alongitudinal citation network of MS research.

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  • became an important physics-based analytical method in thesecond half of the 20th century.11 Presumably, chemical researchusing NMR was published in journals other than AnalyticalChemistry. As the co-word maps already suggested, the mentionof mass spectrometry increased throughout the entire period.

    Whereas between 1929 and 1940 none of the AnalyticalChemistry papers mentioned mass spectrometry in their title,the fraction of papers that did increased to 18% in the 20012012 period (Figure 3), revealing a continuous increase inrelative importance of mass spectrometry in analytical chemistry.

    Figure 2. Evolution of the eld of analytical chemistry. Maps based on all texts published in Analytical Chemistry except for advertisements (19291995)or on all articles, letters, and reviews published in Analytical Chemistry (19962012). The colors depict the cluster the term belongs to (cf. Table 2). Thesize of the circle is proportional to the number of occurrences. The distance of two terms on the map reects the relatedness of the two terms, i.e., howoften they co-occur.

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  • Our bibliometric ndings are by and large in accordance withhistorical studies on the development of analytical chemistry.Historical studies have also shown that analytical chemistry hasundergone major changes during the 20th century. The mainchange has been a shift of focus from chemistry- to physics-basedanalytical methods.11,12 Before the instrument era, analyticalchemists would rst have to separate their compound of interestfrom the sample using chemical reactions, after which they couldqualitatively identify the elements present in the compound. Therst (now called classical) methods of quantitative determi-nation involved gravimetric and volumetric analysis. In the 1940sand the early 1950s, these methods were still the most used,together with colorimetric methods.13,14 The classical methodswere still used in the late 1950s and 1960s but increasinglysupplanted by chromatography, electrophoresis, and MS.15,16

    Another development during the investigated period was thedecrease in the share of papers on inorganic and organicchemistry due to a surge in biochemical research, a ndingmirrored in our maps.

    DEVELOPMENT AND USE OF MASSSPECTROMETRY

    As mass spectrometry became an important technique for ana-lytical chemistry, we now zoom in on the use of this technique.Co-words maps of all Analytical Chemistry papers mentioningMS in their title or abstract were constructed (Figure 4). Topicsof clusters in the map are described in Table 3. The maps show ashift from analysis of smaller molecules (gases, hydrocarbons,metals) and isotope analysis to the analysis of larger and morecomplicated molecules (polymers, proteins). Main topics in the

    Table 1. Glossary of Terms, Techniques and Software Tools

    Bibliometrics The quantitative study of literatures as they are reected in bibliographies22

    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 scientic literature5

    CitNetExplorer Software tool developed by Van Eck and Waltman for visualizing and analyzing citation networks of scientic publications4

    Mapping Positioning of a subset of the publications in a citation network (usually selected based on their citation frequency) in a two-dimensional mapin which the vertical dimension indicates time (i.e., the year of publication) and the horizontal dimension indicates the closeness ofpublications 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 closelyconnected to each other in the citation network.

    Co-word map Map of words (or terms), usually extracted from the titles and abstracts of scientic publications, showing the co-occurrence relations of thewords (i.e., the number of publications in which two words occur together).

    HistCite Software tool developed by Eugene Gareld to generate chronological maps of scientic literature based on WoS input3

    Sci2 Software tool developed by a team led by Borner and Boyack that is a modular toolset specically designed for the study of science. It supportsthe temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), andmacro (global) levels.

    VOSviewer Software tool developed by Van Eck and Waltman for analyzing bibliometric networks,9 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 eachother while weakly connected items tend to be located further away from each other. The horizontal and vertical axes have no specialmeaning. 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 otherin the network.

