Example of sampled projects/articles.
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Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3055",leadTitle:null,fullTitle:"Insecticides - Development of Safer and More Effective Technologies",title:"Insecticides",subtitle:"Development of Safer and More Effective Technologies",reviewType:"peer-reviewed",abstract:"This book contains 20 chapters about the impact, environmental fate, modes of action, efficacy, and non-target effects of insecticides. The chapters are divided into 7 parts. Part 1 covers the non-target effects of insecticides, whereas part 2 is dedicated to integrated methods for pest control, in which insecticides are an important element for diminishing the populations of insect pests. Part 3 includes chapters about the non-chemical alternatives to insecticides, such as metabolic stress and plant extracts. Insecticides and human health are the main topic of part 4, and the interactions between insecticides and environment are discussed in part 5. Part 6 includes the chapters about insecticides against pests of urban areas, forests and farm animals, whereas biotechnology and other advances in pest control are discussed in part 7.",isbn:null,printIsbn:"978-953-51-0958-7",pdfIsbn:"978-953-51-5348-1",doi:"10.5772/3356",price:159,priceEur:175,priceUsd:205,slug:"insecticides-development-of-safer-and-more-effective-technologies",numberOfPages:560,isOpenForSubmission:!1,isInWos:1,hash:"adb06e05715aa4d3ec42f707d1626158",bookSignature:"Stanislav Trdan",publishedDate:"January 30th 2013",coverURL:"https://cdn.intechopen.com/books/images_new/3055.jpg",numberOfDownloads:51305,numberOfWosCitations:156,numberOfCrossrefCitations:69,numberOfDimensionsCitations:249,hasAltmetrics:1,numberOfTotalCitations:474,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 2nd 2012",dateEndSecondStepPublish:"May 23rd 2012",dateEndThirdStepPublish:"August 19th 2012",dateEndFourthStepPublish:"September 18th 2012",dateEndFifthStepPublish:"December 1st 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"78285",title:"Prof.",name:"Stanislav",middleName:null,surname:"Trdan",slug:"stanislav-trdan",fullName:"Stanislav Trdan",profilePictureURL:"https://mts.intechopen.com/storage/users/78285/images/3405_n.jpg",biography:"Prof. Stanislav Trdan, head of the Chair of Phytomedicine, Agricultural Engineering, Crop Production, Pasture and Grassland Management (Dept. of Agronomy, Biotechnical Faculty, University of Ljubljana, Slovenia), obtained his BSc, MSc and PhD (agricultural entomology) from the University of Ljubljana. Since 2006, he has been the president of the Plant Protection Society of Slovenia; since 2008, he has been an associate professor of plant protection. He is a member of many international and national research societies. He has organised two international symposia and (co)organised four national conferences in the field of plant protection. He has attended almost 30 international and 20 national conferences, workshops and seminars. Until now, he was a leader of four national scientific projects and a member of many national and international project groups. Dr. Trdan has published more than 100 scientific papers, and he or the members of his research group have given approximately 90 presentations at symposia. He was the supervisor of four PhD theses, six MSc theses and approximately 70 undergraduate theses. He was a reviewer of more than 50 scientific papers from the field of agricultural entomology or plant protection. His fields of interest are agricultural entomology and zoology, integrated pest management, biological control and other alternatives for controlling plant pests and the efficacy of insecticides.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Ljubljana",institutionURL:null,country:{name:"Slovenia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"318",title:"Pestology",slug:"animal-biology-pestology"}],chapters:[{id:"42191",title:"Side Effects of Insecticides on Natural Enemies and Possibility of Their Integration in Plant Protection Strategies",doi:"10.5772/54199",slug:"side-effects-of-insecticides-on-natural-enemies-and-possibility-of-their-integration-in-plant-protec",totalDownloads:4935,totalCrossrefCites:16,totalDimensionsCites:31,signatures:"Nabil El-Wakeil, Nawal Gaafar, Ahmed Sallam and Christa Volkmar",downloadPdfUrl:"/chapter/pdf-download/42191",previewPdfUrl:"/chapter/pdf-preview/42191",authors:[{id:"80199",title:"Prof.",name:"Christa",surname:"Volkmar",slug:"christa-volkmar",fullName:"Christa Volkmar"},{id:"82718",title:"Dr.",name:"Nabil",surname:"El-Wakeil",slug:"nabil-el-wakeil",fullName:"Nabil El-Wakeil"},{id:"83353",title:"Dr.",name:"Ahmed",surname:"Sallam",slug:"ahmed-sallam",fullName:"Ahmed Sallam"},{id:"83363",title:"Dr.",name:"Nawal",surname:"Gaafar",slug:"nawal-gaafar",fullName:"Nawal Gaafar"}],corrections:null},{id:"42210",title:"Pesticide-Residue Relationship and Its Adverse Effects on Occupational Workers",doi:"10.5772/54338",slug:"pesticide-residue-relationship-and-its-adverse-effects-on-occupational-workers",totalDownloads:2918,totalCrossrefCites:3,totalDimensionsCites:6,signatures:"Nabil El-Wakeil, Shehata Shalaby, Gehan Abdou and Ahmed Sallam",downloadPdfUrl:"/chapter/pdf-download/42210",previewPdfUrl:"/chapter/pdf-preview/42210",authors:[{id:"82718",title:"Dr.",name:"Nabil",surname:"El-Wakeil",slug:"nabil-el-wakeil",fullName:"Nabil El-Wakeil"},{id:"83353",title:"Dr.",name:"Ahmed",surname:"Sallam",slug:"ahmed-sallam",fullName:"Ahmed Sallam"},{id:"161048",title:"Dr.",name:"Shehata",surname:"Shalaby",slug:"shehata-shalaby",fullName:"Shehata Shalaby"},{id:"161050",title:"Dr.",name:"Gehan",surname:"Abdou",slug:"gehan-abdou",fullName:"Gehan Abdou"}],corrections:null},{id:"42214",title:"Predicting the Effects of Insecticide Mixtures on Non-Target Aquatic Communities",doi:"10.5772/53356",slug:"predicting-the-effects-of-insecticide-mixtures-on-non-target-aquatic-communities",totalDownloads:1627,totalCrossrefCites:0,totalDimensionsCites:2,signatures:"Alexa C. 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by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"61391",title:"Mapping Science Based on Research Content Similarity",doi:"10.5772/intechopen.77067",slug:"mapping-science-based-on-research-content-similarity",body:'In 1965, Price [1] proposed studying science using scientific methods. Since then, research in scientometrics has developed techniques for analyzing research activities and for measuring their relationships and constructed maps of science, one of the major topics in scientometrics, that provides a bird’s eye view of the scientific landscape. Maps of science have been useful tools for understanding the structure of science, their spread, and interconnection of disciplines. By knowing such information, science, and technology enterprises can anticipate changes, especially those initiated in their immediate vicinity. Research laboratories and universities that are organized according to the established standards of disciplinary departments can understand an organization’s environment. Furthermore, such maps are important to policy analysts and funding agencies. Since research funding should be based on quantitative and qualitative scientific metrics, they usually perform several analyses on the map with statistical analysis and careful examination by human experts. However, conventional approaches to understanding research activities focus on what authors told us about past accomplishments through inter-citation and co-citation analysis of published research articles. Thus, ongoing project and the recently published articles that do not have enough citations have not been analyzed.
Therefore, we propose to analyze them using a content-based method using natural language processing (NLP) techniques. Recently, word/paragraph embedding has been proposed for finding relationships between unstructured descriptions. Such embedding techniques represent words and paragraphs as real-valued vectors of several hundred dimensions. The distances between the descriptions are calculated from the similarities between vectors. Thus, we constructed a new mapping tool that represents the recent scientific trends, where nodes represent research projects or the articles that are linked by certain distances of the content similarity. Moreover, we drew a map from approx. 300,000 IEEE articles and National Science Foundation (NSF) projects, and then from its chronological changes we obtained some findings regarding the formation processes of research areas.
