食品科学2026，Vol.47Issue(2)：322-333,12.DOI:10.7506/spkx1002-6630-20250803-007
融合机器学习的表面增强拉曼光谱检测技术在食品安全检测中的应用进展
Machine Learning-Integrated Surface-Enhanced Raman Spectroscopy for Food Safety Detection:A Review
摘要
Abstract
Surface-enhanced Raman spectroscopy(SERS)offers significant advantages in the on-site rapid screening of food-safety risk factors due to its high sensitivity,specificity,and cost-effectiveness.However,the widespread application of this technique still faces challenges,including high-dimensional spectral data handling,interference from complex food matrices in trace-level detection,and difficulties in resolving overlapping spectral peaks.Recent advances in deep learning(DL)and machine learning(ML)have provided innovative solutions for SERS data analysis.The integration of ML methods(especially multivariate tools)with SERS enables efficient processing of complex spectral data,significantly improving detection performance,and has become a research hotspot.This review first briefly introduces the fundamentals of SERS and ML.Next,it highlights the application of SERS combined with ML(SERS-ML)in detecting food safety risk factors,such as pathogens(e.g.,bacteria,viruses),organic/inorganic toxins(e.g.,pesticides,antibiotics),and microplastics(MPs),with an emphasis on their identification and quantification.Furthermore,the key challenges and factors for the application of SERS-ML to complex food systems are discussed.Finally,the practical application potential of SERS-ML integration is outlined to inspire further research and technological innovation.关键词
表面增强拉曼光谱/机器学习/食品安全/风险因素Key words
surface-enhanced Raman spectroscopy/machine learning/food safety/risk factors分类
医药卫生引用本文复制引用
张萌,姚凯,郭巧珍,张晶,牛宇敏,邵兵,孙洁芳..融合机器学习的表面增强拉曼光谱检测技术在食品安全检测中的应用进展[J].食品科学,2026,47(2):322-333,12.基金项目
"十四五"国家重点研发计划重点专项(2022YFF1101000) （2022YFF1101000）
