Article(id=1152687436200067633, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1152687434774000221, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250214003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739462400000, receivedDateStr=2025-02-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752751700686, onlineDateStr=2025-07-17, pubDate=1747238400000, pubDateStr=2025-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752751700686, onlineIssueDateStr=2025-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752751700685, creator=13701087609, updateTime=1752751700685, updator=13701087609, issue=Issue{id=1152687434774000221, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='9', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752751700342, creator=13701087609, updateTime=1756708585928, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1169283815848555430, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1152687434774000221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1169283815848555431, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1152687434774000221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=206, endPage=215, ext={EN=ArticleExt(id=1152687436581749299, articleId=1152687436200067633, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Bibliometric analysis of the ion mobility spectrometry applied in food scientific studies, columnId=1151895321388347923, journalTitle=Journal of Food Safety & Quality, columnName=Food Analysis and Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To gain a more intuitive understanding of the academic context and evolutionary trends of the ion mobility spectrometry applied in the field of food science. Methods This study took the literature from the Web of Science core database over the past 15 years as the research object, and used bibliometric analysis methods to reveal the current status, hotspots and development trends of research in this field. Results Our study had shown that the number of publications on the application of ion mobility spectrometry in food science research is constantly increasing, which involves the interdisciplinary fields such as chemistry, nutrition, biochemistry, agriculture, spectroscopy, environment and pharmacy beyond food science and technology. The number of the China’s publication was far ahead, and its institutions had great international discourse power, but the personal academic influence of Chinese authors still needs to be improved. At present, the main hotspots included the studies on different food qualities and flavors, metabolomics, volatile (organic) compounds and enrichment, optimization of instrument parameters and conditions, statistical methods, sample identification and classification, and multiple instrument combination analysis based on the application of ion mobility spectrometry. The co-occurrence, clustering and emergence analysis of the keywords further reveal the evolution process and mutation situation of research hotspots. Conclusion This study can provide theoretical basis for experts and scholars in the fields of food science and analytical chemistry to grasp the international application status and future development trends of ion mobility spectrometry.

, correspAuthors=Ke-Xian CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yu-Xin XIAO, Xiao-Wei ZHANG, Zong-Jian HE, Wan-Ying SHAO, Ke-Xian CHEN), CN=ArticleExt(id=1152687467938365942, articleId=1152687436200067633, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=离子迁移谱应用于食品科学研究的文献计量分析, columnId=1151895321958773274, journalTitle=食品安全质量检测学报, columnName=食品分析与检测, runingTitle=null, highlight=null, articleAbstract=

目的 更直观地掌握离子迁移谱应用于在食品科学领域的学术脉络与演化趋势。方法 本研究以Web of Science核心数据库中近15年的文献为研究对象, 采用文献计量分析方法揭示了该领域研究的现状、热点与发展趋势。 结果 研究表明, 离子迁移谱应用于食品科学研究的发文量呈现不断增长趋势, 除了涉及食品科学技术, 还涉及化学、营养、生物化学、农业、谱学、环境、药学等多学科交叉领域。我国的发文量遥遥领先, 机构拥有很大的国际话语权, 但作者的个人学术影响力还有待提高。目前主要热点为基于离子迁移谱开展的不同食品的品质与风味、代谢组学、挥发性(有机)化合物及富集、仪器参数与条件优化、统计方法、样品鉴别及分类和多种仪器组合分析等研究。关键词共现、聚类和突现分析进一步揭示了热点的演变过程和突变情况。结论 本研究可为食品科学和分析化学领域的专家学者把握离子迁移谱的国际应用现状和未来发展趋势提供理论依据。

, correspAuthors=陈可先, authorNote=null, correspAuthorsNote=
* 陈可先(1983—), 男, 讲师, 主要研究方向为食品风味分析。E-mail:
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肖雨馨(2004—), 女, 主要研究方向食品风味分析。E-mail:

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Foods, 2022, 11(22): 3708., articleTitle=E-nose, E-tongue combined with GC-IMS to analyze the influence of key additives during processing on the flavor of infant formula, refAbstract=null), Reference(id=1169272847575818531, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, doi=null, pmid=null, pmcid=null, year=2024, volume=451, issue=null, pageStart=139429, pageEnd=null, url=null, language=null, rfNumber=[33], rfOrder=36, authorNames=LI JY, NIE ZJ, HU JW, journalName=Food Chemistry, refType=null, unstructuredReference=LI JY, NIE ZJ, HU JW, et al. Geographical traceability of flavor compounds in Chinese mitten crab (Eriocheir sinensis): Implications for quality differentiation, authenticity assessment, and mechanism research[J]. 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label=图8, caption=关键词的共现谱

