苏 丹,王志霞,周 佳,高 畅,李亚莉,周红杰.基于知识图谱分析近红外光谱技术在茶叶分析中的研究进展[J].食品安全质量检测学报,2022,13(4):1193-1200
基于知识图谱分析近红外光谱技术在茶叶分析中的研究进展
Research progress of near infrared spectroscopy in tea analysis based on knowledge mapping
投稿时间:2021-10-30  修订日期:2022-02-21
DOI:
中文关键词:  近红外光谱  茶叶分析  VOSviewer  CiteSpace  知识图谱
英文关键词:near infrared spectroscopy  tea analysis  VOSviewer  CiteSpace  knowledge mapping
基金项目:云岭产业技术领军人才项目(发改委[2014]1782)
作者单位
苏 丹 云南农业大学食品科学技术学院 
王志霞 云南农业大学茶学院 
周 佳 云南农业大学茶学院 
高 畅 云南农业大学茶学院 
李亚莉 云南农业大学茶学院 
周红杰 云南农业大学茶学院 
AuthorInstitution
SU Dan College of Food Science and Technology, Yunnan Agricultural University 
WANG Zhi-Xia College of Tea, Yunnan Agricultural University 
ZHOU Jia College of Tea, Yunnan Agricultural University 
GAO Chang College of Tea, Yunnan Agricultural University 
LI Ya-Li College of Tea, Yunnan Agricultural University 
ZHOU Hong-Jie College of Tea, Yunnan Agricultural University 
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中文摘要:
      目的 探究近红外光谱技术在茶叶分析领域的研究进展。方法 以“近红外光谱”和“茶”为主题检索中国知识资源总库(China National Knowledge Infrastructure, CNKI), 利用VOSviewer、CiteSpace和Wordle分析年度发文量并构建近红外光谱在茶叶分析中的知识图谱, 通过词云、Overlay图谱及Time-line chart可视化分析近红外光谱在茶叶分析领域的研究进展。结果 近红外光谱技术在茶叶分析领域的研究团队及研究机构相对单一。以茶叶主要成分含量预测(茶多酚、咖啡碱等)、茶叶判别是稳定的研究主题。结论 茶叶样本理化性质影响红外光谱有效采集, 通过不同的光谱预处理、特征选择与提取可提高茶叶分析的校正模型稳健性。本研究可为基于近红外光谱技术研究茶叶化学成分检测方法、生产过程质量监管、原产地溯源、茶叶掺假等领域的研究者提供理论参考和决策依据。
英文摘要:
      Objective To explore the research progress of near-infrared spectroscopy in the field of tea analysis. Methods The China National Knowledge Infrastructure (CNKI) was searched using "Near Infrared Spectroscopy" and "Tea" as the subject, VOSviewer, CiteSpace, and Wordle were used to analyze the amount of annual publications, then the knowledge map of near infrared spectroscopy in tea analysis was constructed, the research progress of near-infrared spectroscopy in the field of tea analysis through Word cloud, Overlay map and Time-line chart were visually analyzed. Results The research team and research institution of near-infrared spectroscopy in the field of tea analysis were relatively single. Research on the prediction of the content of the main components of tea (tea polyphenols, caffeine, etc.) and the identification of tea were stable research theme. Conclusion The physical and chemical properties of tea samples affect the effective collection of infrared spectroscopy, and the robustness of the correction model for tea analysis can be improved through different spectral preprocessing, feature selection and extraction. This study can provide theoretical references and decision-making basis for researchers in the fields of research on tea chemical composition detection methods, production process quality supervision, origin traceability, and tea adulteration based on near-infrared spectroscopy.
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