机器学习-SERS化学舌食品检测的研究进展
Research Progress on Machine Learning-Assisted SERS Chemical Tongue for Food Detection
韦晓兰 1张村芳2
作者信息
- 1. 重庆工商大学食品科学与工程学院,中国重庆 400067
- 2. 重庆工商大学食品科学与工程学院,中国重庆 400067
- 折叠
摘要
\justifying 表面增强拉曼光谱（SERS）化学舌通过构建具有交叉响应特征的传感阵列,能够实现复杂样品中多组分信息的快速获取与整体识别,在食品安全检测领域展现出重要应用潜力。近年来,随着传感基底设计、阵列构建及光谱采集技术的发展,SERS化学舌在食品品质评价、掺假鉴别、农兽药残留检测以及食源性致病菌识别等方面取得了较大进展。与此同时,机器学习方法在高维光谱数据的特征提取、模式识别和定量分析中表现出明显优势,显著提升了SERS化学舌对复杂食品体系的识别效率与检测准确性。本文围绕机器学习-SERS化学舌食品检测的研究进展,综述其基本原理、常用机器学习方法及在食品检测中的典型应用,以期为该技术的进一步研究与应用提供参考。
Abstract
\justifying The surface-enhanced Raman spectroscopy (SERS) electronic tongue, based on cross-responsive sensing arrays, provides an effective strategy for rapid acquisition and holistic recognition of multicomponent information in complex samples, showing broad application prospects in food analysis. In recent years, with the development of substrate fabrication, array construction, and spectral acquisition techniques, SERS electronic tongues have made remarkable progress in food quality evaluation, adulteration identification, pesticide and veterinary drug residue detection, and foodborne pathogen analysis. Meanwhile, machine learning methods have demonstrated significant advantages in feature extraction, pattern recognition, and quantitative analysis of high-dimensional spectral data, greatly improving the efficiency and accuracy of food detection. This paper reviews the research progress of machine learning-assisted SERS electronic tongues in food detection, including their basic principles, commonly used algorithms, and representative applications, in order to provide a reference for further studies and practical applications in this field.关键词
食品科学/表面增强拉曼光谱/化学舌/机器学习/食品检测Key words
food science/surface-enhanced Raman spectroscopy/electronic tongue/machine learning/food detection引用本文复制引用
韦晓兰,张村芳.机器学习-SERS化学舌食品检测的研究进展[EB/OL].(2026-05-11)[2026-05-13].http://www.paper.edu.cn/releasepaper/content/202605-29.学科分类
计算技术、计算机技术/自动化技术、自动化技术设备
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