注: 分别为用CiteSpace (a)和VOSviewer (b)软件制作的图。

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Top 10 authors who published the most research papers

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序号 作者 发文量 H指数 机构名称 国家
1 Arce L 24 32 Universidad de Cordoba(科尔多瓦大学) 西班牙
2 Nerin C 18 59 University of Zaragoza(萨拉戈萨大学) 西班牙
3 Dreolin N 15 12 University of Zaragoza(萨拉戈萨大学) 西班牙
4 Canellas E 15 25 University of Zaragoza(萨拉戈萨大学) 西班牙
5 Weller P 14 16 University of Mannheim(曼海姆大学) 德国
6 Goshawk J 12 13 Aberystwyth University(亚伯大学) 英格兰
7 Righetti L 12 17 University of Parma(帕尔马大学) 意大利
8 Rohn S 12 54 Technical University of Berlin(柏林工业大学) 德国
9 Lin SY 11 39 Dalian Polytechnic University(大连工业大学) 中国
10 Dall’asta C 11 48 University of Parma(帕尔马大学) 意大利
), ArticleFig(id=1169272844056797429, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=CN, label=表1, caption=

发文量前10的作者

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 作者 发文量 H指数 机构名称 国家
1 Arce L 24 32 Universidad de Cordoba(科尔多瓦大学) 西班牙
2 Nerin C 18 59 University of Zaragoza(萨拉戈萨大学) 西班牙
3 Dreolin N 15 12 University of Zaragoza(萨拉戈萨大学) 西班牙
4 Canellas E 15 25 University of Zaragoza(萨拉戈萨大学) 西班牙
5 Weller P 14 16 University of Mannheim(曼海姆大学) 德国
6 Goshawk J 12 13 Aberystwyth University(亚伯大学) 英格兰
7 Righetti L 12 17 University of Parma(帕尔马大学) 意大利
8 Rohn S 12 54 Technical University of Berlin(柏林工业大学) 德国
9 Lin SY 11 39 Dalian Polytechnic University(大连工业大学) 中国
10 Dall’asta C 11 48 University of Parma(帕尔马大学) 意大利
), ArticleFig(id=1169272844123906294, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=EN, label=Table 2, caption=

Top 21 institutions who published the most research papers

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序号 机构名称 发文量 中介中心性 国家
1 Ministry of Agriculture & Rural Affairs(农业农村部) 66 0.2 中国
2 Chinese Academy of Agricultural Sciences(中国农业科学院) 51 0.14 中国
3 Dalian Polytechnic University(大连工业大学) 32 0.02 中国
4 Jiangnan University(江南大学) 26 0.17 中国
5 China Agricultural University(中国农业大学) 23 0.09 中国
6 Beijing Technology & Business University(北京工商大学) 22 0.07 中国
7 Ocean University of China(中国海洋大学) 22 0.15 中国
8 Universidad de Cordoba(科尔多瓦大学) 21 0.12 西班牙
9 Waters UK(沃特世公司) 21 0.05 英格兰
10 Nanjing Agricultural University(南京农业大学) 19 0.00 中国
11 Zhejiang University(浙江大学) 18 0.02 中国
12 Jiangsu University(江苏大学) 17 0.05 中国
13 Wageningen University & Research(瓦格宁根大学) 17 0.04 荷兰
14 University of Zaragoza(萨拉戈萨大学) 16 0.00 西班牙
15 Institute of Food Science & Technology(国际食品科学院) 16 0.01 中国
16 Chinese Academy of Sciences(中国科学院) 15 0.11 中国
17 University of Parma(帕尔马大学) 14 0.10 意大利
18 University of Hamburg(汉堡大学) 13 0.03 德国
19 Hainan University(海南大学) 11 0.02 中国
20 Anhui Agricultural University(安徽农业大学) 11 0.03 中国
21 Sichuan Tourism University(四川旅游学院) 11 0.02 中国
), ArticleFig(id=1169272844191015159, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=CN, label=表2, caption=