    Web of Science (WoS) Multidisciplinary bibliographic database produced by Thomson Reuters

    Table 2. Main Topics in Analytical Chemistry (cf. Figure 2)

    Color Description Color Description Color Description Color Description

    19291940 19411950 19511960 19611970Green Apparatuses Green Apparatuses Cyan Chromatography Cyan ChromatographyPink Gases Pink Inorganic chemistry:

    gases/halogensSea 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 foodchemistry

    Green Apparatuses Yellow General/editorial andinformatics

    Yellow General/editorial Yellow General/editorialCyan Industrial applications

    and hydrocarbons

    19711980 19811990 19912000 20012012Cyan Chromatography Yellow General/editorial Cyan Chromatography Sea green Detection, electrochemistry and

    (bio)sensorsRed Inorganic chemistry Sea green Electrochemistry Purple Electrophoresis Brown Small molecules and

    quantitationSea green Electrochemistry Red Inorganic chemistry Sea green Inorganic chemistry, electrochemistry

    and (bio)sensorsBlue Mass spectrometry

    Yellow General/editorial Cyan Chromatography Yellow General/editorial Mustard Separations, microuidics, andtheory and simulations

    Green Apparatuses Blue Mass spectrometry Blue Mass spectrometry and proteomicsPink Gases

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  • 19411960 period are hydrocarbons, structural analysis,quantitation, and gases (again, click the period to download aninteractive Java application of the map). The 19611970 and

    19711980 periods are characterized by the emergence ofsoftware and by the development of apparatuses and interfaces.In addition, the 19711980 period saw the establishment ofchromatography and chemical ionization as important ancillarytechnologies. Terms relating to secondary ionmass spectrometry(SIMS) can rst be distinguished for the 19711980 period (aspart of a cluster that also includes quantitation) and also formed acluster in the 19811990 period (also including laser and plasmadesorption) and the 19912000 period (also including theanalysis of polymers). Another main topic that emerged in the19912000 period is proteomics. In this period it formed acluster with matrix-assisted laser desorption ionization (MALDI)while also being positioned close to the cluster on electrosprayionization, quadrupoles, ion traps, Fourier transform ioncyclotron resonance (FTICR), and tandem mass spectrometry(MS/MS) but became its own cluster in the 20012012 period.MALDI then formed a cluster with imaging mass spectrometry.The main nding from these co-word maps is again the shift

    from the analysis of simple to more complex molecules, as was

    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/orabstract published in Analytical Chemistry except for advertisements (19291995) or on all articles, letters, and reviews with mass spectro* publishedin Analytical Chemistry (19962012). The colors depict the cluster the term belongs to (cf. Table 3).

    Figure 3. Use of dierent techniques in Analytical Chemistry. Searchterms used were mass spectro*, nuclear magnetic resonance orNMR, titration, gas chromato*, liquid chromato*, and micro-uid*, searched against the titles of Analytical Chemistry papers.

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  • the case for analytical chemistry generally. The maps also providethe likely explanation that enabled this shift: the improve-ment of equipment, interfaces and software, and especially thedevelopment of new physical techniques. Examples are thedevelopment of chemical ionization and SIMS in the 1970s, andMALDI in the 1990s. In addition, joining of older techniques andMS is evident from these maps, such as the incorporation ofchromatography in the 1970s. These developments in turnenabled the applications ofMS in a wider area of research, such asproteomics (enabled by several ionization techniques) andpolymer analysis (enabled by SIMS), which is also visible in themaps.Next, we set out to estimate how often MS was used over time

    in all research elds, insofar as they are covered by the WoSdatabase. We determined how many articles in the WoS had theterm mass spectrometry in their title or abstract. The plottedgraph of this absolute number shows a large increase from 1981until 2013 (Figure 5a, pink plot). The graph is discontinuousbetween 1990 and 1991 due to abstracts being regularly includedinto the WoS database only from 1991, including for journalspublishing many papers using MS, such as Journal of BiologicalChemistry, Journal of Chromatography, and Rapid Communica-tions in Mass Spectrometry. After the 1991 discontinuity, theincrease in use of MS is still considerable. In 1991, about 2 600papers on or including MS were published; by 2013, this gurehad increased to around 16 000. However, the WoS databaseexpanded tremendously between 1981 and 2013, due to anincreasing number of journals being covered and each journal onaverage publishing more articles per year (Figure S-1 in theSupporting Information). Therefore, we also determined therelative number of articles with MS in their titles or abstracts.This analysis shows that MS was indeed increasingly used inrelative terms (Figure 5a, yellow).This raises the question which scientic disciplines work on

