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Related Experiment Video

Updated: Jun 13, 2026

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

SERS-Based E-Tongue Data Analysis Methods: From Spectrum Preparation to Qualitative and Quantitative Modeling.

Pengju Yin¹, Xiaoyao Wu¹, Yumeng Xiao¹

¹School of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei 056038, China.

|March 3, 2026

Summary

This summary is machine-generated.

Surface-enhanced Raman spectroscopy (SERS)-based electronic tongues (E-tongues) offer noninvasive taste analysis. This review guides researchers in selecting machine learning methods for SERS data, addressing challenges in complex mixture analysis.

Keywords:

E-tongue SERS data preprocessing evaluation indicators liquid detection machine learning algorithms method selection guidance qualitative analysis quantitative analysis

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Area of Science:

Analytical Chemistry
Spectroscopy
Chemometrics

Background:

Surface-enhanced Raman spectroscopy (SERS) coupled with electronic tongues (E-tongues) provides label-free taste analysis.
SERS-E-tongues utilize sensing arrays, spectral acquisition, and machine learning (ML) for complex liquid analysis.
Extracting features and selecting appropriate ML methods for SERS data remain challenging.

Purpose of the Study:

To review SERS-based E-tongue principles and progress.
To provide guidance on scientifically selecting ML methods for SERS data based on characteristics and goals.
To address practical data processing challenges for SERS-E-tongue researchers.

Main Methods:

Systematic review of SERS-E-tongue technical principles and research progress.
Summary and evaluation of preprocessing and feature engineering methods for SERS data.
Guidelines for selecting qualitative and quantitative ML algorithms based on spectral data and task requirements.

Main Results:

Detailed review of SERS-E-tongue technology and its applications.
Systematic summary of data preprocessing, feature engineering, and ML algorithm selection strategies.
Practical methodological references for laboratory researchers in the field.

Conclusions:

Effective ML method selection is crucial for SERS-E-tongue data analysis.
The review provides a framework for choosing appropriate methods based on data and objectives.
Addresses current bottlenecks and outlines future research directions in SERS-E-tongue technology.