发文量前21位的机构

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 机构名称 发文量 中介中心性 国家
1 Ministry of Agriculture & Rural Affairs(农业农村部) 66 0.2 中国
2 Chinese Academy of Agricultural Sciences(中国农业科学院) 51 0.14 中国
3 Dalian Polytechnic University(大连工业大学) 32 0.02 中国
4 Jiangnan University(江南大学) 26 0.17 中国
5 China Agricultural University(中国农业大学) 23 0.09 中国
6 Beijing Technology & Business University(北京工商大学) 22 0.07 中国
7 Ocean University of China(中国海洋大学) 22 0.15 中国
8 Universidad de Cordoba(科尔多瓦大学) 21 0.12 西班牙
9 Waters UK(沃特世公司) 21 0.05 英格兰
10 Nanjing Agricultural University(南京农业大学) 19 0.00 中国
11 Zhejiang University(浙江大学) 18 0.02 中国
12 Jiangsu University(江苏大学) 17 0.05 中国
13 Wageningen University & Research(瓦格宁根大学) 17 0.04 荷兰
14 University of Zaragoza(萨拉戈萨大学) 16 0.00 西班牙
15 Institute of Food Science & Technology(国际食品科学院) 16 0.01 中国
16 Chinese Academy of Sciences(中国科学院) 15 0.11 中国
17 University of Parma(帕尔马大学) 14 0.10 意大利
18 University of Hamburg(汉堡大学) 13 0.03 德国
19 Hainan University(海南大学) 11 0.02 中国
20 Anhui Agricultural University(安徽农业大学) 11 0.03 中国
21 Sichuan Tourism University(四川旅游学院) 11 0.02 中国
), ArticleFig(id=1169272844270706936, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=EN, label=Table 3, caption=

Top 10 countries with the most research papers

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 国家 发文量 中介中心性 初现年份 序号 国家 发文量 中介中心性 初现年份
1 中国 588 0.50 2013 6 意大利 41 0.34 2011
2 西班牙 99 0.40 2010 7 伊朗 38 0.06 2011
3 德国 80 0.25 2011 8 荷兰 23 0.08 2013
4 美国 75 0.16 2010 9 法国 21 0.13 2014
5 英格兰 42 0.12 2010 10 奥地利 14 0.23 2010
), ArticleFig(id=1169272844337815801, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=CN, label=表3, caption=

发文量前10的国家

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 国家 发文量 中介中心性 初现年份 序号 国家 发文量 中介中心性 初现年份
1 中国 588 0.50 2013 6 意大利 41 0.34 2011
2 西班牙 99 0.40 2010 7 伊朗 38 0.06 2011
3 德国 80 0.25 2011 8 荷兰 23 0.08 2013
4 美国 75 0.16 2010 9 法国 21 0.13 2014
5 英格兰 42 0.12 2010 10 奥地利 14 0.23 2010
), ArticleFig(id=1169272844400730362, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=EN, label=Table 4, caption=

Top 20 frequency occurrence keywords

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 关键词 频次 中介
中心性
初现
年份
序号 关键词 频次 中介
中心性
初现
年份
1 ion mobility spectrometry 209 0.34 2010 11 flavor 48 0.01 2020
2 volatile compounds 162 0.05 2015 12 food 47 0.07 2013
3 identification 132 0.08 2014 13 separation 42 0.05 2011
4 mass spectrometry 127 0.12 2011 14 hs gc ims 42 0.04 2019
5 quality 105 0.06 2017 15 solid phase microextraction 39 0.05 2012
6 gas chromatography 89 0.13 2011 16 ms 38 0.07 2018
7 volatile organic compounds 63 0.02 2011 17 meat 38 0.03 2020
8 ion mobility 54 0.08 2014 18 components 37 0.04 2012
9 liquid chromatography 52 0.09 2013 19 aroma 37 0.02 2015
10 electronic nose 51 0.04 2012 20 extraction 35 0.05 2013
), ArticleFig(id=1169272844472033531, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=CN, label=表4, caption=

出现频次前20的关键词

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 关键词 频次 中介
中心性
初现
年份
序号 关键词 频次 中介
中心性
初现
年份
1 ion mobility spectrometry 209 0.34 2010 11 flavor 48 0.01 2020
2 volatile compounds 162 0.05 2015 12 food 47 0.07 2013
3 identification 132 0.08 2014 13 separation 42 0.05 2011
4 mass spectrometry 127 0.12 2011 14 hs gc ims 42 0.04 2019
5 quality 105 0.06 2017 15 solid phase microextraction 39 0.05 2012
6 gas chromatography 89 0.13 2011 16 ms 38 0.07 2018
7 volatile organic compounds 63 0.02 2011 17 meat 38 0.03 2020
8 ion mobility 54 0.08 2014 18 components 37 0.04 2012
9 liquid chromatography 52 0.09 2013 19 aroma 37 0.02 2015
10 electronic nose 51 0.04 2012 20 extraction 35 0.05 2013
), ArticleFig(id=1169272844534948092, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1152687436200067633, language=EN, label=Table 5, caption=

