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<p>This paper explores the growing field of wine and grape authenticity based on chemical compounds. A total of 468 studies spanning 45 years were gathered from the Web of Science and subjected to bibliometric analysis and an advanced text mining approach. The findings emphasize the salience of the geographic origin, authenticity, terroir, and fingerprinting. Global studies indicate a concentration in Europe, followed by Asia, North America, South America, Australia, and Africa. Notable countries, including China, Spain, Italy, France, Portugal, Romania, Brazil, Argentina, Chile, and Australia, emerged as focal points for both wine production and scientific exploration. Key terms such as exploratory data analysis, PCA, cluster analysis, ICP-MS, pattern recognition, and LDA dominate the discourse, while recent trends spotlight terms like feature selection, support vector machines, neural networks, machine learning, and data mining. The applied methodology offers a broad perspective on wine and grape authenticity research, guiding future projects.</p>

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A methodological framework for wine and grape authenticity: combining bibliometrics and text mining

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Nattane Luíza da Costa,
  • Márcio Dias de Lima

摘要

This paper explores the growing field of wine and grape authenticity based on chemical compounds. A total of 468 studies spanning 45 years were gathered from the Web of Science and subjected to bibliometric analysis and an advanced text mining approach. The findings emphasize the salience of the geographic origin, authenticity, terroir, and fingerprinting. Global studies indicate a concentration in Europe, followed by Asia, North America, South America, Australia, and Africa. Notable countries, including China, Spain, Italy, France, Portugal, Romania, Brazil, Argentina, Chile, and Australia, emerged as focal points for both wine production and scientific exploration. Key terms such as exploratory data analysis, PCA, cluster analysis, ICP-MS, pattern recognition, and LDA dominate the discourse, while recent trends spotlight terms like feature selection, support vector machines, neural networks, machine learning, and data mining. The applied methodology offers a broad perspective on wine and grape authenticity research, guiding future projects.