    (and with) MS. We determined which disciplines mainly con-tribute to research involving MS. To this end we used theNOWT classication of journals, which classies journals

    according to scientic disciplines (see Table S-1 in theSupporting Information for a complete list).17 This reveals thatmost research using MS has been published in journals from thechemistry, physics, and astronomy category (Figure 5b).18

    However, the emphasis of these disciplines has decreasedover time as the use of MS in the life sciences increaseddramatically between 1991 and 2013. Furthermore, therehas been a slight decrease in the share of papers published inengineering journals.This analysis, however, only measures the share of scientic

    disciplines in the total output of research using MS. Therefore,we also determined to what extent MS was used per scienticdiscipline. Results of the latter analysis show that the use of MS inthe chemistry, physics, and astronomy category has still beenincreasing over the past 15 years, albeit slowly (Figure 5c). Incomparison, there has been a large increase in the use of MSin the life sciences, from about 0.75% of all papers in life sciencesjournals to over 2.5%. The use of MS in the medical sciences hasalso increased, as it has in earth and environmental sciences, butin the latter, the rate of growth has decreased. As mentionedabove, it is important to keep in mind that text mining from titlesand abstracts is more likely to pick up new applications or noveltechnologies rather than routine, established use, where theymayonly appear in the methods section.Finally, we investigated which lines of research have been the

    most important in research employing MS and which papershave been most inuential. To this end, we visualized alongitudinal citation network using CitNetExplorer.4 All articles,reviews, and letters with the term mass spectrometry in thetitle, abstract, or listed keywords published in the online versionof the WoS, along with their cited references, were included intothe analysis. The inclusion of cited references makes it possible toalso include scientic work that is not included in the WoS, suchas textbooks, older articles, and articles not employing MS butcited by mass spectrometrists, in the citation network. For ashort explanation on mapping and clustering, see the glossary(Table 1).

    Table 3. Main Topics in Mass Spectrometry within the Field of Analytical Chemistry (cf. Figure 4)

    Color Description Color Description Color Description

    19411960 19611970 19711980Yellow General and editorial Mustard Software Cyan ChromatographyPurple Hydrocarbons Green-brown Sample preparation, separations and

    derivatizationBrown Compound quantication and

    secondary ion MSDark blue Structural analysis Purple Hydrocarbons and organic chemistry Green Apparatuses and interfaces

    (incl. informatics)Brown Quantitation Green Apparatuses and interfaces Sea green Chemical ionizationPink Gases Blue General MSGray Nondiscernible Red Inorganic chemistry, metals and isotope ratio MS

    Yellow Editorial

    19811990 19912000 20012012Brown Compound quantication Dark blue MALDI-TOF and proteomics Blue MALDI and imaging mass

    spectrometryCyan Chromatography Purple Chromatography, quantitation and isotope ratio

    MSSea green Direct analysis (DART etc.),

    ESI and ICPMSSea green Chemical ionization Red SIMS, surfaces and polymers Brown Quantitation (GCMS,

    LCMS)Pink FAB, FD/mass analyzers and

    MS/MSSea green Electrospray ionization, quadrupoles, ion traps,

    FTICR and MS/MSGreen-brown Sample preparation (labeling,

    enrichment, purication)Green-brown Secondary ion mass

    spectrometry, laser desorptionand plasma desorption

    Dark blue Proteomics

    Gray Nondiscernible

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  • The citation network displays 10 clusters, of which the largestis on peptides and proteins (Figure 6). The network showsa tightly knit group of clusters on peptides and proteins (darkblue) and metabolites (orange) and part of a cluster on technicalaspects of MS (dark green). Clusters of research on carbo-hydrates (red) and lipids, plants and neonatal metabolism(brown) are also quite closely related to this group. The foodanalysis cluster (pink) is connected both to the aforementionedmetabolite cluster and to a cluster on environmental research(purple). Further right on this map is part of the cluster onsurfaces and polymers (bright green); the other part on imagingmass spectrometry is mapped more closely to the biologicalgroup. Finally, there are discrete clusters on atmospheric andgeological science (cyan) and isotopes (yellow).In addition we determined which research paper per cluster