Top cited 10 scientific research papers

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 标题 作者 来源出版物 总被引
频次
1 Software tools and approaches for compound identification of LC-MS/MS data in metabolomics Blazenovic I, Kind T, Ji J, et al. Metabolites, 2018, 8(2): 31 489
2 Recent progress in food flavor analysis using gas chromatography-ion mobility spectrometry (GC-IMS) Wang SQ, Chen HT, Sun BG. Food Chemistry, 2020, 315: 126158 406
3 Development of a flavor fingerprint by HS-GC-IMS with PCA for volatile compounds of Tricholoma matsutake Singer Li MQ, Yang RW, Zhang, H, et al. Food Chemistry, 2019, 390: 32-39 276
4 Ambient ionization mass spectrometry:
A tutorial
Huang MZ, Cheng SC, Cho YT, et al. Analytica Chimica Acta, 2011, 702(1): 1-15 272
5 A review of recent, unconventional applications of ion mobility
spectrometry (IMS)
Armenta S, Alcala M, Blanco M. Analytica Chimica Acta, 2011, 703(2): 114-123 212
6 Identification of changes in volatile compounds in dry-cured fish during storage using HS-GC-IMS Zhang Q, Ding YC, Gu SQ, et al. Food Research International, 2020, 137: 109339 176
7 Characterization of the aroma profiles of oolong tea made from three tea cultivars
by both GC-MS and GC-IMS
Guo XY, Schwab W, Ho CT, et al. Food Chemistry, 2022, 376: 131933 174
8 Microextraction techniques in the analysis of food flavor compounds: A review Jelen HH, Majcher M, Dziadas M. Analytica Chimica Acta, 2012, 738: 13-26 162
9 Discrimination of epimeric glycans and glycopeptides using IM-MS and its potential for carbohydrate sequencing Both P, Green AP, Gray CJ, et al. Nature Chemistry, 2014, 6(1): 65-74 161
10 A literature survey of all volatiles from healthy human breath and bodily fluids: The human volatilome Drabinska N, Flynn C,
Ratcliffe N, et al.
Journal of Breath Research,
2021, 15(3): 034001
160
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引用量前10的文献

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 标题 作者 来源出版物 总被引
频次
1 Software tools and approaches for compound identification of LC-MS/MS data in metabolomics Blazenovic I, Kind T, Ji J, et al. Metabolites, 2018, 8(2): 31 489
2 Recent progress in food flavor analysis using gas chromatography-ion mobility spectrometry (GC-IMS) Wang SQ, Chen HT, Sun BG. Food Chemistry, 2020, 315: 126158 406
3 Development of a flavor fingerprint by HS-GC-IMS with PCA for volatile compounds of Tricholoma matsutake Singer Li MQ, Yang RW, Zhang, H, et al. Food Chemistry, 2019, 390: 32-39 276
4 Ambient ionization mass spectrometry:
A tutorial
Huang MZ, Cheng SC, Cho YT, et al. Analytica Chimica Acta, 2011, 702(1): 1-15 272
5 A review of recent, unconventional applications of ion mobility
spectrometry (IMS)
Armenta S, Alcala M, Blanco M. Analytica Chimica Acta, 2011, 703(2): 114-123 212
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离子迁移谱应用于食品科学研究的文献计量分析
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肖雨馨 1 , 章晓薇 1 , 何宗健 1 , 邵婉莹 2 , 陈可先 1, *
食品安全质量检测学报 | 食品分析与检测 2025,16(9): 206-215
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食品安全质量检测学报 | 食品分析与检测 2025, 16(9): 206-215
离子迁移谱应用于食品科学研究的文献计量分析
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肖雨馨1 , 章晓薇1, 何宗健1, 邵婉莹2, 陈可先1, *
作者信息
  • 1.浙江工商大学食品与生物工程学院, 杭州 310018
  • 2.建德市食品药品检验检测中心, 建德 311600
  • 肖雨馨(2004—), 女, 主要研究方向食品风味分析。E-mail:

通讯作者:

