Аңдатпа
The surface-enhanced Raman spectroscopy (SERS)-based electronic tongue (E-tongue) represents a noninvasive and label-free taste-mimicking technology. This method employs an SERS sensing array as the core for signal amplification and detection, integrated with spectral signal acquisition and machine learning (ML) techniques, to achieve specific identification, qualitative analysis, and quantitative determination of multiple components in complex liquid matrices. However, extracting key features and information from complex, multi-component mixture spectra and selecting appropriate analytical methods based on data characteristics and specific tasks remain significant challenges in the field. This paper reviews the technical principles and research progress of SERS-based E-tongues, focusing on the core issue of "How laboratory researchers can scientifically and efficiently select ML methods after obtaining raw SERS data from sensor arrays, based on the data characteristics and analysis goals." The sections on preprocessing and feature engineering systematically summarize mainstream methods and evaluate their applicable scenarios. The qualitative analysis and quantitative modeling section also provides clear guidelines for selecting algorithms based on typical spectral characteristics and specific task requirements. The entire discussion addresses the practical challenges throughout the data processing pipeline, aiming to provide clear and practical methodological references for the majority of laboratory personnel engaged in SERS-based E-tongue research. Finally, it highlights the latest applications, summarizes current bottlenecks, and outlines future directions.