    is the most cited (excluding reviews and book chapters). Thisanalysis attempts to nd the main papers inuencing applicationsof mass spectrometry in dierent elds. It turns out that fora considerable number of clusters, the most cited research paperis not one specically on MS, e.g., Laemmlis paper on proteinquantitation, Arthur and Pawliszyns on the solid phasemicroextraction of organochlorides, Bligh and Dyers on lipid

    extraction, Van den Dool and Kratzs on gas chromatography,and Guenther et al.s on gas emission (Figure 5b), againillustrating MS is frequently combined with other methods.In conclusion, analysis of the scientic papers in which MS is

    mentioned reveals a shift from development of the technology bythe physics and chemistry communities to application in the life,medical, and earth and environmental sciences. This shift isevident from a slight increase in the use, or further development,of MS within physics and chemistry but a much larger increasein the more applied elds. The most cited papers in researchapplying MS are often not concerned with MS, but withallied technologies, illustrating the interdisciplinary natureof much of the research using MS. The results from thisbibliometric analysis trace the common historical narrative ofmass spectrometry, e.g., as described in Graysons MeasuringMass: From Positive Rays to Proteins.19 This book also cites thecommercialization of mass spectrometers as the main enablingfactor behind the expansion of applications of MS. Thiscommercialization was rst fueled by the oil industry and theManhattan Project but later driven by a need for pharmaceuticaland environmental analysis.

    Figure 5. Use of mass spectrometry in scientic literature and by scientic discipline, 19812013. (a) Number of MS papers in WoS database. Thesearch term used was mass spectrometry, which was searched for in the title and/or the abstract. (b) Share of scientic disciplines in MS research.(c) Percentage of papers using the term mass spectrometry in title and/or abstract, per scientic discipline. Scientic disciplines are based on NOWTmedium categories, fractionally counted.

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  • CONCLUSIONSBibliometric mapping reveals clear shifts in analytical chemistryresearch topics from inorganic and (small-molecule) organicchemistry to biochemistry and complex biomolecules. Fur-thermore, a sequence of new methods emerging and sometimesreplacing older ones is apparent in the time series analyses. It isimportant to note that our ndings are, for the most part, neithernew nor even unexpected but rather support historical researchand the results from other bibliometric methods that have beenused to investigate the development of analytical chemistry.However, we show here how these ndings can be obtained usingsemiautomatic bibliometric mapping methods to visualize theevolution of research elds in an unsupervised manner.

    BIBLIOMETRIC VISUALIZATION TOOLS FOR YOUROWN RESEARCH

    The use of bibliometric visualization tools is not limited tohistorical analyses such as the one presented here. The tools canalso be used to obtain a comprehensive view of other researchelds. This is especially useful for junior researchers who aregetting started in the eld and would like to have a rst glance atits structure. Furthermore, by charting time series new topics ofpotential interest to the researcher can be found. Co-word mapsare especially well-suited to obtain an overview of a eld, as theyshow the main terms used in that eld.However, there are limitations to co-word analysis, e.g.,

    researchers use dierent writing styles, and terminology,homonyms and synonyms all aect the co-occurrence of terms.

    In addition, mapping by denition is a simplication, whichcauses loss of information.20 For example, electrospray ionizationhas arguably been a major development in the development ofMS.21 It was rst developed for recording mass spectra of largebiomolecules by Yamashita and Fenn already in 1984. However,the terms electrospray ionization and ESI are not depictedon our 19811990 maps on MS, but only from 1991 to 2000,presumably because the number of occurrences had not reachedthe set threshold of minimum occurrences for the 19811990period. Hence, only the largest subelds are generally visible onthe map, whereas the smaller ones (that might actually hold themost exciting developments) are not. Furthermore, the divisionof terms into clusters is to some extent subject to the chosenclustering parameters, making clustering dependent on a certainsubjectivity. Still, the main structure of a research eld is easilymapped.Bibliometric visualization tools may be useful to uncover