The remainder of this chapter is organized as follows. In Section 2 discusses related work, and Section 3 describes our proposed method for calculating the content similarity and its evaluations. Then, Section 4 introduces our tool, Mapping Science, and we confirm on the map the formation process of research areas such as the Internet of Things in Section 5, final conclusions and suggestions for future work are provided in Section 6.
Maps of Science (
Funding agencies and publishers generally have their own classification systems. Projects/articles have more than one code; thus, interdisciplinary projects can be found by searching multi-labeled projects. However, even if two projects/articles are assigned the same category, their similarity may not be found. Moreover, funding agencies and publishers use different categories, and there is no comprehensive scheme for characterizing projects or articles; thus, they cannot be compared between different agencies or publishers. For example, comparing articles with Association for Computing Machinery classification (
Therefore, several content-based methods are proposed in the related literature. Previous studies have examined automatic topic classification using probabilistic latent semantic analysis (pLSA) [4] and latent Dirichlet allocation (LDA) [5]. One uses LDA to find the five most probable words for a topic, and each document is viewed as a mixture of topics [6]. This approach can classify documents across different agencies and publishers. However, the similarity between projects/articles cannot be computed directly. In this regard, the National Institutes of Health (NIH) Visual Browser [7, 8] (
By contrast, a word/paragraph vector, which is a distributed representation of words and paragraphs, is attracting attention in NLP. Assuming that context determines the meaning of a word [10], words appearing in similar contexts are considered to have a similar meaning. In the basic form, a word vector is represented as a matrix, whose elements are the co-occurrence frequencies between a word w with a certain usage frequency in the corpus and words within a fixed window size c from w. A popular representation of word vectors is word2vec [11, 12]. Word2vec creates word vectors using a two-layered neural network obtained by a skip-gram model with negative sampling. Specifically, word vectors are obtained by calculating the maximum likelihood of objective function L in Eq. (1), where T is the number of words with a certain usage frequency in the corpus. Word2vec clusters words with similar meanings in a vector space.
In addition, Le and Mikolov [13] proposed a paragraph vector that learns fixed-length feature representations using a two-layered neural network from variable-length pieces of texts such as sentences, paragraphs, and documents. A paragraph vector is considered another word in a paragraph and is shared across all contexts generated from the same paragraph but not across paragraphs. The contexts are fixed length and sampled from a sliding window over the paragraph. The paragraph vectors are computed by fixing the word vectors and training the new paragraph vector until convergence, as shown in Eq. (2).
where di is a vector for a paragraph i that includes wt. Whereas word vectors are shared across paragraphs, paragraph vectors are unique among paragraphs and represent the topics of the paragraphs. By considering word order, paragraph vectors also address the weaknesses of bag-of-words models in LDA and pLSA. Therefore, paragraph vectors are considered more accurate representations of the context of the content. We can then input resulting vectors into the analysis using machine learning and clustering techniques for finding similar articles in different academic subjects as well as the relationships between projects from different agencies. Thus, we tried to convert the natural sentences in project descriptions and article abstracts to paragraph vectors in this study.
This section introduces our proposed paragraph embedding method using entropy and then evaluates whether the similarity of the resulting vectors accurately represents the content similarity of documents.
Before introducing the proposed method, we present a problem in applying the paragraph vectors for research project descriptions. We implemented the paragraph embedding technique using the Deep Learning Library for Java (
However, the result showed that projects are scattered and not clustered by any subject or discipline in the vector space. Most projects are slightly connected to a low number of projects. Thus, it is difficult to grasp trends and compare an ordinary classification system. Closely observing the vector space reveals some of the reasons for this unclustered problem: each word with nearly the same meaning has slightly different word vectors, and shared but unimportant words are considered the commonality of paragraphs. In fact, Le and Mikolov reported classification accuracy with multiple categories of less than 50% [13].
Therefore, for addressing this problem, we introduce the information entropy [14] for clustering word vectors before constructing paragraph vectors. The fact that synonyms tend to gather in a word vector space indicates that the semantics of a word spatially spread to a certain distance. This observation is also suggested in the related literature [15]. Therefore, to unify word vectors of almost the same meanings, excluding trivial common words, we generated clusters of the word vectors based on the semantic diversity of each concept in a thesaurus. We first extracts 19,685 hypernyms (broader terms) with one or more hyponym (narrower term) from the Japan Science and Technology Agency (JST) science and technology thesaurus [16]. The JST thesaurus primarily consists of keywords that have been frequently indexed in 36 million articles accumulated by the JST since 1975. Currently, this thesaurus is updated every year and includes 276,179 terms with English and Japanese notations in 14 categories from bioscience to computer science and civil engineering. Based on the World Wide Web Consortium (W3C) Simple Knowledge Organization System (SKOS), the JST thesaurus also exists in W3C Resource Description Framework (RDF,
Construction of paragraph vectors based on cluster vectors.
Hereafter, the “word” is a word in the dataset, the “term” is a term in a thesaurus, and terms are classified into hypernyms, hyponyms, and their synonyms. The “concept” is defined as a combination of a hypernym and one or more hyponyms one level below the hypernym indicated as a red box in Figure 2. Given that a thesaurus consists of terms Ti, we calculated the entropy of a concept C by considering the appearance frequencies of a hypernym T0 and its hyponyms T1…Tn as an event probability. The frequencies of synonyms Si0…Sim of term Ti was summarized to a corresponding concept (synonyms Sij include descriptors of terms Ti themselves).
Concepts in a thesaurus.
In Eq. (3), p(Sij|C) is the probability of a synonym Sij given a concept and terms Ti. For each concept in the thesaurus, we calculated the entropy H(C) in the dataset. As the probabilities of events become equal, H(C) increases. If only particular events occur, H(C) is reduced because of low informativeness. Thus, the proposed entropy of a concept increases when a hypernym and hyponyms that construct a concept separately appear with a certain frequency in the dataset. Therefore, the degree of entropy indicates the semantic diversity of a concept. Then, assuming that the degree of entropy and the spatial size of a concept in a word vector space are proportional to a certain extent, we split the word vector space into clusters. In fact, our preliminary experiment indicated that the entropy of a concept has high correlation R = 0.602 with the maximum Euclidean distance of hyponyms in the concept in a vector space, at least while the entropy is rather high. Specifically, we refined clusters by repeatedly subdividing them until the defined criterion was satisfied. In our method, we set the determination condition as shown in Eq. (4).
This condition represents that the word vectors w0…wT are subdivided into two clusters proportionally to the ratio of the highest two concept entropies H(C(wi)) and H(C(wj)), which are selected from all entropies of concepts in a cluster (an initial cluster is the whole vector space). C(wi) and C(wj) mean concepts C to which words wi and wj belong, respectively. The words wi and wj are words, whose lemmatized forms are identical to terms or synonyms in the thesaurus. However, note that the entropies of the other words whose correspondences are not included in the thesaurus are not calculated in Eq. (3). Cl(w) means a cluster to which a vector of a word w should be classified.
The vector space is subdivided until the entropy becomes lower than 0.25 (the top 1.5% of entropies) or the number of elements in a cluster is lower than 10. These parameters were also determined empirically through the experiments. After generating 1260 clusters from 66,830-word vectors, we considered the centroid of all vectors in a cluster as a cluster vector. Then, we constructed paragraph vectors using the cluster vectors rather than word vectors, as shown in Eq. (5) that is an extension of Eq. (2). After all, each cluster vector represents a concept that has the highest entropy in all concepts included in the cluster.
Next, we evaluate the resulting vectors on the map constructed from the following dataset. In this article, the dataset includes titles and abstracts of 266,772 IEEE conference articles published from 2012 to 2016, including 2,290,743 sentences in total and titles and descriptions of 34,192 NSF projects from 2012 to 2016, including 730,563 sentences in total. Note that IEEE journal, transaction, symposium, and workshop articles are not included, and NSF project domains are limited to Computer and Information Science and Engineering, Mathematical and Physical Sciences, and Engineering in accordance with IEEE articles. All words in the sentences were tokenized and lemmatized by Stanford CoreNLP before creating the vector space.