* 陈可先(1983—), 男, 讲师, 主要研究方向为食品风味分析。E-mail:
Bibliometric analysis of the ion mobility spectrometry applied in food scientific studies
Yu-Xin XIAO1 , Xiao-Wei ZHANG1, Zong-Jian HE1, Wan-Ying SHAO2, Ke-Xian CHEN1, *
Affiliations
  • 1. School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
  • 2. Jiande Municipal Food and Drug Inspection and Testing Center, Jiande 311600, China
出版时间: 2025-05-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250214003
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目的 更直观地掌握离子迁移谱应用于在食品科学领域的学术脉络与演化趋势。方法 本研究以Web of Science核心数据库中近15年的文献为研究对象, 采用文献计量分析方法揭示了该领域研究的现状、热点与发展趋势。 结果 研究表明, 离子迁移谱应用于食品科学研究的发文量呈现不断增长趋势, 除了涉及食品科学技术, 还涉及化学、营养、生物化学、农业、谱学、环境、药学等多学科交叉领域。我国的发文量遥遥领先, 机构拥有很大的国际话语权, 但作者的个人学术影响力还有待提高。目前主要热点为基于离子迁移谱开展的不同食品的品质与风味、代谢组学、挥发性(有机)化合物及富集、仪器参数与条件优化、统计方法、样品鉴别及分类和多种仪器组合分析等研究。关键词共现、聚类和突现分析进一步揭示了热点的演变过程和突变情况。结论 本研究可为食品科学和分析化学领域的专家学者把握离子迁移谱的国际应用现状和未来发展趋势提供理论依据。

离子迁移谱  /  食品科学  /  文献计量分析  /  研究热点

Objective To gain a more intuitive understanding of the academic context and evolutionary trends of the ion mobility spectrometry applied in the field of food science. Methods This study took the literature from the Web of Science core database over the past 15 years as the research object, and used bibliometric analysis methods to reveal the current status, hotspots and development trends of research in this field. Results Our study had shown that the number of publications on the application of ion mobility spectrometry in food science research is constantly increasing, which involves the interdisciplinary fields such as chemistry, nutrition, biochemistry, agriculture, spectroscopy, environment and pharmacy beyond food science and technology. The number of the China’s publication was far ahead, and its institutions had great international discourse power, but the personal academic influence of Chinese authors still needs to be improved. At present, the main hotspots included the studies on different food qualities and flavors, metabolomics, volatile (organic) compounds and enrichment, optimization of instrument parameters and conditions, statistical methods, sample identification and classification, and multiple instrument combination analysis based on the application of ion mobility spectrometry. The co-occurrence, clustering and emergence analysis of the keywords further reveal the evolution process and mutation situation of research hotspots. Conclusion This study can provide theoretical basis for experts and scholars in the fields of food science and analytical chemistry to grasp the international application status and future development trends of ion mobility spectrometry.