    unexpected linkages to other elds and scientic literature aswell. Citation network visualization is useful to analyze how abody of documents is related and which other work it drawsupon. The ability to uncover linkages in the scientic literaturemay be especially of use when writing a review of the literature.CitNetExplorer is a useful tool in this regard, as it can also showreferences to articles not included in the input data.In this work we used two dierent bibliometric visualization

    tools that suited our purposes, VOSviewer and CitNetExplorer.4,9

    However, there are many other mapping tools (also freelyavailable) that have more extensive functionality for otherpurposes. Examples include CiteSpaceII and Sci2.5,6 CiteSpaceII

    Figure 6. Longitudinal citation network of mass spectrometry research, 19452013. The colors represent the cluster a publication belongs to thecolored numbers represent the cluster numbers in the table. Labels show the last name of the last author of a publication.

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  • provides a built-in database for data handling as well as geospatialanalysis features. Sci2 has functionality for geospatial analysis andnetwork analysis, such as calculation of in-degree, k-core, andcommunity detection.

    ASSOCIATED CONTENT*S Supporting InformationAdditional information as noted in text. This material is availablefree of charge via the Internet at http://pubs.acs.org.

    AUTHOR INFORMATIONCorresponding Author*Phone: +31 71 527 6072. Fax: +31 527 3911. E-mail:[email protected] authors declare no competing nancial interest.BiographiesCathelijn Waaijer works as a doctoral researcher at the Centre forScience and Technology Studies at Leiden University. She did herM.Sc. in Biomedical Sciences and did a research project on the massspectrometric analysis of cartilage tumors at the Leiden UniversityMedical Center (LUMC).

    Magnus Palmblad is Associate Professor at the LUMC Center forProteomics andMetabolomics specializing in clinical applications of andinformatics solutions for mass spectrometry based proteomics.

    ACKNOWLEDGMENTSWewould like to express our gratitude to the American ChemicalSociety for making data available for this study and technicalsupport. In particular, we would like to thank Catherine Boylan,Emma Moore, David Martinsen, and Jerey Krugman. We alsothank Rob Marissen (LUMC) and Bjorn Victor (Institute forTropical Medicine, Antwerp) for technical assistance. Finally, wewould like to thank Nees Jan van Eck and Ludo Waltman (bothCWTS) and Michael Grayson for fruitful discussions.

    REFERENCES(1) Gareld, E.; Sher, I. H.; Torpie, R. J. The Use of Citation Data inWriting the History of Science; Institute for Scientic Information Inc.:Philadelphia, PA, 1964.(2) Hummon, N. P.; Doreian, P. Soc. Networks 1989, 11, 3963.(3) Garfield, E. J. Inf. Sci. 2004, 30, 119145.(4) van Eck, N. J.; Waltman, L. J. Informetr. 2014, 8, 802823.(5) Chen, C. M. J. Am. Soc. Inf. Sci. Technol. 2006, 57, 359377.(6) Sci2 team. Science of Science (Sci2) Tool; Indiana University andSciTech 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. Technol. 2010, 61, 24052416.(8) Naturally, the use of a single journal as a representation of a eld isan oversimplication of all research in a eld. However, working with asingle journal also has clear advantages, such as the continuity of a single,uninterrupted stream of publications in a journal managed by oneeditorial board (which changes slowly over time), a nite number ofarticle types and formats and the practical possibility of accessing allarticles published over nearly a century. For 19291995, only scannedrst pages of articles processed by Optical Character Recognition(OCR) to TXT-les were available. Thus, we extracted nwords after thearticles title, with n being the average abstract length for a ve-yearperiod. Therefore, a limitation of our study is that for 19291995 wewere not able to precisely extract abstracts, and that OCR errors werepresent in the texts.(9) van Eck, N. J.; Waltman, L. Scientometrics 2010, 84, 523538.(10)Waltman, L.; van Eck, N. J.; Noyons, E. C. M. J. Informetr. 2010, 4,629635.

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    Analytical Chemistry Feature

    DOI: 10.1021/ac5040314Anal. Chem. 2015, 87, 45884596

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