In terms of the unclustered problem, we confirmed that the proposed method successfully formed several clusters compared with the original paragraph embedding method. For a quantitative comparison, in Figure 3 shows the relationships between the cosine similarities and the number of edges, and the relationship between the degree centrality and the number of nodes (i.e., projects) in the case of the cosine similarities of >0.35. As a result, we confirmed that edges with a higher cosine similarity and nodes with higher degrees increase. The reason for this result is because, through the use of high-entropy concepts, which are significant in scientific and technological contexts excluding scientifically unimportant words—as elements between paragraph vectors, the paragraph vectors were able to comprise meaningful groups. Simultaneously, newly, unknown synonyms, and closely related words that are not defined in the thesaurus can be unified to a cluster vector, if they are in the same cluster. Taking the centroid vector as a representative vector in a cluster involves separating each cluster vector as much as possible to form a clear difference in the vector space.
Comparison between paragraph vectors and those with entropy clustering.
In terms of the accuracy of content similarities, the evaluation encounters difficulty since, to the best of our knowledge, there is no gold standard for evaluating the similarity among scientific and technological documents. Therefore, we first evaluated the degree of the similarities based on a sampling method. We randomly extracted 100 pairs of projects with a cosine similarity of >0.5 (similarities less than 0.5 are not considered in the map layout), to make the distribution similar to the entire distribution. Each pair has two project titles and descriptions, and a cosine value that is divided into three levels: weak (0.5 ≤ cos. < 0.67), middle (0.67 ≤ cos. < 0.84), and strong (0.84 ≤ cos.). Some examples of two projects and their cosine value are shown in Table 1. Then, three members of our organization, a funding agency in Japan, evaluated the similarity of each pair. The members were provided the prior explanations for the intended use of the map and some examples of evaluation. The members received the same data, and their backgrounds are bioscience, psychology, and computer science. As a result, we confirmed that 78% of the similarities matched majority votes of the members’ opinions. Examples misjudged include, for example, the not related pairs of two projects that have the same acronyms with different meanings, and the stronger pairs of two projects that have only a few common words, but which are recent technologies attracting attention. We expect that those words will eventually have higher entropies and then the project similarities will be estimated to be stronger. We also plan to replace acronyms in project descriptions with full words before making vectors. By contrast, the accuracy of the similarities of the original paragraph embedding method was 21%. The evaluation results were determined to be in “fair” agreement (Fleiss’ Kappa κ = 0.29) (Table 2).
Example of sampled projects/articles.
Similarity | Weak | Middle | Strong |
---|---|---|---|
Precision | 77.5 | 83.3 | 100.0 |
Recall | 98.6 | 33.3 | 83.3 |
F1 value | 86.8 | 47.6 | 90.9 |
Evaluation of similarity based on sampling (%).
Moreover, we evaluated the accuracy of content similarities using the artificial data, part of which is randomly replaced with the other projects/articles. We replaced 10, 20, …, 100% of a project description or a article abstract with sentences randomly selected from the others. Then, we measured a cosine similarity between a vector generated from the artificial project/article and a vector of the original project/article. The projects/articles were randomly selected from all projects/articles, and then we evaluated 1000 pairs of the original project/article and the artificial project/article. The relationship of the replacement ratios and the cosine similarities is shown in Figure 4. As a result, we confirmed that there is an obvious correlation between content similarities of projects/articles and their cosine similarities with R2 = 0.89. The paragraph vectors without the entropy clustering also had the same trend, but the vectors with the entropy clustering had higher similarities on average. This result matches the relationships between the cosine similarities and the number of edges shown in Figure 3.
Cosine similarities of artificial data with partial replacement.
This section describes our content-based map of science, Mapping Science [18, 19]. After introducing its interface, we describe our clustering and layout method of articles and projects in the map and analytical functions provided.
In Figure 5 shows three main views of the Mapping Science, which are a portfolio view, a clustered view, and analytic views.
Interface of Mapping Science.
In the portfolio view, five research areas, Information, Mathematics and Physics, Communication, Electronics and Mechatronics, and Power and Energy, to which the entire dataset has been divided by full-text search with predefined queries, are shown. The size of circles corresponds to the number of articles and projects in the area.
In the clustered view that opens when users click one of the areas in the portfolio view, the results of clustering all the articles and projects in the area are shown. The details of the clustering method are shown in the next section. This view is for taking a look at the technologies in the area. Each cluster has at most 10 labels, which are extracted as feature phrases using a probabilistic information retrieval method, BM25 [20].
In the analytic view that opens when users click one of the clusters in the clustered view, each node corresponds to a article or a project, and distances between the nodes are proportional to the cosine similarities between articles/projects, as much as possible. In addition, direct citation links between articles (citing → cited) are shown in light green edges with labels showing common phrases between two articles, which are also extracted by the BM25 method. When users click a node, the detailed information about the node (article or project) is shown on the map.
In all the views, the search box located at the upper-left corner provides full-text search for all articles and projects included in the current view, and the search results are highlighted in the view. Moreover, the analytic view provides the time-shift bar, which displays the cumulative changes in a cluster according to published/started years of articles/projects. The trial version of this map is publicly available at
In this section, we describe a method for generating the clustered view and the analytic view. There are too many nodes (articles and projects) even in a research area to explore a specific research topic (over 160,000 nodes included in the Information area in Table 3). We thus divided them into several hundred clusters and provided analytic functions described in the next section to explore articles and projects in each cluster.
Information | Mathematics and Physics | Communication | Electronics and Mechatronics | Power and Energy | |
---|---|---|---|---|---|
# of nodes | 165,823 | 113,982 | 99,995 | 88,023 | 89,845 |
# of clusters | 474 | 345 | 338 | 400 | 303 |
# of clusters (only by infomap) | 2313 | 1614 | 1630 | 2807 | 1776 |
# of nodes and clusters in each research area.
A major concern in clustering and laying out the nodes is to reduce 500-dimensional paragraph vectors to a 2D network structure. In general, conventional clustering or dimension reduction techniques such as multi-dimensional scaling (MDS) have O(n3) computational complexity, which increases the calculation time in proportion to that. We thus, to accommodate the practical calculation time, generated a network structure only from the edges that are the 30 highest similarities (at least, 0.5 or more) to other nodes. Sci2Tool [3] also generated the network only from the 15 highest similarities edges and successfully created an informative map of journals.
Clusters in the clustered view are calculated by info map [21], which is one of modularity-based network clustering algorithms [22]. By increasing the modularity, the nodes are divided into clusters that have more edges within the clusters than edges between the clusters. Thus, articles or projects in a cluster have relatively high similarities and form meaningful sets. However, the simple application of the info map generated too many clusters to explore the clustered view (over 2800 clusters included in Electronics & Mechatronics area in Table 3). Therefore, we merged small clusters comprised of less than 50 nodes into the nearest cluster, which has the highest similarity pair between any of two nodes in the clusters. This operation corresponds to a single linkage clustering in agglomerative clustering. As a result, the numbers of clusters are reduced as in Table 3. Although the accuracy of the clustering result falls (the modularity decreases), nodes incorporated into the nearest cluster tend to form independent sets of nodes in the analytic view and can be distinguished in the view. The distances between clusters in the clustered view mean the distances in the single linkage-clustering.
The layout algorithm in the analytic view is OpenOrd (formally, DrL) [23]. This is a well-known force-directed layout algorithm and frequently used in other maps of science such as Sci2Tool. In Figure 6 shows a comparison of layout algorithms for Internet of thing cluster (see the next section), which includes the OpenOrd (edge cut parameter: 0.88, 0.91, and 0.94), MDS with cosine dissimilarity, large graph layout (LGL) [24] and Fruchterman Reingold layout (FR) [25]. The LGL and the FR are also force-directed algorithms. We can obviously confirm several clusters in the OpenOrd, but those are not clear in the other algorithms. The number of clusters in the OpenOrd increase as the edge cut parameter increases. Thus, we empirically set the OpenOrd with the edge cut parameter: 0.91 in the analytic view by default. The other parameters were also empirically set to show the structural features as much as possible. However, as shown in the next section, the analytic view provides several other layout algorithms and parameters; thus, users can change the layout of nodes according to their needs.