ion mobility spectrometry  /  food science  /  bibliometric analysis  /  research hotspot
肖雨馨, 章晓薇, 何宗健, 邵婉莹, 陈可先. 离子迁移谱应用于食品科学研究的文献计量分析. 食品安全质量检测学报, 2025 , 16 (9) : 206 -215 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250214003
Yu-Xin XIAO, Xiao-Wei ZHANG, Zong-Jian HE, Wan-Ying SHAO, Ke-Xian CHEN. Bibliometric analysis of the ion mobility spectrometry applied in food scientific studies[J]. Journal of Food Safety & Quality, 2025 , 16 (9) : 206 -215 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250214003
离子迁移谱(ion mobility spectrometry, IMS)是一种基于气态离子在常压和电场中迁移并用迁移率表征各种不同化学物质的快速分离技术[1-2], 其类型包括高场不对称波形离子迁移谱(high field asymmetric waveform ion mobility spectrometry, FAIMS)[3]、行波离子迁移谱(travelling wave ion mobility spectrometry, TWIMS)[4-5]、陷阱离子迁移谱(trapped ion mobility spectrometry, TIMS)[6]和差分离子淌度谱(differential ion mobility spectrometry, DMS)[7]等。IMS也可与其他仪器联用, 比如离子迁移谱-质谱法(ion mobility spectrometry-mass spectrometry, IMS-MS)[8-9]、高效液相色谱-离子迁移谱-质谱法(high performance liquid chromatography- ion mobility-mass spectrometry, HPLC-IMS-MS)[10-11]和气相色谱-离子迁移谱法(gas chromatography-ion mobility spectrometry, GC-IMS)[12-15]等不同类型。在食品科学研究中最为常见的是用于挥发性化合物分析的GC-IMS[12-15], 如海能公司的FlavourSpec®和BreathSpec®。在GC-IMS测定挥发性组分时可直接顶空进样以避免对样品做富集与浓缩等复杂预处理, 样品使用量少即可实现痕量组分分析, 在较短时间内得到包含保留时间、迁移时间和信号强度等三维数据, 不仅解决了IMS单独使用时对混合物与迁移率相近物质具有较低分辨率的难题, 也有助于对气相色谱分离不佳或者难以分离化合物进行二次分离, 让食品风味及其差异可视化[12-15]。因此, GC-IMS在灵敏度、选择性、便捷性等方面优于食品风味研究中常用的电子鼻(electronic nose, E-noses)、气相色谱-嗅味计(gas chromatography olfactometry, GC-O)或气相色谱-质谱仪(gas chromatography-mass spectrometry, GC-MS)等分析工具, 但也具有检测范围受基质干扰、仅适用于挥发性或半挥发性组分和可检测化合物数量有限等不足[12,14]
近年来, IMS, 特别是GC-IMS在食品科学研究中的应用越来越多, 主要应用场景包括以下几个方面: (1)食品真实性检测[16-18], 包括掺假, 掺杂, 造假, 以次充好, 以及过敏原或转基因等真实性标识; (2)食品品质[1,14,15,19], 如新鲜度、变质程度; (3)食品中异味、有害物质或非法添加物检测[20]; (4)不同食品风味组成[12,21-25]及其形成过程[22,26-27]; (5)在食品加工与贮藏过程中的食品风味监控[2,22,28-31]; (6)食品中添加物分析[32]; (7)原产地保护与食品溯源[33]。尽管文献中已经有较多关于离子迁移谱在食品中应用的综述, 但这些综述主要围绕IMS的原理、分析效果和大致的应用范围展开, 或者仅是围绕某一个角度概括。实际上, IMS应用于食品科学研究涉及的领域非常广泛, 现有的文献尚未从大数据分析的视角揭示现阶段与未来的热点领域和学术脉络, 难以从整体了解离子迁移谱应用的侧重点以及国内外差距。基于以上原因, 本研究对国际权威数据库中的相关研究开展了文献计量分析, 以期为IMS与食品科学研究的深度融合和为食品分析领域或行业专家提供决策参考。
本研究所用文献数据均源自Web of Science核心库, 检索时间范围为2010年1月1日—2024年12月31日, 检索时间为2025年2月2日21点, 文章检索的关键词为ion mobility spectrometry和food, 文献类型仅为article和review, 共获得1079篇文献。为了尽可能让文献集中在食品科学领域, 排除了Physics Applied、Physics Atomic Molecular Chemical、Neurosciences、Polymer Science、Acoustics、Medicine Research Experimental等若干与食品不相关主题的文献, 最后获得978篇文献用于后续研究, 其中这些文献绝大多数基于GC-IMS。本研究采用Web of Science自带的分析工具, 以及CiteSpace 6.3.R1版本软件(https://citespace.podia.com/)与VOSviewer 1.6.19.0版本软件(https://www.vosviewer.com/)对所得文献进行统计分析。除了文中有特别说明外, 其余参数均采用默认值。由于受文献检索条件限制和软件参数设置等的综合影响, 部分数据可能会存在一些出入。
