量子电子学报2026，Vol.43Issue(3)：361-374,14.DOI:10.3969/j.issn.1007-5461.2026.03.004
基于CNN-SVM混合模型的工程机械尾气醛类物质SERS光谱高精度分类方法
High-precision classification method for SERS spectra of aldehyde substances in construction machinery exhaust based on CNN-SVM hybrid model
摘要
Abstract
To address the low classification accuracy caused by spectral overlap and similar spectral characteristics in detecting aldehydes using surface-enhanced Raman scattering(SERS)spectroscopy technology,a hybrid model integrating convolutional neural network(CNN)and support vector machine(SVM),termed CNN-SVM,is proposed in this work.The model employs CNN to automatically extract both local and global features of SERS spectra and utilizes SVM to enhance classification performance.Experimental results on 1,500 SERS spectral data of various aldehyde substances collected on a self-built platform show that the CNN-SVM model achieves a classification accuracy of 97.33%on the test set,significantly outperforming the least squares support vector machine model(88.67%)or the CNN(92.00%)model separately.Meanwhile,the CNN-SVM model substantially reduces spectral processing time and memory consumption compared to a pure CNN architecture.Due to effectively overcoming the issue of spectral overlap,the proposed CNN-SVM hybrid model delivers high accuracy,high computational efficiency,and low resource requirements,providing a viable strategy for real-time,highly sensitive monitoring of aldehydes in complex environments and broadening the application of SERS spectroscopy technology in food safety and environmental monitoring.关键词
光谱分析/分类模型/表面增强拉曼散射/醛类污染物/深度学习/工程机械尾气Key words
spectral analysis/classification model/surface-enhanced Raman scattering/aldehyde pollutants/deep learning/construction machinery exhaust分类
数理科学引用本文复制引用
邹楠,聂新明,柏宇轩,薛一夫,钟晗月,渠陆陆,刘园园,戚俊杰,孟鑫..基于CNN-SVM混合模型的工程机械尾气醛类物质SERS光谱高精度分类方法[J].量子电子学报,2026,43(3):361-374,14.基金项目
国家重点研发计划(2022YFC2807701),国家自然科学基金青年基金(62205134),国家级大学生创新创业训练计划(202510320037) （2022YFC2807701）