Comparison of graph layout algorithms.
In addition to the functions described in Section 4.1, the Mapping Science provides the following analytical functions: (1) translation of article abstracts and project descriptions, (2) visualization of statistical information, (3) summarization of feature phrases, (4) querying and exporting using SPARQL, (5) change of layout algorithms, and (6) generation of customized analytic views.
In the analytic views, users can see the detailed information, such as titles, article abstracts/project descriptions, authors/project members, affiliations, and publication year/proposed year. In addition, the abstracts/descriptions are translated into Japanese by clicking “Translate” buttons. The users can read the original abstracts/descriptions in the same pane for confirming the translation validity.
As in Figure 7, the analytic view can visualize the summary of bibliometric information of the nodes contained in the view. There are several widgets, such as for citation (Impact Factor, SJR, and CiteScore) metrics, publications by year, citations by year, and publications by each country. Moreover, the users can select the nodes in a rectangle area and see the statistical information of the selected nodes. The upper part of the publication by country shows an article count (AC) (
Statistical information.
As in Figure 8, the feature phrases of the selected nodes can be summarized in word clouds. At most 10 feature phrases of each node are extracted based on the BM25 method in advance. Then, if the users select the multiple nodes, the feature phrases with higher frequencies are displayed larger and placed closer to the center of the word cloud. This function is useful for understanding specific themes of the selected nodes in a cluster.
Feature phrases in the selected nodes.
The background data in the Mapping Science have been converted to RDF data and stored in a graph database. Therefore, the analytic views provide a high-level search using a formal query language, SPARQL, as in Figure 9. For example, the users can search for articles, which have >0.8 similarities with articles cited 100+ times from journals with >10 impact factor (such articles might be obscure but important). When the users click a node ID in the result table, the node is highlighted and the viewpoint is automatically moved to the node. Moreover, the users can store their own SPARQL queries as macros. Therefore, users who are not familiar with SPARQL can simply call the macros and obtain the query results.
SPARQL search widget.
In addition, since we received requests for downloading the information displayed on the map, the information of the selected nodes and all nodes in a cluster can be exported in comma-separated values (CSV) format. The result of SPARQL queries can be also exported in CSV format.
As described in the previous section, the layout of the analytic view was calculated by the OpenOrd (edge-cutting value: 0.91). In addition to that, the analytic views can be redrawn by the OpenOrd (edge-cutting value: 0.94 or 0.88), LGL, Fruchterman-Raingold, or Kamada-Kawai [26]. When the users select a layout, the layout algorithm is executed in the background, the resulting layout information is stored and the view is redrawn. If the layout information is stored in advance, the layout is redrawn immediately. The layout calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes.
The analytic views were composed by the info map algorithm, but the users can create the customized Analytic views by keyword search. When the users enter keywords into the widget in the portfolio view, the nodes are extracted by the full-text search for all nodes in five research areas, and then the layout is calculated by the OpenOrd based on the cosine similarities of the extracted nodes. For example, an analytic view for studies related to neural networks and artificial intelligence across multiple research areas can be created by keywords such as “Artificial Intelligence [AND] Neural Network.” This function could help find interdisciplinary studies. The calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes. The information on the customized analytic views is stored in the background; the same view is immediately displayed for the second time. The customized analytic view can provide the same analytical functions, such as keyword search, visualization of statistical information, visualization of the cumulative changes by year, and layout change.
In this map, we try to understand the formation processes of several research areas through chronological changes of network structure. This section describes two cases for the Internet of Things (IoT) and Brain-Computer Interface (BCI).
In Figure 10 shows the analytic views for an IoT area from 2012 to 2016, which includes 574 nodes as of 2016. The last view is the analytic view in 2016 displaying >0.6 cosine similarities as edges.
Formation of IoT areas.
In 2012, four islands (places, at which nodes are densely located) mainly for IoT frameworks and networks and for IoT system and security are barely found (labels of each island have been extracted by the summarization function of feature phrases).
In 2013, a funding project (orange node) was firstly established in the security, and then the corresponding island grew bigger, that is, the number of articles increased, although a causal relationship is unclear.
Then, in 2014, the island of the IoT frameworks and networks also had a funding project and grew bigger. At the same time, researchers of each island, which seem to correspond to the different research community, started to recognize with each other, and thus mutual citation links (light green edges) between islands began to be drawn.
In 2015 and 2016, this movement was accelerated; thus, we can confirm that the islands were getting bigger and denser, and mutual citation links increased. Moreover, the other islands than the first four islands, for example, an island for IoT services and semantics at the upper-left corner also gradually grew, and some of them are greatly increasing the articles by getting funding projects.
Finally, the edges of the cosine similarity 0.6 in the last view mean relatively weak similarity described in Section 3.2. In contrast, nodes which compose an island are mutually connected with stronger similarities, although they are too dense to confirm in the figure. Therefore, in this IoT area, there are several research communities dedicated to specific research themes, and they are mutually connected with their content similarity and citation relations. Thus, we can understand that they are developing each theme while forming the IoT area as a whole.
We confirmed several other processes of research area formation in our case studies. For example, in Figure 11 shows the analytic view for BCI in 2016. In this figure, an island at the top is growing while citing articles for several specific research themes, such as medical applications, brain waves, pattern recognition, and steady state visual evoked potentials (SSVEP). Thus, we can understand that the BCI has been simultaneously approached from several different conventional research themes, and is integrating them. In this manner, we confirmed that the formation processes of research areas can be captured by closely observing the map.
Formation of BCI.
In this study, we developed a map of science, Mapping Science based on the research content similarity for funding project descriptions and recently published articles, which have difficulty in applying the citation analysis. After improving the existing paragraph embedding technique with an entropy-based clustering method of word vectors, we confirmed the good face validity. Then, we introduced the map constructed from approx. 300 k IEEE articles and NSF projects from 2012 to 2016 with the clustering and layout method of articles/projects and analytic functions provided on the map. Finally, we confirmed that formation processes of some specific research areas can be captured as changes of network structure.
As the next step, we plan to have a comparison with citation-based methods on concrete scenarios and incorporate patent information on the map. In addition, by overlaying domestic funding projects with NSF and Horizon2020 through the JST thesaurus that has English and Japanese notations, we will identify the trend of public grants. Finally, we try to extract metrics from chronological changes of the network structure of research areas. Foresight and understand from scientific exposition (FUSE) program in Intelligence advanced research projects activity (IAPRA) already conducted a study for identifying emerging research area based on several metrics obtained from several maps of science from 2011 to 2015. We, JST, will also utilize such metrics in statistical analysis and machine learning techniques to detect emerging research areas in their early stage for the next science and technology policies.
Pectin is the major constituent of all plants and makes up approximately two-third of the dry mass of plant primary cell walls. It provides structural integrity, strength, and flexibility to the cell wall and acts as barrier to the external environment [1]. Pectin is also a natural component of all omnivorous diet and is an important source of dietary fiber. Due to the resistant in digestive system and lack of pectin digestive enzymes, human beings are not able to digest pectin directly but microorganism present in large intestine can easily assimilate the pectin and convert it into soluble fibers. These oligosaccharides promote beneficial microbiota in gut and also help in lipid and fat metabolism, glycemic regulation, etc. [2]. Being complex and highly diverse in structure, role of pectin is not only limited to the biological and physiological functions, but it has tremendous potential and contributes substantially in other applications ranging from food processing to pharmaceuticals. Pectin is a water-soluble fiber and used in various food as emulsifier, stabilizer, gelling, and thickening agent.
\nCommercial pectins are extracted from citrus and apple fruit. On the basis of dry mass, apple pomace contains 10–15% pectin, whereas citrus peel possesses 20–30% pectin. However, pectin has also been extracted in higher amount from several other fruits and their by-products, such as sunflower head, mango peal, soybean hull [3], passion fruit peel [4], sugar beet pulp [5], Akebia trifoliata peel [6], peach pomace [7], banana peel [8], chickpea husk [9], and many more [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]. Table 1 summarizes the different types of pectin extracted from various horticultural crops. But detection and extraction of pectin in higher concentration is not sufficient to qualify that fruit as a source of commercial pectin because of the structural variation and modification in side-chain sugars, and also that pectin from different sources has different gelling properties.
