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基于PCA-Stacking模型的食源性致病菌拉曼光谱识别

Raman Spectroscopic Classification of Foodborne Pathogenic Bacteria Based on PCA-Stacking Model

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摘要

食源性致病菌的快速识别是一项重要的工作,与传统检测方法相比,拉曼光谱能在无损检测的同时加快鉴别速度。为了提高大肠杆菌O157∶H7以及布鲁氏菌S2株拉曼光谱识别的准确性和效率,提出一种基于主成分分析与Stacking算法的集成判别模型,使用网格搜索以及K折交叉验证来提高模型的稳健性。与逻辑回归、K近邻、支持向量机等单一模型进行对比,实验结果证明PCA-Stacking集成模型有最高的准确率,达99.73%,达到了预期效果。

Abstract

The rapid identification of foodborne pathogenic bacteria is an important task. Compared with the traditional detection methods, Raman spectroscopy is a non-destructive testing method and can simultaneously enhance the identification speed. In order to improve the accuracy and efficiency of Raman spectroscopic identification of Escherichia coil O157∶H7 and Brucella suis vaccine strain S2, a integral classification model is proposed based on the principal component analysis and the Stacking algorithm, whose robustness is improved by the grid search and K-fold cross validation. It is experimentally confirmed that compared with the logistic regression, K nearest neighbor, support vector machine and other single models, the integral model based on the Stacking algorithm possesses the highest accuracy rate of 99.73% the expected result is achieved.

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中图分类号:TP391

DOI:10.3788/lop56.043003

所属栏目:光谱学

基金项目:国家重点研发计划(2016YFC1201605)

收稿日期:2018-06-27

修改稿日期:2018-08-11

网络出版日期:2018-09-06

作者单位    点击查看

史如晋:上海应用技术大学计算机科学与信息工程学院, 上海 201418
夏钒曾:吉林大学软件学院, 吉林 长春 130122
曾万聃:上海应用技术大学计算机科学与信息工程学院, 上海 201418
曲晗:军事医学科学院军事兽医研究所吉林省人畜共患病预防与控制重点实验室, 吉林 长春 130122

联系人作者:曾万聃(zengwd@sit.edu.cn)

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引用该论文

Shi Rujin,Xia Fanzeng,Zeng Wandan,Qu Han. Raman Spectroscopic Classification of Foodborne Pathogenic Bacteria Based on PCA-Stacking Model[J]. Laser & Optoelectronics Progress, 2019, 56(4): 043003

史如晋,夏钒曾,曾万聃,曲晗. 基于PCA-Stacking模型的食源性致病菌拉曼光谱识别[J]. 激光与光电子学进展, 2019, 56(4): 043003

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