根据Web of Science核心库中文献的统计分析结果(图1), IMS在食品科学领域的研究涉及食品科学技术外, 还涉及化学分析、化学应用、营养与营养学、生物化学研究方法、生物化学与分子生物学、化学多学科、农业多学科、光谱学、环境科学和药理药学等领域。可见, IMS的应用范围较广, 覆盖了食品科学、营养学、化学、农学、药学和环境科学等众多学科。由图2可知, 这些研究的发文量整体上是增加的, 特别是近4年增加迅速。因此, IMS在食品科学领域越来越受欢迎, 为解决许多食品领域难题提供了新的思路或视角。
离子迁移谱在食品科学领域发文量前10的作者见表1, 人均发文量在11篇以上, 最高的H指数(个人学术影响力)达到59。这些作者中, 4位来自西班牙, 2位来自德国, 2位来自意大利, 2位分别来自英格兰和中国。用CiteSpace分析了作者间的合作关系(K值设为5), 共产生123个节点(代表作者数), 88条连线(代表合作情况), 共线网络密度为0.0117。采用VOSviewer软件制作了作者间的合作关系图(图3), 合作阈值设为4次, 共有178位作者出现在图中, 不同颜色圈圈间的连线代表着不同作者间的合作关系网络。由图可知, 发文量较多的作者如Arce L、Nerin C、Dreolin N、Weller P和Righetti L等与多位作者均存在密切的合作关系。
表2为发文量排名前21位的机构, 其中我国的机构有15家, 西班牙2家, 英格兰、德国、荷兰和意大利各1家。这些机构发文量均在11篇以上, 最高达66篇。用CiteSpace分析了机构间的合作关系(K值设为20), 共产生299个节点(代表机构数), 481条连线(代表合作情况), 共线网络密度为0.0108。采用VOSviewer软件制作了机构间的合作关系网络(图4), 合作阈值设为5次, 共包含99家机构。由图4可知, 农业农村部、中国农业科学院、大连工业大学、沃特世公司、中国海洋大学、科尔多瓦大学、江南大学等均与多家机构存在密切的合作关系。中介中心性(表2)显示农业农村部、江南大学、中国农业科学院、中国海洋大学等机构之间存在较多的合作关系。
主要国家的发文量见图5表3, 排名前5的国家依次为中国、西班牙、德国、美国和意大利。这些国家的年发文量增加较多, 其中, 我国的增幅最大。用CiteSpace软件分析了国家间的合作关系(K值设为5), 共产生61个节点(代表国家数), 177条连线(代表合作情况), 共线网络密度为0.0967, 说明国家间的合作强度高于作者间或机构间的合作强度。采用VOSviewer软件制作了国家间的合作关系网络(图6), 合作阈值设为1次, 共包含67个国家。由图可知, 中国、德国、西班牙、美国、英格兰和意大利等国家与其他国家的合作较为密切, 这与表3中各国家的中介中心性数值相一致。
图7为用VOSviewer软件产生的学术期刊间互引关系图, 互引次数阈值为2, 一共有88个期刊出现在图中。此外, 也用CiteSpace分析了学术期刊间的互引关系(K值设为5), 共产生194个节点(代表期刊数), 1205条连线(代表合作情况), 共线网络密度为0.0644。由图7可知, 发文量较多的期刊主要为Food Chemistry、LWT-Food Science and Technology、Journal of Agricultural and Food Chemistry、Food Research International、Food Chemistry: X、Foods、Analytical Chemistry、Molecules和Analytical Chimina Acta。从这些期刊名称分析, IMS在食品科学研究领域的文章主要涉及食品化学、光化学、食品质量与安全、食品分析方法、作物保护与采摘、分离、分子科学、呼吸研究、香精香料、营养、环境、代谢和分析化学等领域。
IMS在食品科学研究领域的热点关键词及其相互关系见图8, 出现频次较高的关键词见表4。基于CiteSpace软件的关系图(图8a, K值设为13)由290个关键词、1709条连线组成, 共线网络密度为0.0408, 每个圈圈从内到外的颜色代表关键词的出现次数的逐年变化。基于VOSviewer软件的关系图(图8b, 阈值设为9次)由177个关键词节点组成, 根据颜色的不同可以将关键词间的关系分为4类。圈圈大小代表关键词的出现频次, 与表4中主要关键词的中介中心性的大小相一致。由图8可知, 热点关键词主要为IMS、挥发性(有机)化合物、鉴定、质谱、品质、气相色谱、离子迁移、液相色谱、E-noses、风味(化合物)、食品、分离、顶空-GC-IMS、固相(微)萃取、肉类、组分、芳香(化合物)、萃取、分类、脂质氧化、指纹图谱、抗氧化、轮廓、乳酸菌、多元统计分析、水样、残留物、样品准备、稳定性、酒类、鉴别、主成分分析、脂肪酸、电喷雾电离、发酵、橄榄油、代谢物、茶类、离子化、碰撞截面、脂质组学、味觉、鱼类、分辨率和爆炸物。由此可推测, 目前IMS在食品科学研究中的应用主要围绕IMS、食品品质与风味、代谢组学、挥发性化合物及富集、仪器参数与条件优化、统计方法、样品鉴别及分类和多种仪器组合分析等方面开展。