\nS. No | \nSource | \nParts used | \nExtraction method used | \nPectin yield (%) | \nType of pectin (HMP/LMP) | \nRef | \n
---|---|---|---|---|---|---|
1 | \nPassion fruit | \nPeel | \nAPP | \n14.8% | \nHMP | \n[4] | \n
2 | \nBanana | \nPeel | \nAPP | \n5–21% | \nHMP (DE, 50–80%) | \n[8] | \n
3 | \nChick pea | \nHusk | \nAcid extraction, APP, and freeze dried | \n8% | \nLMP (DE, 10%) | \n[9] | \n
4 | \nKrueo Ma Noy | \nLeaves | \nAPP, DPP | \n21–28% | \nLMP (DE, 34–42%) | \n[11] | \n
5 | \nYellow Passion | \nFruit rind | \nAPP, DPP, MPP | \n3–16% | \nHMP (DE, 54–59%) | \n[12] | \n
6 | \nDurian | \nRind | \nAPP | \n2–10.25% | \nHMP (DE, 50–64%) | \n[13] | \n
7 | \nMulberry | \nMulberry bark with epidermis (MBE) and without epidermis (MB) | \nExtracted using 60–100% isopropanol | \n11.88% | \nHMP (MB–DE, 71.13%); LMP (MBE–DE, 24.27%) | \n[14] | \n
8 | \nYuzu, citrus family | \nPomace | \nExtracted with APP and enzyme (Viscozyme® L with 1.2 × 10−4 fungal β-glucanase | \nDPP, APP (7.3–8%) | \nLMP (APP–DE, 41%; DPP–DE, 46.3%) | \n[16] | \n
9 | \nCacao pods | \nHusk | \nExtracted with 1 N HNO3 at different pH and precipitated by ethanol and acetone | \n3.7–8.6% | \nLMP (DE 36.7% @ pH 1, DE 44.3% @ pH 3); HMP (DE 52.4% @ pH 2) | \n[17] | \n
10 | \nCashew apple | \nPomace | \nAOP at different pH (1.0, 1.5, and 2.0) | \n10.7–25.3% | \nLMP (DE, 28–46%) | \n[18] | \n
11 | \nCyclea barbata Miers (CBM) | \nLeaves | \nExtracted with acid and alkali, precipitated the pectin by ethanol | \n4–8% | \nHMP (acid treated: 65–75% DE) LMP (Alkali treated: 36% DE) | \n[19] | \n
12 | \nDragon fruit | \nPeel | \nExtracted using HCl, precipitated and purified with 70 and 99.6% isopropanol. | \n18.59% | \nLMP (DE, 46.95%) | \n[20] | \n
13 | \nJackfruit | \nPeel | \nUltrasonic-microwave-assisted extracted (UMAE) pectin | \n21.5% | \nHMP (DE, 62.5%) | \n[22] | \n
14 | \nPotato | \nPulp | \nExtracted with different acids and precipitated by ethanol | \n4.08–14.34% | \nLMP (DE, 21.51–37.45%) | \n[23] | \n
High methoxyl pectins (HMP) and low methoxyl pectins (LMP) from various horticultural crops.
APP, alcohol-precipitated pectin; MPP, metal ion-precipitated pectin; DPP, dialyzed precipitated pectin.
Pectin is a highly complex plant cell wall polysaccharide that plays a significant role in plant growth and development. It is predominantly present in fruits and vegetables and constitutes approximately 35–40% of the primary cell wall in all the dicot plants [24]. The composition and structure of pectin is influenced by the developmental stages of plants [25, 26]. Structural analysis of pectin revealed that it is a polymer comprised of chain-like configuration of approximately 100–1000 saccharide units; therefore, it does not possess a defined structure. In general, pectin is illustrated as a heteropolysaccharide of three components namely, homogalacturonan (HG), rhamnogalacturonan-I (RGI), and rhamnogalacturonan-II (RGII) [28, 29]. The Backbone structure may branch with other neutral sugar chains such as arabinan, xylogalacturonan (XGA), arabinogalactan I (AG-I), and arabinogalactan II (AG-II).
\nHomogalacturonan (HG) is a polymer of galacturonic acid (GalA), in which Gal A residues are linked together by α-1-4 glycosidic bond and the number of GalA residues in HG may vary from 72 to 100% depending on the source of pectin [30]. For instance, the HG backbone of cashew apple pectin, C. maxima pectin, sunflower pectin, citrus pectin, comprises of 69.9–85%, 71–75%, 77–85%, 80–95%, GalA residues, respectively. Amaranth pectin contains more than 80% GalA residues in HG backbone structure. Furthermore, it was also observed that HG may be methoxy-esterified at C-6 and/or O-acetylated at the O-2 and/or O-3. Some exception has also been reported in the detailed structural analysis of HG region of pectin such as C-3 substitution of the galacturonic acids of HG with xylose in pea, apple, carrot, duck weed, etc. [31], and C-2 or C-3 with apiose in duck weeds (Lemna minor) [32]. HG is susceptible to both mechanical and enzymatic deesterification and degradation.
\nRhamnogalacturonan I represents approximately 20–35% of the pectin polysaccharides. It is the highly branched and heterogeneous polysaccharide which is characterized as repeating units of α-(1 → 2)-linked rhamnose and α-(1 → 4)-linked GalA residues. It can be O-acetylated at O-2 and/or O-3 positions of GalA residues [33, 34]. Pectin from citrus peels, mung bean, kidney bean, apple fruit, and flax hypocotyls has been reported 100% methyl esterified in the RGI region [35, 36]. The composition of RGI varies in pectin extracted from different sources. In sugar beet pectin, 80 repeating units of [→2] –α-L-Rha-(1–4)- α-D-GalA-(1→) comprised the backbone of rhamnogalacturonan I (RG-I), whereas citrus pectin contains only 15–40 repeating units [37]. The polymeric side chains of galactans and arabinans are substituted at the O-4 position of RG-I backbone. Arabinogalactan I (AG-I) and arabinogalactan II (AG-II) are also reported to be present as polymeric side chains [38, 39, 40]. The side chains are often referred to as “hairs” and believed to play an important role in pectin functionality. The loss of side chains may increase the solubility of the pectin [41]. PGI is prone to enzymatic depolymerization. However, protease and acid-catalyzed cleavage of RGI has also been reported [28, 42, 43].
\nThe highly conserved polysaccharide of pectin is rhamnogalacturonan II which constitutes about 10% of the pectin polymer [44]. This polysaccharide is made up of (1 → 4)-linked-α-D-GalA units containing 12 monosaccharide such as apiose, acetic acid, 3-deoxy-manno-2-octulosonic acid (KDO), and 3-deoxy-lyxo-2-heptulosaric acid (DHA) as side chains [30, 39]. GalA present in backbone of rhamnogalacturonan II (RG-II) may be methyl esterified at the C-6 position. The percentage of esterified GalA and acetylated groups in HG chain is termed as the DE and DAc, respectively. It is proposed that in the early developmental stages of plants, highly esterified pectin is formed that undergoes some deesterification in the cell wall or middle lamella. In general, tissue pectin ranges from 60 to 90% DE [45]. Both the DE and the DAc of pectin may vary depending on the method of extraction and plant origin [30, 46]. The functional properties of the pectin are determined by the amount and the distribution of esterified GalA residues in the linear backbone. Presence and distribution of esterified and nonmethylated GalA in pectin define the charge on pectin molecules. Based on their degree of esterification (DE), pectins are classified as high methoxy pectins (HMP) or low methoxy pectins (LMP). DE values of HM pectin range from 60 to 75%, whereas pectin with 20–40% of DE is referred as LM pectin. It was also observed that solubility, viscosity, and gelation properties of pectin are correlated and highly dependent on structural features [47, 48]. Pectin and monovalent salts of pectins are generally soluble in water but di- and trivalent ions are insoluble. The solubility of pectin in water increases with decrease in polymer size and increase in methoxy contents. Pectin powder gets hydrated very fast in water and forms clumps. The solubility of these clumps is very slow. As the pectin molecules come in contact with water, deesterification and depolymerization of pectins start spontaneously. The rate of decomposition of pectin depends on pH and temperature of the solution. As the pH of the solution decreased, with elevated temperature, ionization of carboxylate groups also reduced, which suppresses the hydration and repulsion between the polysaccharide molecules and results in the association of molecules in the form of gels. During thermal processing, solubilization of pectin is affected by β-elimination which depolymerized the pectin molecule and reduced its chain length. Small polymers have poor affinity with cell wall framework and solubilize easily. However, preheating, as well as reduced moisture contents in thermal processing, adversely affects the solubility of pectin in water [49, 50].
