Machine Learning-Driven Multidimensional Tea Profiling from a Single SERS Spectrum: Toward Practical Application

Jincheng Ni; Yanyan Lu; Xuewen Chen; Haoming Bao; Yu Qiao; Shuai Zhang; Daoshuan Ding; Jiamei Jin; Huaiyuan Zhao; Qian Zhao; J. Wang; Hongwen Zhang; Weiping Cai

doi:10.1039/d6an00093b

January 1, 2026The Analyst

Machine Learning-Driven Multidimensional Tea Profiling from a Single SERS Spectrum: Toward Practical Application

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Key Points

To develop a method for authenticating tea quality using machine learning and SERS technology.
Used a single SERS spectrum for multidimensional tea profiling.
Applied machine learning algorithms to analyze the spectral data.
Focused on ensuring rapid and systematic authentication.
Demonstrated effective tea authentication with high accuracy.
Showed potential for improving consumer experience and market integrity.

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Implication

Machine learning techniques authenticate tea quality in a rapid manner, enhancing brand integrity.

Abstract

Multidimensional tea profiling is crucial for both consumer experience, brand reputation, and market integrity. Here, we introduce a rapid and systematic strategy for multidimensional tea authentication based on a single...

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Cite This Study

Ni et al. (2026) studied this question.

synapsesocial.com/papers/69be35606e48c4981c6738a0 https://doi.org/10.1039/d6an00093b

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Machine Learning-Driven Multidimensional Tea Profiling from a Single SERS Spectrum: Toward Practical Application

Key Points

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Implication

Abstract

Discussion

Cite This Study

Machine Learning-Driven Multidimensional Tea Profiling from a Single SERS Spectrum: Toward Practical Application

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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