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<p>The aging process has a significant impact on the quality and market value of white tea (WT), as described in a traditional proverb: “One year’s tea, three years’ medicinal value, and seven years becomes a treasure.” However, the lack of a rapid and reliable method for age identification poses challenges for market regulation. This study proposes a novel approach that combines surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) to accurately identify the age of white tea. SERS is used to obtain molecular fingerprint spectra from Fujian white tea samples covering seven different years (2011−2023). Seven machine learning models, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were systematically evaluated. The LR model, after being preprocessed through principal component analysis—logistic discriminant analysis, demonstrated relatively excellent performance. This SERS-ML method can perform rapid analysis and requires very little samples preparation. Our work establishes a robust, efficient, and field-deployable strategy for identifying the age of white tea, which is of great significance for combating fraud and protecting consumers.</p> Graphical Abstract <p></p>

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Rapid Authentication of White Tea Vintages Using SERS Fingerprints and Machine Learning

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Hui Lin,
  • Zhenglong Chen,
  • Chunfeng Ren,
  • Ruiyun You,
  • Yudong Lu

摘要

The aging process has a significant impact on the quality and market value of white tea (WT), as described in a traditional proverb: “One year’s tea, three years’ medicinal value, and seven years becomes a treasure.” However, the lack of a rapid and reliable method for age identification poses challenges for market regulation. This study proposes a novel approach that combines surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) to accurately identify the age of white tea. SERS is used to obtain molecular fingerprint spectra from Fujian white tea samples covering seven different years (2011−2023). Seven machine learning models, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were systematically evaluated. The LR model, after being preprocessed through principal component analysis—logistic discriminant analysis, demonstrated relatively excellent performance. This SERS-ML method can perform rapid analysis and requires very little samples preparation. Our work establishes a robust, efficient, and field-deployable strategy for identifying the age of white tea, which is of great significance for combating fraud and protecting consumers.

Graphical Abstract