\nFood additives that are used in food processing to blend two immiscible liquids to produce a desirable product are known as food emulsifier or emulgent. These additives act as surface-active agents on the border of immiscible layers and reduce oil crystallization and prevent water separation. Emulsifiers are used in large number of food products such as ice creams, low-fat spreads, yoghurts, margarine, salad dressings, salty spreads, bakery products, and many other creamy sauces, to keep them in stable emulsion [27]. Emulsifiers increase the whip-ability of batters, enhance mouthfeel of the products, and improve texture and shape of the dough. Moreover, emulsions also help to encapsulate the bioactives [51]. Based on the disperse phase, there are two types of emulsion: oil in water (O/W) and water in oil (W/O). Milk, mayonnaise, dressings, and various beverages are some examples of O/W emulsion, whereas butter and margarine are the typical examples of W/O emulsion. Progress in hydrocolloid chemistry has resulted in the development of multitype emulsion such as O/W/O and O/W/O type emulsion (Figure 1). These emulsions are very important for fat reduction or encapsulation of bioactives and are used in preparation and stabilization of various low-fat creams, seasoning, and flavoring of sauces [52].
\nTypes of emulsions.
Commonly used emulsifiers in food processing are (i) small-molecular surfactant such as lectithins, derivatives of mono- and diglycerides prepared by mixing edible oils with glycerin or ethylene oxide, fatty acid derivatives such as glycol esters, sorbitan esters, polysorbates and (ii) macromolecular emulsifiers that include proteins and plant-based polymers such as soy polysaccharide, guar gum, modified starch, pectin, etc. [53]. As far as the properties of food emulsifier are concern, a good emulsifier should be low in molecular weight, capable to reduce the surface tension rapidly at interface, and should be soluble in continuous phase [54]. Research on food additives revealed the adverse effect of synthetic food additives on human being. Chassaing et al. found that polysorbate 80(P80) or carboxy methyl cellulose (CMC) had adverse effects on gut microbiota and their continuous use triggered the weight gain and metabolic syndrome after 12 weeks of administration in mouse [55]. A recent research carried out on mice shows that regular use of P80 and CMC triggers low-grade intestinal inflammation which may ultimately lead to the development of colon cancer [56]. Therefore, safety issues with the synthetic food additives and consumer’s demand for all natural food ingredients have necessitated the use of plant-based emulsifiers and stabilizers in food.
\nPectin is a natural hydrocolloid which exhibits wide spectrum of functional properties. Because of the gelling ability of pectin, it is used as viscosity enhancer. During emulsification process, pectin molecules adsorb at the fine oil droplets from at O/W interface and protect the droplet from coalescing with adjacent drops (short-term stability). The quality of emulsifier is defined by its ability to provide long-term stability against flocculation and coalescence [27]. Figure 2 depicts the stages in long-term emulsion formation using pectin as emulgent. When the viscosity of the continuous phase is increased, the movements of oil droplets become restricted which improves the shelf life of emulsion [57]. In the past decade, some pectin has also been reported to exhibit surface active behavior in oil-water interface and thereby stabilizing the fine oil droplets in emulsion [42, 58]. These functions of pectin are determined by its source, structural modification during processing, distribution of functional groups in pectin backbone, and also by various extrinsic factors such as pH, temperature, ionic strength, cosolute concentration, etc. The emulsification or surface active properties of pectin, i.e., formation of fine oil droplets, are mainly contributed due to the high hydrophobicity of protein residue present in pectin [46, 59] and also by hydrophobic nature of acetyl, methyl, and feruloyl esters [42, 60], whereas emulsion-stabilizing ability is attributed to the carbohydrate moieties and their conformational features [61].
\nEmulsion formation and stabilization using polymer as emulgent.
The mechanism of emulsion formation is shown in Figure 3. Different models explain the emulsion formation as covalently bound protein moieties in pectin are adsorbed onto the oil-water interface [46], form anchor points at the interface, and reduce the interfacial tension while the charged carbohydrate units extend into the aqueous phase [62] and stabilize by steric and viscosity effects in the aqueous phase(Figure 3a). Now, it is a well-established fact that pectin from different source shows variability in structure and protein contents. Leroux et al. identified many anchor points in sugar beet pectin (SBP) molecules [46], and proposed a loop-and-tail model (Figure 3b). According to the authors, only a limited amount of protein is adsorbed at the oil surface and acts as main moiety in the stabilization of the emulsion. This model was further confirmed by Siew and others [62]. The study was carried out to measure the thickness of the adsorbed SBP on oil-water interface layer, proposed a multilayer adsorption model (Figure 3c). Electrostatic interactions between the positively charged protein moiety and the negatively charged carbohydrate moiety were also reported.
\nDifferent models showing pectin adsorption at oil/water interface during emulsion formation.
Pectin O/W emulsion is generally stabilized through steric and electrostatic interaction. The carbohydrate moieties and neutral sugar side chains of RG I region of pectin confer the stability to the pectin emulsions through steric properties of the adsorbed polymers, when pectin is used as monoemulsifiers. In addition, pectin reversible association with galactan/arabinogalactan prior to emulsification also improves the emulsion stability [42, 63]. Electrostatic stabilization of emulsion is ascribed to sugar moieties and structural features of the HG units of pectin. If the pH of dispersion medium is above 3.5, nonmethylated carboxylic group of HG region gets ionized and confers charge on the pectin surface. Interaction of an ionic surfactant with oil droplets results in electrostatic stabilization [64]. Pectin viscosity also plays an important role in controlling the emulsion stability. HG region-rich pectin shows higher intrinsic viscosity ([η]); therefore, HG and RG ratio of pectin and molecular interactions that improve the intrinsic viscosity ([η]) of pectin solution also contributes in shelf life of emulsion [65, 66]. It has also observed that structural features of pectin such as pectin protein content, molecular mass, and presence of ferulic acid, and acetyl group in carbohydrate moieties of pectin also affect pectin’s emulsifying and emulsion stabilization properties [15]. Williams et al. showed that ferulic acid-rich pectin did not show significant difference in emulsifying ability of pectin when compared with pectin poor in ferulic acid [67]. Digestion of sugar beet pectin(SBP) with acidic proteases resulted in formation of larger size of oil droplet, lower creaming stability, and loss of emulsifying activity of SBP which confirms that protein contents of SBP play an important role in emulsifying ability of the polymer [42]. Nevertheless, in other research, it was also found that protein-rich fractions of SBP did not necessarily displayed better emulsifying ability; therefore, it was concluded that both protein with carbohydrate moiety together help in controlling emulsifying ability of SBP. Castellani et al. further suggest that both the carbohydrate and protein moieties function together as unit and affect the hydrophilic-hydrophobic equilibrium of the SBP molecule [68]. Therefore, when SBP is digested with proteases or other enzyme, a single moiety may function differently. Furthermore, it was also proposed that protein folding may also mask the hydrophobic effect of protein and thus affect the overall properties of the polymers [69].
\nMolecular weight of pectin has also been reported to affect the emulsifying capacity of pectin. Pectin with low molecular weight was more efficient in stabilizing small emulsion droplets than high-molecular weight pectin. However, very small size of citrus pectin had negative effect on emulsion-stabilizing ability of pectin. It could be due to the poor steric stabilization of depolymerized polymer [59].