采用CiteSpace软件对上述热点关键词进行了聚类分析(K值设置为13), 其中平均轮廓值(S值)为0.7066>0.7, 模块化值(Q值)为0.3883>0.3, 因此上述聚类结果是可靠的。上述热点关键词可以聚类为#0 挥发性化合物、#1 IMS、#2 离子迁移、#3 碰撞截面、#4 南极虾和#5 交换等6类主题(图9), 主题之间没有明显的交叉。图9b(时间线图)给出了各聚类主题关键词变化的时间线, 其中圈越大, 表示该关键词热度越高, 连线越多越粗, 表示关键词联系越多越紧密。由图9可知, #1和#2是基础性主题, #5主题相关的疾病研究较少, 也较早, 而其他主题则是新兴主题, 存在时间(间断性)较长(如#3和#4)或处于持续热点状态(如#0)。由图9a(聚类谱)可知, IMS在食品科学研究领域的应用经历了从挥发性化合物、仪器检测、不同食品研究甚至到疾病、香烟研究的变化过程。
采用CiteSpace软件对上述热点关键词进行了突现分析(K值设为13), 产生突现强度排名前25的关键词, 其突现强度在3.07至10.08之间(图10)。其中, 突现强度在5以上的5个关键词, 分别为IMS、质谱、液相色谱、生物胺和橄榄油。早期突现关键词为IMS和超高效液相色谱。突现持续时间在7年以上的关键词为IMS、超高效液相色谱、质谱、分离、液相色谱和串联质谱。近3年突现的关键词为栽培、轮廓、鱼、菌和风味化合物。根据这些top关键词可知, IMS在食品科学研究领域的应用还突现在电泳、固相萃取、组分、顶空、碰撞截面和食品产品等关键词上。这也进一步说明该领域除了围绕食品、组分分离、检测仪器与方法外, 还涉及了种植、风味、微生物和有害物质分析等。
Web of Science核心库中引用量排名前十的论文见表5, 单篇被引次数在160以上, 最高达489。这些高影响力论文的主题涉及松茸、腌鱼干和乌龙茶香气等食品中的挥发性成分分析、人体挥发物、差向异构聚糖和糖肽、代谢组学化合物鉴定工具和方法, 也包含了多篇应用研究综述, 涵盖了GC-IMS在食品风味分析中的最新进展、IMS的近期非常规应用和食品风味化合物分析中的微萃取技术。期刊涉及食品化学、代谢物、分析化学、化学和呼吸研究等领域。这些研究工作可以粗略分为两类: 检测方法学研究与总结; 某类样品的分析检测研究。
本研究以IMS和食品为关键词对Web of Science核心库中近15年文献的现状、热点和演变趋势进行了文献计量分析。研究表明, IMS应用于食品科学的研究始终以挥发性化合物的分析为主线, 热点领域主要涉及鱼(如腌鱼干)、肉类、菌类(如松茸、乳酸菌)、茶类(如乌龙茶)、油类(如橄榄油)、水、酒类、人体挥发物(如呼出气体)、营养组分(如脂肪酸)、碳水化合物(如聚糖)、香精香料、芳香(化合物)类风味组分、有害物质(如生物胺)和代谢物等样品特征及其差异的可视化, 紧紧围绕挥发性组分(主要为风味化合物)及富集(如固相微萃取)、食品品质与风味、仪器参数与条件优化(如分辨率、碰撞截面、离子化)、统计方法、样品鉴别及分类、代谢组学和多种仪器组合分析(如与E-noses、液相色谱或质谱组合)等方面开展, 应用范围涵盖了食品科学、营养学、化学、农学、药学和环境科学等交叉学科领域。因此, IMS在多学科交叉领域均具有大的应用优势, 不仅揭示了各类样品的风味组分(挥发性化合物)的轮廓(指纹图谱), 也为各类样品的风味特征、等级划分、产地归属、品质保障和差异分析提供了技术支持。此外, 本研究还揭示了不同国家及其机构、作者的影响力及其合作网络, 不同期刊的发文量及其互引关系等数据, 这为加强IMS研究领域的专家学者的研究与合作提供了理论参考。
根据本研究的文献计量分析结果和文献初步筛查, 未来还可以从以下几个方面开展IMS应用于食品科学的研究: (1)多维色谱串联IMS仪的设计、开发与应用; (2)基于食品样品差异化性质的IMS方法库的构建; (3)代谢组学库的建立及其鉴定工具和方法的开发; (4)挥发性化合物结构与其迁移的构效关系研究; (5)不同产地食品差异化特征指纹库构建及其可追溯标准体系的建立; (6)在线实时样品品质与风味分析检测技术的开发; (7)挥发性化合物离子迁移库的扩展。
  • 国家级大学生创新创业训练计划项目(202410353011)
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2025年第16卷第9期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250214003
  • 接收时间:2025-02-14
  • 首发时间:2025-07-17
  • 出版时间:2025-05-15
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  • 收稿日期:2025-02-14
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国家级大学生创新创业训练计划项目(202410353011)
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    1.浙江工商大学食品与生物工程学院, 杭州 310018
    2.建德市食品药品检验检测中心, 建德 311600

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* 陈可先(1983—), 男, 讲师, 主要研究方向为食品风味分析。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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