\nEmulsion-based food products can be defined as a network of pectin-protein molecules entrapping the oil droplet in between. Nowadays, a large number of pectin- and polysaccharide-based emulsified low-fat dairy products, meat products, spreads or desserts, bakery products, sauces, etc., are available in market. Low-fat and low-cholesterol mayonnaise, low-fat cottage cheese, low-fat drinking yogurt, and flavored oil-containing acidified milk drinks are the few examples of pectin-based emulsified products. These products are prepared by replacing full-fat milk from skimmed milk, emulsified oil, and whey proteins [70, 71]. A low-fat cheese was prepared using skimmed milk and water-in-oil-in-water (W1/O/W2) emulsified canola oil. Different emulsifiers such as amidated low-methoxyl pectins (LMP), gum arabic (GA), carboxymethylcellulose (CMC), and combinations of GA-CMC or GA-LMP were used to stabilize the emulsion. Textural characteristics and sensory evaluation of low-fat cheese show that polymers used to stabilize the emulsion affected both microcrystalline structure and organoleptic properties. The cheese prepared using GA and LMP was almost similar in textural characteristics to the full-fat milk cheese [72]. In another study, Liu et al. compared the textural and structural features and sensory quality of full-fat and low-fat cheese analogs prepared with or without the incorporation of pectin [71]. Microstructure analysis using scanning electron microscopy revealed that full-fat cheese was denser and contained higher concentration of fat globules than low-fat cheese made with or without pectin. Comparison within the low-fat cheese analogs showed clear difference in their hardness, gumminess, chewiness, and adhesiveness. Addition of pectin had positive effect on textural and sensory attribute and scored better in mouthfeel also.
\nLow-fat (Lf) mayonnaise was prepared by partial replacement of egg yolk and incorporation of pectin as emulsifier [73, 74]. Pectin weak gel, pectin microencapsulation, and whey protein isolate were used in preparation of low-fat (Lf) mayonnaise. Physicochemical and sensory properties of Lf mayonnaise were compared with full-fat (Ff) mayonnaise; Lf mayonnaise had low energy and more water contents than Ff. Textural features and rheological properties of the Lf and Ff mayonnaise were similar and both displayed thixotropic shear thinning behavior and categorized as weak gels. Moreover, Lf mayonnaise prepared using pectin had better acceptability than whey protein incorporation [75]. Emulsified oil is used as an effective delivery system of active compound in functional foods, and also serves as milk fat replacer in fat-free dairy products. To improve the nutritional value of food, low-fat dairy products are produced, whereas saturated milk fat is generally replaced with emulsified-unsaturated vegetable oils [76].
\nIn recent year, pectin in combination with inulin has been reported to prepare low-fat meat batter. Méndez-Zamora et al. studied the effect of substitution of animal fat with different formulations of pectin and inulin on chemical composition, textural, and sensory properties of frankfurter sausages [77]. Finding of the research showed that fracturability, gumminess, and chewiness of the low-fat sauces were slightly lower than those of the control. However, addition of 15% inulin improves the sensory properties. In a similar work, replacement of pork back fat with 15% pectin and 15% inulin was found effective in maintaining the physicochemical properties and emulsion stability of the low-fat meat batter [78].
\nThe use of pectin in food products as a gelling agent is a long tradition. Later on, it was discovered that pectin forms different types of viscoelastic solution under suitable conditions. This property of pectin is commercially exploited in preparation of jams, jellies, and marmalades. Rheological behaviors of pectin depend on pectin source, its degree of methylation, distribution of nonmethylated GalA unit on pectin backbone, and degree of acetylation, and also on various extrinsic factors such as temperature, pH, concentration, and presence of divalent ions. At a constant pH, the setting time of pectin increases with decreasing DM and degree of blockiness (DB) in the absence of bivalent ions [79]. Therefore, on the basis of gelling process, pectin is classified as rapid, medium, and slow set pectin [80].
\nGelling process of pectin and its stabilization follows different mechanisms for different types of pectin. HMP form gels in a narrow pH range (2.0–3.5) in the presence of sucrose at a concentration higher than 55% w/v in medium. During the gelatin process of HMP, junction zones are formed due to the cross-linking of two or more pectin molecules. These junctions are stabilized by weak molecular interaction such as hydrogen and hydrophobic bonds between polar and nonpolar methyl-esterified groups and require high sugar concentration and low pH [81]. These gels are thermally reversible. LMP can form gel over a wide pH range (2.0–6.0) independent of sucrose, but requires divalent ion, such as calcium [82, 83]. LMP follow the eggbox model for its gelation, where positively charged calcium ions (Ca2+) are entrapped in between the negatively charged carboxylic group of pectin. The zigzag network of Ca2+ ion and GalA molecules looks like eggbox, and therefore, model is named as eggbox model [80]. These gels are stabilized by electrostatic bonds. In the presence of Ca2+, calcium bridges are formed with pectin molecules that make the solution more viscous. At the higher pH, the ionic strength of the solution is increased and thus more Ca2+ is needed for gelation. In case of highly acetylated pectin such as sugar beet, acetyl groups cause steric hindrances and interfere with the Ca2+ ion and GalA bond formation, thus preventing gel formation. Kuuva et al. [84] reported that enzymatic modification in pectin structure, i.e., removal of acetyl groups using α-arabinofuranosidase (α-Afases) and acetyl esterase enzymes, can improve the gelling property of acetylated pectin.
\nHMP are generally used in preparation of standard jams where sugar contents are above 55%, high-quality, tender confectionary jellies, fruit pastes, etc. LMP do not require sugar for its gelatin and therefore preferred choice for the production of low-calorie food products such as milk desserts, jams, jellies, and preserves, [28, 85]. LM pectins are more stable in low pH and high temperature conditions as compare to HM pectins and can be stored for more than a year.
\nFood packaging is one of the fastest growing segments of food industry. Traditionally, packaging system was limited to the containers and packaging material to transport the food items from manufacturer to the retail market and then to the consumers. Such type of packaging was unable to contribute in the extension of the shelf life and maintenance of the quality of the products. Due to the globalization of food market and increasing demand of shelf-stable processed food that retains the natural properties of food, the need of functional/active packaging material is increasing. To meet the industrial demand, a number of polymers are being synthesized and used in food packaging because of their flexibility, versatility, and cost effectiveness. Although, synthetic materials are able to fulfill all the industrial needs and keep food fresh and safe by protecting them from abiotic factors such as moisture, heat, oxygen, unpleasant odor, and biotic components such as micro- and macroorganisms. But, disposal of nonbiodegradable packaging material is a serious problem which poses a threat to the environment. Therefore, more research has been focused on the development of biodegradable packaging for food packaging applications using poly(lactic acid) (PLA), poly(hydroxyalkanoates) (PHAs), starch, etc. [86]. Among all the natural polymers, polysaccharides are gaining more attention as they are versatile in nature and easily available in relatively low cost.
\nA variety of natural polysaccharides, such as pectin, chitosan derivatives, alginate, cellulose, seaweed extract, and starch are usually used in the preparation of edible films and coatings [87]. Pectin is one of the most significant renewable natural polymers which are the main component of all the biomass and ubiquitous in nature. Being flexible in nature, pectin and its derivatives are used in many biodegradable packaging materials that serve as moisture, oil, and aroma barrier, reduce respiration rate and oxidation of food [88]. Pectin along with food grade emulsifiers is also used in the preparation of edible films. These films are used in fresh and minimally processed, fruits and vegetables, foods and food products as pectin is the main component of the omnivorous diet and can be metabolized. Edible coating protects the nutritional properties of the food and also saves highly perishable food from the enzymatic browning, off-flavor development, aroma loss, retards lipid migration, and reduces pathogen attack during storage.
\nAt low pH, LM pectins are cross-linked with calcium cations and form hard gels. These gels have highly stable structure and act as water barriers. Because of these properties, LM pectin films are used as edible coatings [88, 89]. Extension of shelf life of avocado fruits was also reported to over a month at 10°C by using edible pectin films. It was found that when avocados were coated with edible pectin films and stored at 10°C, rate of oxygen absorption and rate of respiration decreased which results in delaying of texture and color change of fruits [90]. Oms-Oliu et al. used calcium chloride and sunflower oil cross-linked with LM pectin films onto fresh-cut melon to see the effect on extension of shelf life of cut fruits [91]. It was observed that edible pectin films maintained the initial firmness, decrease the wounding stress of fresh-cut fruits, and prevent the dehydration during storage up to 15 days at 4°C but could not reduce the microbial growth onto the fresh melon. It has been observed that to reduce the respiration rate and to prevent the off-flavor development, different pectin and emulsifier formations are required for different fruits. Edible coating film formulation consisted on pectin, sorbitol, and bee wax was successfully used by Moalemiyan et al. to keep the fresh-cut mangoes in original state for over 2 weeks [92]. Whereas in a similar study, pectin coating containing sucrose and calcium lactate was able to prevent the fruits’ respiration rate and maintain sensory properties in fresh melon fruits for up to 14 days storage at 5°C. In a similar study [93], pectin edible coating solution containing pectin (3%), glycerol (2.5%), polyvinyl alcohol (1.25%), and citric acid (1%) was prepared and applied on sapota fruits by dipping method and uncoated sapota fruits were used as control. Both the treated and control fruits were stored at 30 ± 3°C. Physicochemical parameters namely, weight, color, firmness, acidity, TSS, pH, and ascorbic acid contents of both the coated and control fruits were measured at regular interval up to 11th day of the storage at 30 ± 3°C. Reduced rate of change in weight loss and other parameters were reported in pectin-coated sapota as compared to control fruits and it was observed that pectin film formulation was able to maintain good quality attributes and extend the shelf life of pectin-coated sapota fruits up to 11 days of storage at room temperature, whereas control fruits were edible up to 6 days. Furthermore, it was also observed that sapota fruits dipped in sodium alginate containing 2% pectin solution for 2 min were more effective in maintaining the organoleptic properties up to 30 days of refrigerated storage as compared to sapota fruits dipped for 4 min and untreated sapota fruits [94]. Bayarri et al. developed antimicrobial films using lysozyme and LM pectin complex. The main purpose of the study was to control the release of lysozyme in packaged food and to target lysozyme-sensitive bacteria such as Bacillus and Clostridium. It was observed that in the presence of fungal pectinase, due to the dissociation of pectin linkage, lysozyme activity of films increased remarkably. Many food-contaminating bacteria are pectinase producing and such type of films may be used to control food contaminants. These results have opened new avenues for custom-made biodegradable film [95].
\nIn last few years, some researchers have focused on pectin-based coating containing edible essential to improve the antimicrobial properties and to enhance the efficiency of the pectin films. Edible coating formulation containing sodium alginate and pectin (PE) enriched with eugenol (Eug) and citral (Cit) essential oil at different concentrations was used to increase the shelf life of strawberries. Physical and organoleptic parameters of coated fruits stored at 10°C for 14 days show that formulation containing PE 2% + Eug 0.1%; PE 2% + Cit 0.15% was more suitable than sodium alginate-based formulations [96]. Pectin coating containing lemon and orange peel essential oils was reported to increase the shelf life and quality attributes of the strawberry fruits up to 12 days when stored at 5°C. It was also observed that fruits coated with pectin + 1% orange essence showed less weight loss and soluble solids as compare to their control during the storage [97]. Sanchís et al. studied the combined effect of edible pectin coating with active modified atmospheric packaging on fresh-cut “Rojo Brillante” persimmon. Persimmon fruit slices were coated by dipping in the pectin-based emulsion or in water as control. Both the treated and control slices were packed under 5 kPa O2 (MAP) or under ambient atmosphere for up to 9 days at 5°C. Various parameters, such as package gas composition, color and firmness of slice, polyphenol oxidase activity, were measured during storage. It was observed that edible coating along with MAP significantly reduced the CO2 emission and O2 consumption in the packaged fruits. Furthermore, coating was also effective in controlling microbial growth and reducing enzymatic browning and maintains good sensory parameters up to 10 days on storage [98].
\nDrying is the traditional and oldest method of fruit and vegetable preservation. It decreases the enzymatic activity, reduces the moisture contents, and protects the food from microbial attack. However, drying results in loss of nutrients, vitamins, heat-labile enzymes, modifies the texture, color, and organoleptic quality of dried fruits and vegetables and therefore diminishes the market value also. Pretreatment of food products with pectin coatings containing other bioactive compound such as ascorbic acid, CaCl2, edible gum, etc., before drying or blanching has been proposed as an effective method to preserve the nutritional as well as organoleptic quality of dried food [99]. Recent researches have shown that application of pectin coating could protect the moisture and vitamin C loss in pretreated papaya slice and osmotic dehydrated pineapple. In one of the research [100], pineapple slice was pretreated with pectin coating formulation containing (50%)/calcium lactate (4%)/ascorbic acid (2%) solutions and then dried by hot-air-drying method. Physicochemical analysis of dried product showed less reduction in vitamin C contents as compared to untreated pineapple slice. In a similar work, pectin coating supplement with vitamin C (1%) was used for precoating of papaya slice. It was found that incorporation of vitamin C did not affect the drying process. However, significant increase in vitamin C content was observed in final product [101].
\nFrying is a method of cooking that causes changes in chemical and physical parameters of food and enhances the taste. However, high temperature vaporizes the water of food and affects the nutritional properties due to protein denaturation and starch gelatinization. The oil uptake during frying is affected by various parameters such as type of oil used, frying temperature and duration, product moisture content, shape, porosity, prefrying treatment, etc. [102]. Surface area and pretreatment of products are the major factors that determine the oil absorbed. Edible coating has also been used successfully, to reduce the oil uptake during frying in various deep-fried products. Reduction in oil uptake and improvement of texture and quality of potato slices was reported by Daraei Garmakhany et al. in 2008. Authors found that coating of potato slices with pectin, guar, and CMC solutions can reduce the oil uptake when compared with nontreated potato chips [103]. Similar results were also obtained by Khalil, where a combination of pectin or sodium alginate with calcium chlorides significantly reduces the oil uptake of French fries. Coating formulation of 0.5% calcium chloride and 5% pectin was most effective in reducing the oil uptake [104]. Kizito et al. used different edible coatings (pectin, carboxy methyl cellulose, agar, and chitosan) at a concentration of 1–2% for pretreatment of potato chips, followed by deep frying of chips. Fried chips were analyzed biochemically and organoleptically to investigate the quality attributes of the products. It was revealed that all the coating polymers were successful in reducing the oil uptake but pectin was most effective and reduced oil uptake up to 12.93%, followed by CMC (11.71%), chitosan (8.28%), and agar (5.25%) and significantly improved moisture retention of strips (p < 0.05) [105].
\nThe application of natural polymers in food industry is increasing day by day. Researchers are focusing more and more toward the pectin because of the ease-of-availability, structural flexibility, and versatile composition. Pectin can be sourced from a number of easily available horticulture crops (Table 1). Pectin is a hydrocolloid which is used as a food emulsifier, gelling agent, thickener, and stabilizer. It is the preferred choice of most of the food processors as fat or sugar replacer in low-calorie foods. In the recent years, increasing demand of ready-to-serve foods, fresh-cut fruits, and vegetable has opened a new market for edible films. Being biodegradable and recyclable, a lot of research is being done on pectin-based edible film formulations. These films reduce the exchange of moisture, gases, lipids, and volatiles between food and environment, and also serve as protective barrier for microorganisms.
\nEven though a lot of information is available regarding pectin structure and many pectin-based products are available in market, role of many carbohydrate moieties and their effect on various function of pectin are not yet well defined. Therefore, it is necessary to understand the structural-function relationship of pectin and its interactions for developing functional food products.
\nThe authors thank Director, CSIR-CFTRI for the encouragement.
\nThe authors declare no conflict of interest.
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